{"id": "2406.15465", "title": "RadEx: A Framework for Structured Information Extraction from Radiology Reports based on Large Language Models", "abstract": "Annually and globally, over three billion radiography examinations and computer tomography scans result in mostly unstructured radiology reports containing free text. Despite the potential benefits of structured reporting, its adoption is limited by factors such as established processes, resource constraints and potential loss of information. However, structured information would be necessary for various use cases, including automatic analysis, clinical trial matching, and prediction of health outcomes. This study introduces RadEx, an end-to-end framework comprising 15 software components and ten artifacts to develop systems that perform automated information extraction from radiology reports. It covers the complete process from annotating training data to extracting information by offering a consistent generic information model and setting boundaries for model development. Specifically, RadEx allows clinicians to define relevant information for clinical domains (e.g., mammography) and to create report templates. The framework supports both generative and encoder-only models and the decoupling of information extraction from template filling enables independent model improvements. Developing information extraction systems according to the RadEx framework facilitates implementation and maintenance as components are easily exchangeable, while standardized artifacts ensure interoperability between components.", "date": "2024-06-14", "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": "2606.18613", "title": "Are LLMs Ready to Assist Physicians? PhysAssistBench for Interactive Doctor-Patient-EHR Assistance", "abstract": "The most plausible near-term role of medical LLMs is to assist rather than replace physicians, yet current evaluations often test isolated capabilities: clinical knowledge, EHR system interaction, or patient communication. Physician assistance instead requires coordinating these capabilities within the same interaction, where physicians issue underspecified requests, patients describe symptoms ambiguously, and EHR systems demand precise tool use. We introduce PhysAssistBench, a benchmark for interactive doctor-patient-EHR assistance. Built from real MIMIC-IV cases, PhysAssistBench uses a scalable pipeline to construct agentic patients: interactive, record-grounded agents that turn static EHR records into multi-turn clinical scenarios while preserving clinical factuality. PhysAssistBench provides a curated bilingual evaluation set of 1,296 manually reviewed and physician-validated turns. Experiments with leading LLMs show that current models remain unreliable in this setting, which exposes a key bottleneck for clinical LLMs: reliable assistance requires coordination across knowledge, communication, and systems, not isolated gains in any of them.", "date": "2026-06-17", "categories": []}
{"id": "2606.19345", "title": "Ensembles of Large Language Models for Identifying EQ-5D Studies in PubMed Based on Their Abstracts", "abstract": "The rapid increase in scientific publications leads to the fact that manual study screening in systematic literature reviews (SLRs) is increasingly resource consuming, inefficient, and inconsistent. Classifying studies that clearly report health-related quality-of-life results, such as EQ-5D data, requires a high level of clinical interpretation and poses challenges for human reviewers. This study investigates the use of Google's Gemini and Gemma large language models (LLMs) in automating EQ-5D detection in the PubMed biomedical database based only on published abstracts. A multi-phase framework is proposed that integrates few-shot prompting, weight ensembling aggregation, and a soft stacking meta-classifier. Nine LLMs are evaluated on a dataset of PubMed studies manually labeled by two experts regarding EQ-5D reporting. The weighted ensemble of gemini-2.5-pro, gemma-3-12b, and gemma-3-27b obtained a 0.74 weighted F1-score and 0.74 accuracy, exceeding individually attained results. The ensembling of top-performing models improved the balance between precision and recall compared to individual models, while the soft stacking approach provided greater reliability and interpretability. Feature analysis shows that the probability results from the models are important in guiding the final predictions. The findings suggest that an ensemble-based LLM setup is a reliable and scalable approach for automating screening in biomedical research.", "date": "2026-04-24", "categories": []}
{"id": "2606.19602", "title": "Configurable Clinical Information Extraction with Agentic RAG: What Works, What Breaks, and Why", "abstract": "Patient contexts span hundreds of heterogeneous documents and thousands of structured data points, yet the document-level metadata that AI systems need for retrieval and triage is absent or incomplete. Standard retrieval-augmented generation fails on this data, mishandling temporal reasoning, cross-document dependencies, and missing metadata. We deploy ACIE (Agentic Clinical Information Extraction) at University Medicine Essen: an on-premise agentic RAG pipeline that reasons over complete patient contexts and grounds every answer in source passages for clinician verification. We quantify the metadata gap, trace the architectural decisions it shaped, and evaluate extraction alongside an independent retrospective lymphoma registry study, in which nuclear-medicine physicians verify every extracted value against its cited sources. Across 7,326 judgments, clinicians accepted 96.5\\% of extractions, with per-type acceptance ranging from 80\\% to 99\\%.", "date": "2026-06-17", "categories": []}
{"id": "2606.19637", "title": "Before the Labels: How Dataset Construction Shapes Suicidality Detection in Clinical Text", "abstract": "Clinical NLP increasingly relies on electronic health record (EHR) data to detect suicidal behaviors, treating clinical documentation as more reliable ground truth than social media. We argue that this framing obscures how EHR-based suicidality datasets encode a particular operationalization of suicidality, shaped by who authors the data, how episodes are bounded, and how ambiguity is resolved. We ground this argument in a case study of the ScAN dataset, built over MIMIC-III clinical notes. We show how governance constraints, ICD-based cohort selection, single-annotator labeling, and hospital-stay-level aggregation produce labels that reflect clinician-documented judgments, treat suicidality as a bounded episode, and assume that intent can be reliably inferred from documentation. A linguistic analysis demonstrates that identical labels subsume heterogeneous clinical framings differing in temporality, negation, and uncertainty. We argue that clinical NLP should examine the assumptions embedded in suicidality datasets before interpreting their labels as ground truth.", "date": "2026-06-17", "categories": []}
{"id": "2606.19852", "title": "Prompt, Plan, Extract: Zero-Shot Agentic LLMs Workflows for Lung Pathology Extraction from Clinical Narratives", "abstract": "Information extraction from pathology reports is essential for cancer staging, tumor registry population. Yet key data remains embedded in narrative reports, making manual extraction labor-intensive and error-prone. Traditional supervised Natural Language Processing pipelines address this through fully supervised Named Entity Recognition and Relation Extraction, but require expensive manual annotation and suffer cascading failures when upstream entities are missed. In this study, we developed a zero-shot, agentic workflow, and evaluated five open-source generative Large Language Models (LLMs) to populate 13 College of American Pathologists synoptic fields from lung resection pathology reports. We compared them against a state-of-the-art supervised GatorTron NER-RE baseline using a novel, registry-aligned evaluation framework. The baseline achieved Micro-F1of 0.960, while the best zero-shot model (GPT-OSS-20B) achieved Micro-F1 of 0.893 (recall: 0.949), accurately extracting complex relations like Pathologic Stage without task-specific training. These results suggest that open-source, zero-shot agentic LLMs are a low-cost solution for extracting lung pathology information.", "date": "2026-06-18", "categories": []}
{"id": "2606.18471", "title": "Possible or Definite? A Benchmark for Evaluating Diagnostic Uncertainty Preservation in Clinical Text", "abstract": "Large language models (LLMs) are increasingly used for clinical text tasks such as summarization and revision. While most studies evaluate the fluency and coherence of LLM-generated text, whether LLMs correctly preserve diagnostic uncertainty remains underexplored. In clinical practice, phrases such as ``possible pneumonia'' communicate the strength of available evidence and directly guide decisions about follow-up testing and treatment. Altering these uncertainty expressions can change the clinical meaning entirely. In this paper, we systematically evaluated this problem in two steps. First, we constructed a benchmark of 1,200 clinical documents with 9,184 uncertainty annotations across five levels. Second, we evaluated three LLMs on this benchmark. Our results show that (1) LLMs preserve the original uncertainty cues poorly, often less than half the time; (2) LLMs struggle with nuanced distinctions between adjacent levels. This work reveals a failure mode not captured by standard evaluation metrics and provides implications for the safe deployment of LLMs in clinical workflows.", "date": "2026-06-16", "categories": []}
{"id": "2606.18797", "title": "Beyond Scalar Scores: Exploring LLM-based Metrics for Clinical Significance Evaluation in Radiology Reports", "abstract": "Reliable evaluation of generated radiology reports requires strict clinical accuracy, as omitted critical findings or mischaracterized radiographic observations can directly affect patient care. Existing metrics obscure this requirement by reducing report quality to a medically ungrounded scalar. Although Large Language Models (LLMs) possess rich medical knowledge, they likewise struggle to draw a reliable boundary between clinically significant errors and harmless variation. We study this boundary using ReEvalMed benchmark as testbed and evaluate metric-level clinical significance from detecting true clinical errors (\"Discrimination\") and tolerating insignificant variations (\"Robustness\"). Across 8 LLM evaluators under one-pass and two-pass settings, we identify a widespread discrimination bias: models effectively detect errors but also over-penalize harmless rephrasings. To mitigate this, we synthesize 4k report pairs and train lightweight interpretable metrics on Qwen3-8B and MedGemma-4B. Our trained metric sharpens the clinical significance boundary, surpassing 32B-scale medical LLMs and remaining competitive with proprietary models. Crucially, the more costly two-pass setting fails to consistently improve overall performance and mainly trades discrimination for robustness. These findings suggest one-pass trained metrics as the practical choice for cost-sensitive deployment, with two-pass inference reserved for settings where D-R balance is critical. We will release the dataset and metric.", "date": "2026-06-17", "categories": []}
{"id": "2606.19183", "title": "Language Models as Interfaces, Not Oracles: A Hybrid LLM-ML System for Pediatric Appendicitis", "abstract": "Large language models (LLMs) can make clinical decision support more accessible by interpreting free-text documentation, but their direct use as diagnostic engines is limited by sensitivity to prompts, information order, and plausible but incorrect outputs. Structured machine-learning models offer more stable risk prediction, yet they require tabular inputs that are difficult to integrate with narrative clinical workflows. We present ClaMPAPP (Clinical Language-assisted Machine-learning Pipeline for Appendicitis), a hybrid system that uses an LLM as an interface rather than as the final decision-maker. ClaMPAPP extracts schema-constrained clinical features from note-like narratives, applies deterministic plausibility checks, and passes validated features to an XGBoost classifier trained on clinical, laboratory, and ultrasound variables. We evaluated ClaMPAPP on two independent pediatric appendicitis cohorts from German hospitals and compared it with end-to-end LLM baselines, including open-source and proprietary models. To preserve ground truth while testing free-text input, narratives were generated from structured electronic health records through template rendering and constrained LLM rewriting, with additional sentence-order permutation to assess positional robustness. ClaMPAPP achieved the strongest overall diagnostic performance in both internal and external validation while minimizing missed appendicitis cases, the key safety concern in acute triage. End-to-end LLMs showed unstable sensitivity-specificity trade-offs and greater degradation under narrative reordering. These results support an LLM-as-interface, ML-as-predictor design that separates natural-language usability from predictive inference and provides a more auditable pathway for clinical decision support.", "date": "2026-06-17", "categories": []}
{"id": "2408.15188", "title": "Infusing Acoustic Pause Context into Text-Based Dementia Assessment", "abstract": "Speech pauses, alongside content and structure, offer a valuable and non-invasive biomarker for detecting dementia. This work investigates the use of pause-enriched transcripts in transformer-based language models to differentiate the cognitive states of subjects with no cognitive impairment, mild cognitive impairment, and Alzheimer's dementia based on their speech from a clinical assessment. We address three binary classification tasks: Onset, monitoring, and dementia exclusion. The performance is evaluated through experiments on a German Verbal Fluency Test and a Picture Description Test, comparing the model's effectiveness across different speech production contexts. Starting from a textual baseline, we investigate the effect of incorporation of pause information and acoustic context. We show the test should be chosen depending on the task, and similarly, lexical pause information and acoustic cross-attention contribute differently.", "date": "2024-08-27", "categories": []}
{"id": "2603.17356", "title": "PACE-RAG: Patient-Aware Contextual and Evidence-Constrained RAG for Clinical Drug Recommendation", "abstract": "Drug recommendation requires a deep understanding of individual patient context, especially for complex conditions like Parkinson's disease. While LLMs possess broad medical knowledge, they fail to capture the subtle nuances of actual prescribing patterns. Existing RAG methods also struggle with these complexities because guideline-based retrieval remains too generic and similar-patient retrieval often replicates majority patterns without accounting for the unique clinical nuances of individual patients. To bridge this gap, we propose PACE-RAG (Patient-Aware Contextual and Evidence-Constrained RAG). Rather than directly copying frequent medications from retrieved patients, PACE-RAG personalizes recommendations by first extracting patient-specific clinical features, retrieving cases around these features, and then refining the final prescription using the patient's current symptoms, active medication history, and focus-specific prescribing tendencies. By analyzing treatment patterns tailored to specific clinical features, PACE-RAG generates patient-specific medication recommendations along with an explainable clinical summary. Evaluated on a Parkinson's cohort and the MIMIC-IV benchmark using Llama-3.1-8B and Qwen3-8B, PACE-RAG achieved state-of-the-art performance, reaching F1 scores of 80.84% and 47.22%, respectively. These results suggest that PACE-RAG is a robust and clinically grounded framework for personalized decision support. Our code is available at: https://github.com/ChaeYoungHuh/PACE-RAG.", "date": "2026-03-18", "categories": []}
{"id": "2606.15735", "title": "EHRNote-ChatQA: A Benchmark for Evidence-Grounded Multi-Turn Clinical Question Answering over Longitudinal Discharge Summaries", "abstract": "Discharge summaries are crucial clinical documents containing the context of a patient's overall hospital stay, and are routinely reviewed by medical experts for patient readmission, ongoing care, and diagnostic decision-making. When reviewing them, medical experts often must iteratively synthesize information across multiple summaries while verifying the evidence supporting each answer. Although large language models (LLMs) are increasingly explored for clinical question answering, existing benchmarks do not sufficiently reflect this setting: they often evaluate exam-style medical knowledge or focus on single-turn question answering with limited evidence-grounding evaluation. We introduce EHRNote-ChatQA, the first benchmark for evidence-grounded multi-turn clinical question answering over patients' multiple discharge summaries. Built from de-identified MIMIC-IV discharge summaries, EHRNote-ChatQA contains 967 patient-level multi-turn samples spanning one to five notes and 16,072 medical-expert-verified QA pairs (8,036 content questions, each paired with an evidence-grounding question) across eight clinical categories. The benchmark is constructed through an expert-informed pipeline combining discharge-summary structuring schema, expert-curated multi-turn QA templates, and LLM-based generation, followed by review and revision of every single QA sample by 11 medical experts. Benchmarking 22 open- and closed-source LLMs reveals several challenges, including that LLMs struggle more with evidence grounding than content answering, multi-turn errors compound across turns, and single-turn clinical QA performance does not reliably transfer to this setting. These findings establish EHRNote-ChatQA as a rigorous and practical benchmark for evaluating clinical QA systems. The dataset will be made publicly available through PhysioNet credentialed access.", "date": "2026-06-14", "categories": []}
{"id": "2606.17062", "title": "RadSEM: A Finding-by-Finding Metric for Clinical Consistency in Radiology Reports", "abstract": "Radiology report evaluation must distinguish clinical compatibility from surface similarity, because negation, laterality, or normal-abnormal polarity can reverse a finding. We propose RadSEM (Radiology Sentence-Level Evaluation Metric), a constrained LLM-assisted metric for reference-based evaluation of radiology Findings. RadSEM rewrites reference and generated reports into ordered atomic finding sentences, each expressing one site-finding proposition. It then performs contradiction-constrained many-to-many matching: incompatible pairs such as \"effusion\" and \"no effusion\" receive no credit, while compatible granularity differences can receive partial credit. A deterministic stage weights pairs by part-whole and abnormal-detail relationships, counts unmatched findings, and produces an abnormal-focused weighted F1 score. Thus, the LLM supports structured rewriting and local alignment rather than acting as an opaque judge. We evaluate RadSEM with SSREE, a controlled monotonicity stress test built from 2,448 de-identified reports expanded into five graded corruption levels. RadSEM achieves Kendall tau_b of 0.957, all-pairs concordance of 97.8%, adjacent concordance of 95.0%, and strict five-level ordering for 81.9% of reports, outperforming radiology-specific and general text metrics while avoiding the failure in which polarity-inverted reports regain lexical overlap. On the same SSREE set, RadSEM outperforms the Ref-anchored RadSEM-Alt policy, improving adjacent concordance from 90.7% to 95.0% and strict ordering from 67.2% to 81.9%. On a 599-triplet synonym/antonym subset, RadSEM prefers synonyms in 597 cases (99.67%). These results suggest that explicit finding units, contradiction-aware matching, and abnormal-focused deterministic scoring make report scoring more interpretable and sensitive to clinically meaningful errors. Code is available at https://github.com/jdh-algo/RadSEM.", "date": "2026-06-03", "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": "2606.18019", "title": "Reading between the Lines: Leveraging Large Language Models for Global Dementia and Depression Assessment from Clinical Interviews", "abstract": "Dementia and depression are the most prevalent neuropsychiatric disorders in geriatric populations, and their overlapping symptoms pose major challenges for differential diagnosis. In this study, we investigate open-weights Large Language Models (LLMs) for predicting dementia and depression severity from speech samples collected during standardized history taking interviews with 154 German-speaking subjects. We introduce an observer-based Global Depression Scale (GDS-D) aligned with the established Global Deterioration Scale (GDS), enabling parallel global staging of affective and cognitive symptoms. We compare three LLMs (Mistral 3.1, DeepHermes, Qwen3) in two settings: (1) zero-shot prediction and (2) LLM-based feature extraction for Support Vector Regression, using human and pause-enriched transcripts. Results show that LLMs effectively predict depression severity in zero-shot settings (best MAE of 0.60), while dementia assessment benefits substantially from structured feature extraction (best MAE of 0.78), reducing errors by up to 35% over zero-shot baselines. Pause-enriched transcripts achieve competitive performance with human transcriptions, demonstrating the viability of fully automatic screening pipelines for differential neuropsychiatric assessment.", "date": "2026-06-16", "categories": []}
{"id": "2403.13089", "title": "Automatic Summarization of Doctor-Patient Encounter Dialogues Using Large Language Model through Prompt Tuning", "abstract": "Automatic text summarization (ATS) is an emerging technology to assist clinicians in providing continuous and coordinated care. This study presents an approach to summarize doctor-patient dialogues using generative large language models (LLMs). We developed prompt-tuning algorithms to instruct generative LLMs to summarize clinical text. We examined the prompt-tuning strategies, the size of soft prompts, and the few-short learning ability of GatorTronGPT, a generative clinical LLM developed using 277 billion clinical and general English words with up to 20 billion parameters. We compared GatorTronGPT with a previous solution based on fine-tuning of a widely used T5 model, using a clinical benchmark dataset MTS-DIALOG. The experimental results show that the GatorTronGPT- 20B model achieved the best performance on all evaluation metrics. The proposed solution has a low computing cost as the LLM parameters are not updated during prompt-tuning. This study demonstrates the efficiency of generative clinical LLMs for clinical ATS through prompt tuning.", "date": "2024-03-19", "categories": []}
{"id": "2508.01401", "title": "MedSynth: Realistic, Synthetic Medical Dialogue-Note Pairs", "abstract": "Physicians spend significant time documenting clinical encounters, a burden that contributes to professional burnout. To address this, robust automation tools for medical documentation are crucial. We introduce MedSynth -- a novel dataset of synthetic medical dialogues and notes designed to advance the Dialogue-to-Note (Dial-2-Note) and Note-to-Dialogue (Note-2-Dial) tasks. Informed by an extensive analysis of disease distributions, this dataset includes over 10,000 dialogue-note pairs covering over 2000 ICD-10 codes. We demonstrate that our dataset markedly enhances the performance of models in generating medical notes from dialogues, and dialogues from medical notes. The dataset provides a valuable resource in a field where open-access, privacy-compliant, and diverse training data are scarce. Code is available at https://github.com/ahmadrezarm/MedSynth/tree/main and the dataset is available at https://huggingface.co/datasets/Ahmad0067/MedSynth.", "date": "2025-08-02", "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": "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": "2606.15037", "title": "ReportQA: QA-Based Radiology Report Evaluation", "abstract": "Radiology report evaluation is essential for advancing automated report generation. Natural language generation metrics have limited clinical relevance. Clinical efficacy (CE) metrics evaluate important medical findings, but focus mainly on presence and cover only a limited set of entities. Due to heavy reliance on manual annotations, it is difficult for CE metrics to extend clinical entities or attributes. In clinical practice, radiology reports serve as a medium for information transfer. Clinicians use them to perform downstream diagnostic tasks without directly inspecting images. Based on this insight, we propose ReportQA, a clinical-related and flexible radiology report evaluation framework, supporting detailed quantitative analysis of radiology report generation systems. We first collect datasets covering multiple imaging modalities and anatomical regions. We then construct knowledge trees of clinical entities and attributes with radiologist guidance, and use large language models (LLMs) to extract structured information from raw reports. Next, we generate QA pairs from predefined templates and apply quality control through self-filtering and report-based filtering. During evaluation, the report is treated as context, and an LLM acts as a judge model to answer the QA pairs. Based on the resulting QA accuracy, we introduce QAScore metric. Compared with existing metrics, QAScore shows better alignment with radiologist judgments. Experiments on multiple state-of-the-art vision-language models reveal that current report-based inference paradigms struggle to learn fine-grained clinical representations and exhibit strong negative prior biases. In contrast, question-driven inference provides a more effective alternative. For reproducibility and extensibility, we release the knowledge trees, structured reports, and QA pairs, along with the pipeline code for QA construction and evaluation.", "date": "2026-06-13", "categories": []}
{"id": "2606.15696", "title": "Do LLMs Reliably Identify Correct Information Units in Aphasic Discourse?", "abstract": "Correct Information Units (CIUs) are central to discourse assessment in aphasia because they quantify communicative informativeness rather than linguistic form alone. However, CIU scoring is time intensive and requires trained raters. This study examined whether instruction-tuned large language models (LLMs) can reliably perform token-level CIU classification from aphasic discourse transcripts. Sixteen picture-description transcripts elicited with the Cat Rescue stimulus were annotated for CIU status according to Nicholas and Brookshire (1993). The sample spanned four severity strata: control, mild, moderate, and severe aphasia. Four publicly available instruction-tuned LLMs were benchmarked under zero-shot and two few-shot prompting conditions across five stratified random seeds. Performance was evaluated against consensus human labels using accuracy, precision, recall, F1, and Cohen's kappa. Zero-shot prompting was insufficient across models. In contrast, few-shot prompting yielded substantial gains and produced competitive performance for three viable models. Mean few-shot F1 scores ranged from 0.776 to 0.817 across Llama-3.1-8B, Qwen2.5-7B, and Mistral-7B, with no significant differences between fixed global and per-chunk local example selection. Phi-3-mini was unstable and did not yield reliable performance. Viable models showed high recall but lower precision, suggesting systematic over-classification of tokens as CIUs. Performance also varied by discourse severity, with the weakest results in more severe aphasia. Few-shot LLM prompting can support automated CIU identification without gradient-based task training, but agreement with human annotation remains insufficient for fully autonomous use. These findings support LLM-based CIU scoring as a promising human-in-the-loop component of discourse assessment systems.", "date": "2026-04-10", "categories": []}
{"id": "2606.16026", "title": "In-Domain Supervised Pathology Report Classification: A Reproducible Pipeline from Data Curation to Production-Matched Evaluation", "abstract": "We introduce an in-domain supervised pipeline designed to counter the out-of-distribution performance drop that hampers supervised biomedical NLP models, a problem observed when models trained on pathology reports are moved across cancer registries. Our contribution is a reproducible recipe for training a supervised classifier from routinely collected cancer registry data. It describes how to build the in-domain training set and a production-matched holdout, and to choose operating points that keep the false-negative rate (FNR) very low while keeping reviewer workload manageable. The pipeline standardizes data curation with facility-stratified sampling and separate handling of reports linked to registry cases, and includes a blinded manual audit to estimate positive-case prevalence and label noise. On a 418k-report holdout set, the Kentucky model achieved FNR 0.003 and false-positive rate (FPR) 0.097, improving over the Seattle-trained MOSSAIC OncoID baseline (FNR 0.010, FPR 0.183) and raising F1 from 0.860 to 0.922. In a blinded manual review of 600 reports, estimated positive prevalence declined from 0.500 to 0.398, indicating substantial label noise with errors concentrated in rare primary sites.", "date": "2026-06-14", "categories": []}
{"id": "2606.16074", "title": "PVminerLLM2: Improving Structured Extraction of Patient Voice via Preference Optimization", "abstract": "Motivation: Patient-generated text contains critical information on patients' lived experiences, social context, and care engagement, but remains largely unstructured, limiting its use in patient-centered outcomes research. Prior work introduced the PV-Miner benchmark and PVMinerLLM models for structured extraction. However, supervised fine-tuning (SFT) alone struggles with rare, fine-grained, and unevenly distributed errors, particularly in token-critical structured outputs.\n  Results: We present PVminerLLM2, an improved set of LLMs for structured patient voice extraction that applies preference optimization to address token-critical errors beyond the reach of supervised fine-tuning. Our method introduces (i) a preference objective with token-level gated stabilization term that prevents degradation of absolute token likelihood under preference optimization, and (ii) confusion-aware preference pair construction to better capture low-separation distinctions. We further incorporate token-importance weighting and inverse-frequency reweighing to address token imbalance and class skew. Across multiple model sizes, PVMinerLLM2 consistently outperforms strong baselines, achieving gains of up to 4.43% (Code), 3.50% (Sub-code), and 1.55% (Span), and outperforms baseline LLM trained with existing preference optimization methods.\n  Availability and Implementation: The supplementary material, code, evaluation scripts, and trained models for PVminerLLM2 are publicly available at: https://github.com/Data-Mining-Lab-Yale/PVminerLLM2", "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": "2606.13940", "title": "Can Post-Training Turn LLMs into Good Medical Coders? An Empirical Study of Generative ICD Coding", "abstract": "Automated International Classification of Diseases (ICD) coding is a core medical-coding task for billing, epidemiology, and clinical decision support. Generative large language models (LLMs) are often reported as weak medical coders, but this finding mainly comes from inference-time settings such as prompting, retrieval, reranking, or tool use, leaving the role of task-specific post-training underexplored. We present a controlled empirical study of post-training for generative ICD coding, comparing discriminative baselines with LLM coders across prompting, supervised fine-tuning, and reinforcement learning under a common protocol and metric set. To our knowledge, this is the first study to evaluate RL-based post-training for generative LLM coders in ICD coding. We further introduce PHI, a diagnostic curriculum that extends GRPO to refine missed-code cases. Our results show that prompting-only evaluation substantially underestimates the potential of LLMs for ICD coding. SFT provides the main capability jump, GRPO further improves code-set prediction beyond SFT, and PHI provides targeted gains on macro-level performance. These findings suggest that the main bottleneck is not the generative formulation alone, but how the model is adapted and optimized for full-taxonomy recall. We release our code, data splits, and checkpoints at https://github.com/AlexandreWANG915/LLM4ICD.", "date": "2026-06-11", "categories": []}
{"id": "2606.14460", "title": "A Computational Audit of Demographic Association Encoding in ClinicalBERT Language Predictions", "abstract": "Transformer-based clinical language models are increasingly integrated into high-stakes clinical decision support pipelines, yet the computational mechanisms through which demographic associations encoded in medical documentation propagate into model probability distributions remain empirically underspecified. We present a systematic computational audit of representational bias in ClinicalBERT (Alsentzer et al., 2019), a BERT-based model pretrained on MIMIC-III discharge summaries, employing two complementary probing methodologies: Log Probability Bias Analysis (LPBA), which quantifies demographic descriptor-induced shifts in masked token probability distributions across behavioral and evaluative semantic categories, and Masked Language Model-based analysis (MLM), which probes internal representational structure for demographic agency attribution encoding across 98 real clinical sentence templates and eight intersectional race-gender combinations. Corpus frequency analysis operationalizes the distinction between statistical disparity and bias amplification by benchmarking model outputs against empirical term frequencies in the MIMIC-III training corpus. Of 32 statistically significant findings, 65.6% contradict observed corpus distributions, rising to 80% for Black patients and 87.5% for agency attribution under MLM probing, providing direct empirical evidence that representational bias in ClinicalBERT operates predominantly through model-internal amplification rather than training data inheritance. Keywords: natural language processing, clinical documentation, algorithmic auditing, representational bias, health equity 1", "date": "2026-06-12", "categories": []}
{"id": "2602.09379", "title": "LingxiDiagBench: A Multi-Agent Framework for Benchmarking LLMs in Chinese Psychiatric Consultation and Diagnosis", "abstract": "Mental disorders are highly prevalent worldwide, but the shortage of psychiatrists and the inherent subjectivity of interview-based diagnosis create substantial barriers to timely and consistent mental-health assessment. Progress in AI-assisted psychiatric diagnosis is constrained by the absence of benchmarks that simultaneously provide realistic patient simulation, clinician-verified diagnostic labels, and support for dynamic multi-turn consultation. We present LingxiDiagBench, a large-scale multi-agent benchmark that evaluates LLMs on both static diagnostic inference and dynamic multi-turn psychiatric consultation in Chinese. At its core is LingxiDiag-16K, a dataset of 16,000 EMR-aligned synthetic consultation dialogues designed to reproduce real clinical demographic and diagnostic distributions across 12 ICD-10 psychiatric categories. Through extensive experiments across state-of-the-art LLMs, we establish key findings: (1) although LLMs achieve high accuracy on binary depression--anxiety classification (up to 92.3%), performance deteriorates substantially for depression--anxiety comorbidity recognition (43.0%) and 12-way differential diagnosis (28.5%); (2) dynamic consultation often underperforms static evaluation, indicating that ineffective information-gathering strategies significantly impair downstream diagnostic reasoning; (3) consultation quality assessed by LLM-as-a-Judge shows only moderate correlation with diagnostic accuracy, suggesting that well-structured questioning alone does not ensure correct diagnostic decisions. We release LingxiDiag-16K and the full evaluation framework to support reproducible research at https://github.com/Lingxi-mental-health/LingxiDiagBench.", "date": "2026-02-10", "categories": []}
{"id": "2604.10389", "title": "BLUEmed: Retrieval-Augmented Multi-Agent Debate for Clinical Error Detection", "abstract": "Terminology substitution errors in clinical notes, where one medical term is replaced by a linguistically valid but clinically different term, pose a persistent challenge for automated error detection in healthcare. We introduce BLUEmed, a multi-agent debate framework augmented with hybrid Retrieval-Augmented Generation (RAG) that combines evidence-grounded reasoning with multi-perspective verification for clinical error detection. BLUEmed decomposes each clinical note into focused sub-queries, retrieves source-partitioned evidence through dense, sparse, and online retrieval, and assigns two domain expert agents distinct knowledge bases to produce independent analyses; when the experts disagree, a structured counter-argumentation round and cross-source adjudication resolve the conflict, followed by a cascading safety layer that filters common false-positive patterns. We evaluate BLUEmed on a clinical terminology substitution detection benchmark under both zero-shot and few-shot prompting with multiple backbone models spanning proprietary and open-source families. Experimental results show that BLUEmed achieves the best accuracy (69.13%), ROC-AUC (74.45%), and PR-AUC (72.44%) under few-shot prompting, outperforming both single-agent RAG and debate-only baselines. Further analyses across six backbone models and two prompting strategies confirm that retrieval augmentation and structured debate are complementary, and that the framework benefits most from models with sufficient instruction-following and clinical language understanding.", "date": "2026-04-12", "categories": []}
{"id": "2606.12569", "title": "EDEN: A Large-Scale Corpus of Clinical Notes for Italian", "abstract": "We present EDEN (Emergency Department Electronic Notes), a new and unique large-scale corpus of clinical notes produced in Emergency Departments of Italian hospitals. The corpus, in its current version, is composed of approximately 4 million clinical notes fully anonymized, covering diverse phases of patient care during the stay in the emergency department. In addition, a subset of about six thousand notes has been manually annotated by clinical experts through a structured Case Report Form (CRF) containing 132 items relevant for two patient situations in emergency departments, dyspnea and loss of consciousness. Items may assume numerical values (e.g., for blood saturation), categorical (e.g., for level of consciousness ), binary (e.g., for presence of traumas), and mixed value types. The annotation process involved multiple clinicians and underwent iterative revision to resolve ambiguities in item formulation, resulting in a richly structured (although high imbalanced) resource. The dataset aims to fill a relevant gap of data able to support both the development and the use of Large Language Models in concrete medical applications. We describe the data collection protocol, the on-site anonymisation pipeline, corpus statistics, and the annotation scheme. Finally, we propose CRF-filling as a novel structured information extraction benchmark, and provide zero-shot baseline resulting from Gemma-27B and MedGemma-27B. To the best of our knowledge, the EDEN dataset is the largest freely available corpus of clinical notes existing for the Italian language.", "date": "2026-06-10", "categories": []}
{"id": "2606.13082", "title": "sebis at CRF Filling 2026: A Two-Stage Local LLM Pipeline for Medical CRF Filling", "abstract": "The extraction of structured clinical information from unstructured EHR notes is a persistent bottleneck in healthcare informatics. While large language models (LLMs) offer high performance, their deployment in clinical settings is hindered by privacy risks, inference costs, and the tendency to hallucinate beyond textual evidence. We address these challenges for the CL4Health 2026 Case Report Form (CRF) filling task by proposing a fully local, domain-adapted pipeline using the MedGemma-27B model. Our two-stage architecture, which separates binary presence classification from value extraction, enforces strict adherence to textual evidence and ensures deterministic outputs for negated, uncertain, or unknown states. By leveraging item-specific, few-shot in-context learning without external API calls or fine-tuning, our approach achieves a macro-F1 score of 0.55 on the official English test track. This result secures second place among all locally-hosted, open-source submissions. Our work demonstrates that privacy-preserving, on-premise LLM pipelines can achieve near-competitive performance with proprietary frontier models, providing a practical, data-sovereign framework for clinical NLP.", "date": "2026-06-11", "categories": []}
{"id": "2605.04221", "title": "Self-Prompting Small Language Models for Privacy-Sensitive Clinical Information Extraction", "abstract": "Clinical named entity recognition from dental progress notes is challenging because documentation is highly unstructured, domain-specific, and often privacy-sensitive. We developed a locally deployable framework that enables small language models to self-generate, verify, refine, and evaluate entity-specific prompts for extracting multiple clinical entities from dental notes. Using 1,200 annotated notes, we evaluated candidate open-weight models with multi-prompt ensemble inference and further adapted selected models using QLoRA-based supervised fine-tuning and direct preference optimization. Model performance varied substantially, highlighting the need for task-specific evaluation rather than reliance on generic benchmarks. Qwen2.5-14B-Instruct achieved the strongest baseline performance. After DPO, Qwen2.5-14B-Instruct and Llama-3.1-8B-Instruct achieved micro/macro F1 scores of 0.864/0.837 and 0.806/0.797, respectively. These findings suggest that automated prompt optimization combined with lightweight preference-based post-training can support scalable clinical information extraction using locally deployed small language models.", "date": "2026-05-05", "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": "2606.11675", "title": "Lung-R1: A Knowledge Graph-Guided LLM for Pulmonary Diagnostic Reasoning", "abstract": "Diagnosing pulmonary diseases requires integrating heterogeneous evidence amid phenotypic variability and cross-disease overlap. Although large language models (LLMs) have shown progress on pulmonary knowledge question answering (QA) and information-processing tasks, reliable pulmonary diagnosis requires patient-specific, relation-aware reasoning over electronic medical record (EMR) evidence rather than isolated knowledge recall. We define this gap between pulmonary knowledge and case-level diagnostic reasoning as the Pulmonary Knowledge-to-Diagnosis Gap. To address it, we introduce LungKG, the first structured pulmonary knowledge graph for diagnostic knowledge organization and record-grounded reasoning. LungKG contains 59,038 nodes and 164,308 edges across 15 entity types and 112 relation types, serving as both a reusable pulmonary knowledge resource and the foundation for LungKG-guided model adaptation. Built on LungKG, we propose Lung-R1, a LungKG-guided pulmonary LLM trained through KG-constrained reasoning-chain construction and KG-guided reinforcement learning. In a 20-system evaluation, Lung-R1-14B achieves state-of-the-art performance across Choice, Pulmonary-QA, and EMR Diagnosis, reaching an EMR Diagnosis score of 4.3583 and surpassing the strongest non-Lung-R1 baseline by 0.1476 points. These results demonstrate the value of LungKG-guided training for EMR-based pulmonary diagnosis.", "date": "2026-06-10", "categories": []}
{"id": "2512.16189", "title": "Mitigating hallucinations in healthcare LLMs with granular fact-checking and domain-specific adaptation", "abstract": "In healthcare, it is essential for any Large Language Model (LLM)-generated output to be reliable and accurate, particularly in cases involving decision-making and patient safety. However, the outputs are often unreliable in such critical areas due to the risk of hallucinated outputs from the LLMs. To address this issue, we propose a fact-checking module that operates independently of any LLM, along with a domain-specific summarization model designed to minimize hallucination rates. Our model is fine-tuned using Low-Rank Adaptation (LoRA) on the MIMIC-III dataset and is paired with the fact-checking module, which uses numerical tests for correctness and logical checks at a granular level through discrete logic in natural language processing (NLP) to validate facts against electronic health records (EHRs). We trained the LLM on the full MIMIC-III dataset. For evaluation of the fact-checking module, we sampled 104 summaries, extracted them into 3786 propositions, and used these as facts. The fact-checking module achieves a precision of 0.8904, a recall of 0.8234, and an F1-score of 0.8556. Additionally, the LLM summary achieves a ROUGE-1 score of 0.5797 and a BERTScore of 0.9120 for summary quality.", "date": "2025-12-18", "categories": []}
{"id": "2606.05463", "title": "PSEBench: A Controllable and Verifiable Benchmark for Evaluating LLMs in Patient Safety Event Triage", "abstract": "Patient safety event triage, determining whether a clinical event is reportable under jurisdiction-specific policy, is a high-stakes task typically performed manually by patient safety experts. Although LLMs may support this workflow, reliable evaluation is limited by the lack of benchmarks to capture evidence-grounded policy reasoning, proactive information seeking for incomplete reports, and principled abstention in irreducibly ambiguous cases. We address this gap with a policy-grounded construction methodology centered on the clause card, a structured representation that factorizes regulatory text into auditable decision specifications. Combining clause cards with anchor-driven instantiation and closed-loop verification, our scalable pipeline produces narratives with by-construction ground truth and naturally supports generating missing information and uncertain variants. We instantiate this method on Minnesota's 29 Reportable Adverse Health Events, producing PSEBench, a 5,074-case benchmark with an agentic evaluation environment. Evaluation on 15 representative LLMs reveals consistent capability trends, demonstrates the benchmark's utility, and identifies actionable gaps toward reliable LLM-based patient safety event triage.", "date": "2026-06-03", "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": "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": "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": "2606.07540", "title": "Finding Hidden Relationships Between Medical Concepts by Leveraging Metamap and Text Mining Techniques", "abstract": "Text is one of the most common ways to store data in this computerized world. At a glance, it may seem that those data are not interconnected. But in reality, data can have hidden connections. Therefore, in this research, a new model has been presented that can find hidden relationships between two medical concepts by using MetaMap and appropriate text-mining techniques. Specifically, the model creates a new comprehensive index structure and can find cross-document hidden links connecting topics of interest that most existing approaches have ignored. Experiments show the effectiveness of the proposed model in discovering new connections between topics.", "date": "2026-04-30", "categories": []}
{"id": "2606.07853", "title": "Beyond English benchmarks: clinical llm evaluation in Brazilian Portuguese", "abstract": "Large Language Models are transforming the support for clinical decision and their application in real scenarios. Yet, most benchmarks are conducted in English, and cross-lingual evaluation is needed to tackle the language gaps in global access. We introduce ClinicalBr, the first bilingual benchmark for clinical decision built from real Brazilian case reports. The corpus contains 2,892 cases drawn from 28 SciELO medical journals, spanning 18 specialties, and is structured as parallel Portuguese-English pairs. Each case supports four evaluation tasks: diagnosis retrieval, differential diagnosis, exam recommendation, and treatment planning. We evaluate four models: MedGemma-27B, Sabi\\'a-4, DeepSeek-R1, and o3-mini, across both languages. The central finding is that the Portuguese-English performance gap is task-dependent, not general. In diagnosis retrieval, English yields a consistent advantage across all models, with +7.5-12.1 accuracy points. This advantage disappears in differential diagnosis, exam recommendation, and treatment planning, where confidence intervals cross zero for most models and Portuguese completeness scores are marginally higher. Brazilian-endemic conditions proved easier than the full corpus, not harder, indicating that tropical presentations are adequately represented in current pre-training. Exam recommendation was the hardest task across all models and both languages, with F1 scores below 0.10, well below the differential diagnosis ceiling of 0.20-0.27.", "date": "2026-06-05", "categories": []}
{"id": "2606.08311", "title": "Curation of a Cardiology Interface Terminology for Highlighting Electronic Health Records using Machine Learning", "abstract": "Electronic health record (EHR) notes are dense medical documents containing large amounts of information, often filled with complex medical jargon. Highlighting all details in EHRs helps reduce the likelihood of missing crucial information by drawing attention to key content. This study proposes the design of a Cardiology Interface Terminology (CIT) to accurately highlight all details in EHR notes of cardiology patients. We introduce an innovative Machine Learning (ML) technique for the design of CIT. The ML technique requires training data. Manual preparation of such training data is time-consuming and expensive. The process of the CIT design includes three phases. In the first two phases, we innovatively derive a training data CIT to be used by the third phase, ML technique. We start by designing an initial CIT, composed of several components: the cardiology-related sub-hierarchies of SNOMED, other SNOMED concepts mined from EHRs of build set, and necessary components of terms e.g., medical abbreviations and medications. Utilizing an iterative process, fine-grained phrases containing initial CIT concepts are extracted from build set as CIT concept candidates. The candidate concepts are semi-automatically reviewed before being added to CIT, yielding the training data CIT, TCIT. In the third phase, a ML model is trained with TCIT to identify candidates fitting to be concepts in the CIT. This model is used to extract further concepts from build set, yielding the final CIT. The final CIT is then used to highlight the test set and evaluate the extent to which it captures details in an unseen EHR dataset. For this purpose, four evaluation metrics, coverage, breadth, completeness, and conciseness are used. The highlighted test set has a coverage of 74.21%, with a breadth of 1.68. For 20 random notes in test set, the average completeness is 98.2% and average conciseness is 84.2%.", "date": "2026-06-06", "categories": []}
{"id": "2606.08769", "title": "RadOT-Eval: Auditable Structured-Evidence Transport for Radiology Report Evaluation", "abstract": "Automatic evaluation is critical for high-stakes text generation, where errors often involve omitted findings, hallucinated content, polarity reversals, location changes, uncertainty mismatches, and temporal-comparison errors rather than low surface similarity alone. Radiology report generation provides a challenging test case because generated reports must preserve structured clinical evidence across sources. We present RadOT-Eval, an interpretable structured-evidence optimal transport framework for offline auditing of radiology report generation. RadOT-Eval decomposes reference and candidate reports into attribute-structured clinical evidence units, aligns corresponding evidence using entropy-regularized optimal transport, and uses clinically meaningful side-channel discrepancies in a monotone risk model to predict error burden. All transport, feature, and readout choices are selected using the ReXVal dataset, and the frozen system is evaluated on the independent RadEvalX dataset. RadOT-Eval achieves Spearman correlations of 0.715, 0.548, and 0.399 with total, clinically significant, and clinically insignificant annotated error burden, respectively, yielding higher point estimates than standard evaluation metrics and the open-source large language model (LLM)-based evaluator GREEN-radllama2-7B. In a frozen auxiliary corruption-sensitivity stress test on ReXErr-v1, RadOT-Eval achieves 0.768 AUROC and a 0.990 corrupted-greater-than-clean paired win rate. These results show that structured evidence transport provides an auditable, rank-oriented evaluation tool for high-stakes generated clinical text under ReXVal-only model selection and frozen RadEvalX testing.", "date": "2026-06-07", "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": "2605.18937", "title": "Evaluating the Utility of Personal Health Records in Personalized Health AI", "abstract": "Patient-managed Personal Health Records (PHRs) promises to empower patients to better understand their health; but information in the record is complex, potentially hindering insights. In this study, we assess the potential of large language models (LLMs, Gemini 3.0 Flash) to provide helpful answers to user health queries, when provided clinical data from PHRs as context. A total of 2,257 user queries were drawn from 3 different distributions to represent patient questions: shorter web search queries, longer questions derived from templates of chatbot conversations, and questions patients asked to their healthcare team (patient calls). Queries were matched with de-identified PHRs (from a pool of 1,945). Gemini responses were generated (1) without PHR context; (2) with a basic summary of demographics, conditions, and medications; (3) with full, extensive clinical notes. For evaluation, we leveraged an existing rating framework (SHARP), and developed a new framework for specific error modes when interpreting PHRs. Evaluation was performed using autoraters for the full set, and with clinician ratings for a subset (n=95), with both sets of raters knowing the full PHR context. We see significant improvements in the helpfulness of answers to all question types with PHR data (p < 0.001, paired t-test). We also observe potential gains in safety, accuracy, relevance and personalization of answers. Our PHR evaluation framework further identifies gaps in LLM understanding of particular aspects of complex PHRs, such as temporal disorientation, and rare but meaningful confabulations. These results suggest potential for PHR data to help people with a wide range of user needs; and provide a framework for monitoring for gaps in LLM answers based on PHR context. This study motivates further work to assess and realize potential benefits to users from understanding their health records.", "date": "2026-05-18", "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": "2606.05970", "title": "Measuring the sensitivity of LLM-based structured extraction to prompt, model, and schema choices in clinical discharge summaries", "abstract": "Large language models are increasingly used for structured extraction from clinical free-text notes, but the sensitivity of their output to upstream configuration choices is less understood than their accuracy on fixed benchmarks. This work measures that sensitivity without human-annotated ground truth, by holding the extraction task fixed and varying one choice at a time. The fixed schema comprises 17 clinical documentation flags on a three-way yes/no/not_documented value set and a 47-tag vocabulary for the primary admission reason. Three prompt variants expressing this schema were each run at two model sizes on MIMIC-IV v3.1 discharge summaries. Cross-prompt agreement was measured by Cohen's kappa on ICD-stratified subsets. A paired same-note comparison isolated the effect of model choice, and a post-hoc collapse of the three-way flags to binary tested the schema's contribution to disagreement. On the three-way flags, the two models reach the same pooled cross-prompt agreement (median kappa 0.69 and 0.68); the larger model raises agreement on some fields and lowers it on others, a redistribution rather than the absence of an effect. Collapsing the schema to binary dissolves most of the cross-prompt disagreement, locating it on the absence-versus-silence distinction rather than on whether the finding is present. On the multi-class admission categorization, changing the model reassigns the dominant tag on close to half of all notes while changing the prompt phrasing reassigns it on roughly one in eight, and the larger model places far less mass on residual catch-all categories (44% to 26%). These patterns indicate a schema-imposed source of disagreement concentrated on the absence-versus-silence axis and a dominance of model over prompt phrasing on multi-class categorization, identified by a reusable methodology for auditing extraction reproducibility on a population-scale deployment.", "date": "2026-06-04", "categories": []}
{"id": "2605.28910", "title": "Hallucination Detection-Guided Preference Optimization for Clinical Summarization", "abstract": "Large language models (LLMs) have shown promise on summarization tasks, but they often produce hallucinations, which are unsupported or incorrect statements that limit their reliability in specialized healthcare applications. We introduce Hallucination Detection Guided Self-Refinement (HDSR), an inference-time method that leverages hallucination detectors to guide iterative summary revisions toward factual corrections. Building on this, we propose HDSR for Preference Learning (HDSR-PL), which converts detector-guided refinement trajectories into preference pairs for model finetuning. Extensive experiments show that our methods substantially reduce hallucinations for Llama and Gemma models in summarizing real-world clinical notes from MIMIC-IV-Note v2.2. For example, HDSR reduces 24% and HDSR-PL reduces 48% hallucinations in Llama-3.1-8B-Instruct. Importantly, both methods preserve summary fluency, coherence, and relevance according to human expert and LLM-Jury evaluations. Together, these results demonstrate that detection-informed refinement and preference learning offer an automated solution for improving factual faithfulness in clinical summarization.", "date": "2026-05-27", "categories": []}
{"id": "2606.01904", "title": "KliniskVestBERT: BERT Model Specialised to Norwegian Clinical Texts", "abstract": "The increasing application of Natural Language Processing (NLP) in healthcare demands language models specifically attuned to the complexities of clinical language. This work introduces KliniskVestBERT, a suite of three BERT-based encoder models pre-trained on a substantial corpus of real-world, de-identified Norwegian clinical texts from Helse Vest. We continue pretraining existing language models Nb-BERT-large, NorBERT3-large, and ModernBERT on our specialized clinical dataset. This dataset is based on a representative population of Helse Vest patients. The included document types are carefully curated to encompass a broad clinical spectrum in bokm{\\aa}l and nynorsk including discharge summaries, surgical reports, nursing notes etc. ensuring comprehensive representation of the linguistic landscape within Norwegian healthcare settings. Evaluation on three synthtetic Norwegian clinical benchmark datasets and two real-world problems demonstrates that each of our clinically specialized models consistently outperforms their baseline counterparts, highlighting the significant benefit of domain-specific pre-training for NLP tasks within the clinical domain. The project was a joint effort by all Helse Vest entities (Helse Bergen, Helse Fonna, Helse F{\\o}rde and Helse Stavanger) with DIPS under the project lead of Helse Vest ICT.", "date": "2026-06-01", "categories": []}
{"id": "2606.03250", "title": "The Word and the Way: Strategies for Domain-Specific BERT Pre-Training in German Medical NLP", "abstract": "Digital healthcare generates vast amounts of clinical text that can support AI-assisted applications, yet German biomedical language models remain limited by older architectures or restricted training data. We present ChristBERT (Clinical- and Healthcare-Related Issues and Subjects Tuned BERT), a family of domain-specific German RoBERTa-based language models trained on a 13.5GB corpus of scientific publications, clinical texts, health-related web content, and translated clinical resources. To investigate the impact of domain adaptation strategies in German clinical NLP, we compare continued pre-training, training from scratch, and domain-specific vocabulary adaptation. The resulting models are evaluated on three medical named entity recognition tasks and two text classification tasks. ChristBERT consistently outperforms existing general-purpose and medical German language models on four of five benchmarks and establishes a new state of the art for German clinical language modeling. Our results show that the optimal adaptation strategy is task-dependent: in our evaluation, training from scratch is particularly effective for highly specialized clinical texts, whereas continued pre-training performs well on more commonly written medical texts. All models are publicly released to support future research and applications in German medical NLP.", "date": "2026-06-02", "categories": []}
{"id": "2510.17532", "title": "OncoReason: Structuring Clinical Reasoning in LLMs for Robust and Interpretable Survival Prediction", "abstract": "Predicting cancer treatment outcomes requires models that are both accurate and interpretable, particularly in the presence of heterogeneous clinical data. While large language models (LLMs) have shown strong performance in biomedical NLP, they often lack structured reasoning capabilities critical for high-stakes decision support. We present a unified, multi-task learning framework that aligns autoregressive LLMs with clinical reasoning for outcome prediction on the MSK-CHORD dataset. Our models are trained to jointly perform binary survival classification, continuous survival time regression, and natural language rationale generation. We evaluate three alignment strategies: (1) standard supervised fine-tuning (SFT), (2) SFT with Chain-of-Thought (CoT) prompting to elicit step-by-step reasoning, and (3) Group Relative Policy Optimization (GRPO), a reinforcement learning method that aligns model outputs to expert-derived reasoning trajectories. Experiments with LLaMa3-8B and Med42-8B backbones demonstrate that CoT prompting improves F1 by +6.0 and reduces MAE by 12%, while GRPO achieves state-of-the-art interpretability and predictive performance across BLEU, ROUGE, and BERTScore. We further show that existing biomedical LLMs often fail to produce valid reasoning traces due to architectural constraints. Our findings underscore the importance of reasoning-aware alignment in multi-task clinical modeling and set a new benchmark for interpretable, trustworthy LLMs in precision oncology.", "date": "2025-10-20", "categories": []}
{"id": "2601.09696", "title": "Empathy Applicability Modeling for General Health Queries", "abstract": "LLMs are increasingly being integrated into clinical workflows, yet they often lack clinical empathy, an essential aspect of effective doctor-patient communication. Existing NLP frameworks focus on reactively labeling empathy in doctors' responses but offer limited support for anticipatory modeling of empathy needs, especially in general health queries. We introduce the Empathy Applicability Framework (EAF), a theory-driven approach that classifies patient queries in terms of the applicability of emotional reactions and interpretations, based on clinical, contextual, and linguistic cues. We release a benchmark of real patient queries, dual-annotated by human annotators and GPT-4o. In the subset with human consensus, we also observe substantial human-GPT alignment. To validate EAF, we train classifiers on human-labeled and GPT-only annotations to predict empathy applicability, achieving strong performance and outperforming the heuristic and zero-shot LLM baselines. Error analysis highlights persistent challenges: implicit distress, clinical-severity ambiguity, and contextual hardship, underscoring the need for multi-annotator modeling, clinician-in-the-loop calibration, and culturally diverse annotation. EAF provides a framework for identifying empathy needs before response generation, establishes a benchmark for anticipatory empathy modeling, and enables supporting empathetic communication in asynchronous healthcare.", "date": "2026-01-14", "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": "2602.17513", "title": "Bridging the Domain Divide: Supervised vs. Zero-Shot Clinical Section Segmentation from MIMIC-III to Obstetrics", "abstract": "Clinical free-text notes contain vital patient information. They are structured into labelled sections; recognizing these sections has been shown to support clinical decision-making and downstream NLP tasks. In this paper, we advance clinical section segmentation through three key contributions. First, we curate a new de-identified, section-labeled obstetrics notes dataset, to supplement the medical domains covered in public corpora such as MIMIC-III, on which most existing segmentation approaches are trained. Second, we systematically evaluate transformer-based supervised models for section segmentation on a curated subset of MIMIC-III (in-domain), and on the new obstetrics dataset (out-of-domain). Third, we conduct the first head-to-head comparison of supervised models for medical section segmentation with zero-shot large language models. Our results show that while supervised models perform strongly in-domain, their performance drops substantially out-of-domain. In contrast, zero-shot models demonstrate robust out-of-domain adaptability once hallucinated section headers are corrected. These findings underscore the importance of developing domain-specific clinical resources and highlight zero-shot segmentation as a promising direction for applying healthcare NLP beyond well-studied corpora, as long as hallucinations are appropriately managed.", "date": "2026-02-19", "categories": []}
{"id": "2606.00018", "title": "Examine Clinicians' Modification of Hedging Language in Ambient AI Documentation: A Comparative Study of AI Drafts and Final Notes", "abstract": "Ambient AI documentation systems generate clinical note drafts that clinicians frequently revise before signing off into electronic health records, yet how these edits alter hedging language remains unclear. We conducted paired analysis of clinician-edited portions of ambient AI drafts and final notes to examine (1) whether these edits change the prevalence of hedging language, (2) whether these edits exhibit a systematic shift toward greater certainty or uncertainty, and (3) whether these changes in hedging prevalence and directionality differ by ambient AI vendors and clinical specialties. Among 62,811 paired note sections, hedging terms were more often introduced into previously non-hedged text than removed from previously hedged text, and post-edit text contained more hedging mentions than pre-edit text. Directionality analyses showed a significant overall tendency toward greater uncertainty in hedging-related replacement edits. Vendor and specialty analyses revealed substantial heterogeneity in hedging prevalence, pre-to-post changes in hedging mentions, and directionality.", "date": "2026-04-14", "categories": []}
{"id": "2606.00019", "title": "Understanding Stigmatizing Language in Clinical Documentation: A Paired Comparison of Ambient AI Drafts and Clinician Finalized Notes", "abstract": "Ambient artificial intelligence (AI) documentation tools are increasingly deployed to reduce clinician documentation burden, but their implications for biased language in clinical notes remain unclear. We conducted a large-scale comparison analysis of AI drafts and corresponding clinician finalized notes to quantify stigmatizing language changes pre- and post-editing. Using a lexicon-based natural language processing (NLP) pipeline, we measured (1) the prevalence of stigmatizing language in AI drafts, (2) the prevalence and term composition in final notes, and (3) the frequency of removal or introduction of stigmatizing terms. Across 66,297 paired note sections, 21.4% of AI draft sections contained at least one stigmatizing language mention, rising to 24.0% in clinician finalized versions. Introductions occurred more often than removals, suggesting clinician editing can be a net source of stigmatizing language entering the EHR with using Ambient AI.", "date": "2026-04-14", "categories": []}
{"id": "2606.00026", "title": "Cognitive-Linguistic Indicators of Depression in Online Communities: Analysed by DistilBERT and Holographic Reduced Representation", "abstract": "This paper investigates whether combining cognitively grounded linguistic features with transformer-based embeddings improves automated detection of depression in online text. Using Beck's Cognitive Theory of Depression, the study extracts cognitive distortions as measurable features, including first-person pronoun density, absolutist words, and negative emotion in Reddit posts from depression-related and control communities. Using a subset of the Kaggle Reddit Suicide and Depression Detection dataset, two classification pipelines are compared, a TF-IDF embedding with Naive Bayes as a baseline, and a hybrid model that concatenates DistilBERT sentence embeddings with Holographic Reduced Representation (HRR) vectors encoding the cognitive-linguistic features, followed by Logistic Regression. The hybrid DistilBERT HRR model achieves a macro F1 score of 0.94 versus 0.80 for the TD-IDF baseline, with 5-fold cross validation F1 improving from 0.83 to 0.92, and AUC from 0.958 to 0.981.", "date": "2026-04-15", "categories": []}
{"id": "2606.00647", "title": "LinguIUTics at PsyDefDetect: Iterative Imbalance-Aware Fine-tuning of Qwen3-8B for Psychological Defense Mechanism Classification", "abstract": "Detecting psychological defense mechanisms in conversational text remains a challenging clinical NLP problem. For the PsyDefDetect 2026 shared task (nine-class utterance classification evaluated via macro F1), our team LinguIUTics achieves a macro F1-score of 0.3917 on the official positive-class leaderboard, ranking 4th out of 21 registered teams and improving over the Ministral-8B task baseline (31.48 macro F1) by 7.7 absolute points (24.4 percent relative). BERT-family encoders and zero-shot LLMs proved ineffective on rare classes due to severe class imbalance, leading us to QLoRA fine-tuning of Qwen3-8B. We leverage three key strategies: grouped stratified cross-validation (preventing leakage), minority-class round-robin lexical augmentation, and a post-processing pipeline with logit bias tuning and ensemble blending. Together, these components close much of the validation-to-leaderboard gap and substantially improve minority-class recall, driving the critical \"Unclear\" class (Level 8) from near-zero performance to an F1 score of 0.797.", "date": "2026-05-30", "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": "2606.02487", "title": "Towards Multidisciplinary Summarization of Hospital Stays: Efficient Sentence-Level Clinical Provenance Categorization", "abstract": "Effective \"all-team\" summarization in high-complexity settings like the Neonatal Intensive Care Unit (NICU) requires aggregating insights from diverse disciplines (physicians, nurses, therapists) spread across hundreds of clinical free-text notes. Simply pooling heterogeneous text often leads to incoherent outputs. Structured summarization therefore first requires accurate categorization of sentence-level provenance across multi-source notes. This pilot study introduces a clinical provenance categorization pipeline using supervised fine-tuning (SFT) of large language models (LLMs). We adapted two Llama-3 models (8B and 70B) to MedSecId, a corpus of 2,002 MIMIC-III (Adult ICU) notes annotated with clinical provenance headers, achieving in-domain Macro F1 scores above 92% for both models. To evaluate cross-domain generalization, we assessed model capacity (8B vs. 70B) and quantization on a gold-standard dataset of 227 sentence-level spans derived from three multi-disciplinary NICU summaries. Experimental results demonstrate a scale-dependent transfer effect: while SFT produced only marginal changes for the 8B model, it substantially improved the 70B model, increasing Macro F1 by 7%. Notably, the quantized fine-tuned 70B model outperformed its full-precision baseline while substantially reducing computational requirements. These findings suggest that sufficient model capacity is critical for preserving semantic flexibility during cross-domain clinical transfer and that efficient quantized adaptation can enable structured provenance modeling for downstream summarization.", "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": "2509.12440", "title": "MedFact: Benchmarking the Fact-Checking Capabilities of Large Language Models on Chinese Medical Texts", "abstract": "Deploying Large Language Models (LLMs) in medical applications requires fact-checking capabilities to ensure patient safety and regulatory compliance. We introduce MedFact, a challenging Chinese medical fact-checking benchmark with 2,116 expert-annotated instances from diverse real-world texts, spanning 13 specialties, 8 error types, 4 writing styles, and 5 difficulty levels. Construction uses a hybrid AI-human framework where iterative expert feedback refines AI-driven, multi-criteria filtering to ensure high quality and difficulty. We evaluate 20 leading LLMs on veracity classification and error localization, and results show models often determine if text contains errors but struggle to localize them precisely, with top performers falling short of human performance. Our analysis reveals the \"over-criticism\" phenomenon, a tendency for models to misidentify correct information as erroneous, which can be exacerbated by advanced reasoning techniques such as multi-agent collaboration and inference-time scaling. MedFact highlights the challenges of deploying medical LLMs and provides resources to develop factually reliable medical AI systems.", "date": "2025-09-15", "categories": []}
{"id": "2605.16215", "title": "Fully Open Meditron: An Auditable Pipeline for Clinical LLMs", "abstract": "Clinical decision support systems (CDSS) require scrutable, auditable pipelines that enable rigorous, reproducible validation. Yet current LLM-based CDSS remain largely opaque. Most \"open\" models are open-weight only, releasing parameters while withholding the data provenance, curation procedures, and generation pipelines that determine model behavior. Fully Open (FO) models, which expose the complete training stack end-to-end, do not currently exist in medicine. We introduce Fully Open Meditron, the first fully open pipeline for building LLM-CDSS, comprising a clinician-audited training corpus, a reproducible data construction and training framework, and a use-aligned evaluation protocol. The corpus unifies eight public medical QA datasets into a normalized conversational format and expands coverage with three clinician-vetted synthetic extensions: exam-style QA, guideline-grounded QA derived from 46,469 clinical practice guidelines, and clinical vignettes. The pipeline enforces system-wide decontamination, gold-label resampling of teacher generations, and end-to-end validation by a four-physician panel. We evaluate using an LLM-as-a-judge protocol over expert-written clinical vignettes, calibrated against 204 human raters. We apply the recipe to five FO base models (Apertus-70B/8B-Instruct, OLMo-2-32B-SFT, EuroLLM-22B/9B-Instruct). All MeditronFO variants are preferred over their bases. Apertus-70B-MeditronFO improves +6.6 points over its base (47.2% to 53.8%) on aggregate medical benchmarks, establishing a new FO SoTA. Gemma-3-27B-MeditronFO is preferred over MedGemma in 58.6% of LLM-as-a-judge comparisons and outperforms it on HealthBench (58% vs 55.9%). These results show that fully open pipelines can achieve state-of-the-art domain-specific performance without sacrificing auditability or reproducibility.", "date": "2026-05-15", "categories": []}
{"id": "2605.30529", "title": "Generalistic or Specific Embeddings, Which is Better? An Empirical Study on Search for Clinical Coding in Non-English Languages", "abstract": "Sentence-embedding models for semantic search are overwhelmingly developed and evaluated on English corpora. When applied to clinical retrieval in other languages -- particularly retrieval of ICD-10-CM / CIE-10 codes -- recall degrades in ways often masked by aggregate benchmarks. We study whether large generative language models can serve as data factories to close this gap. We build a two-stage retriever (bi-encoder followed by cross-encoder reranker), fine-tuned from a Spanish biomedical encoder (PlanTL-GOB-ES/bsc-bio-ehr-es) on Gemini-generated synthetic data covering English, Spanish, Catalan, Italian, Portuguese and French, and evaluate against BioBERT-ST and the un-tuned Spanish encoder. The bi-encoder alone matches BioBERT-ST on MRR (0.876 vs. 0.866) and overtakes it on R@3 (0.650 vs. 0.626) and R@5 (0.804 vs. 0.790) without English biomedical pretraining. Adding a cross-encoder reranker lifts aggregate R@5 to 0.822 and dominates on four of five languages (+0.017 Spanish, +0.033 Catalan, +0.018 French, +0.037 Portuguese) at the cost of a small English regression. The trade-off is clinically acceptable: Portuguese reaches R@5 = 0.829 vs. BioBERT-ST's 0.714. Contributions: an open recipe for building domain-specific medical retrievers from LLM-generated data; quantification of the learning gain (MRR 0.755 to 0.876, +15.9% with ~19,500 synthetic pairs); and a characterisation of where gains concentrate by language and rank.", "date": "2026-05-28", "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": "2605.30646", "title": "Same Patient, Different Words, Different Diagnosis? Evaluating Semantic Stability in Clinical LLMs", "abstract": "Large Language Models (LLMs) are increasingly used in clinical applications. However, their behavior remains highly sensitive to subtle linguistic variations, such as rephrasing or syntactic variation. This sensitivity poses risks in safety-critical healthcare settings, where semantically equivalent inputs should produce consistent predictions. However, a key challenge is to ensure that prompt variations truly preserve clinical meaning, as embedding-based similarity metrics often fail to capture distinctions involving negation, temporality, or severity. To address this limitation, we propose a semantic verification framework based on Natural Language Inference (NLI) to filter meaning-preserving prompt variations, which are further refined using an LLM-as-a-judge and audited by a clinical expert. In addition, we introduce three metrics to quantify model sensitivity: MeaningPreserving Variation Sensitivity (MVS), confidence variation (\\Delta C), and Worst-Case Instability (WCI). We evaluate 16 open-source general-purpose (GP) and medical LLMs within the same model families and parameter scales, using reformulated prompts derived from the DiagnosisQA and MedQA datasets. Our results demonstrate that robustness differences between domain-specific (DS) models are mixed and highly model-dependent, i.e., domain specialization does not consistently improve or reduce robustness to meaning-preserving prompt reformulations. Several DS models rank among the most robust (when compared with GP counterparts), and strong GP baselines remain competitive as well.", "date": "2026-05-28", "categories": []}
{"id": "2605.30984", "title": "Generating Reports or Repeating Templates? Measuring and Mitigating Template Collapse in 3D CT Report Generation", "abstract": "Modern 3D medical vision-language models (VLMs) can generate fluent radiology-style text while exhibit critically low pathology detection and output diversity, collapsing to generic templates that under-report rare yet critical findings. We identify this failure mode as Template Collapse. This failure stems from the unique constraints of 3D medical imaging, e.g., limited data, severe label imbalance, and weak signals from volumetric encoders. Under these constraints, text-generation objectives encourage shortcut learning and fluent but weakly grounded reports. We systematically diagnose the Template Collapse through clinical fidelity, output diversity, normal-template bias, and rare-finding survival. To mitigate it, we propose CLarGen, a decoupled framework that separates what to say (clinical detection) from how to say it (language synthesis). CLarGen uses (i) a Latent Query Transformer for multi-label pathology detection, (ii) pathology-guided retrieval for clinically matched exemplars, and (iii) a medical language model to synthesize the final report from detected findings and retrieved context. Across state-of-the-art 3D CT report generation baselines, CLarGen mitigates Template Collapse and substantially improves clinical accuracy (macro-F1 0.487 vs. 0.189; CRG 0.472 vs. 0.368) while maintaining fluent reporting. Our results suggest that explicit, measurable clinical grounding is essential for template-collapse-resistant 3D CT report generation. Code will be released upon acceptance.", "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": "2605.27377", "title": "Enhancing LLM Medical Coding with Structured External Knowledge", "abstract": "Accurate medical coding requires consulting authoritative resources such as the ICD tabular list and coding guidelines. Existing LLM-based automated methods largely rely on LLMs' internal knowledge, which is prone to hallucination and cannot keep pace with guideline updates. We introduce RAG-Coding, an agentic, training-free method that augments LLMs with structured external knowledge: the tabular list is encoded as a knowledge graph capturing hierarchical and instructional code relationships, and the guidelines are distilled into concise, code-specific summaries rather than retrieved as raw text. To enable our study, we also introduce MDACE-2025, expert re-annotations of the MDACE dataset under the 2025 ICD-10-CM/PCS guidelines, adding code sequencing and justification comments. On MDACE, RAG-Coding outperforms the best LLM-based baseline by 3--13\\% in micro-F1 across five LLM backbones, and achieves comparable micro- and macro-F1 to the supervised state-of-the-art, with higher recall ($+$11\\%) at the cost of precision ($-$6\\%). On MDACE-2025, RAG-Coding outperforms all baselines, demonstrating effective generalisation to updated guidelines. Ablations confirm stepwise gains, highlighting the importance of integrating structured external knowledge for LLM-based medical coding.", "date": "2026-04-09", "categories": []}
{"id": "2605.30295", "title": "MedCase-Structured: A Text-to-FHIR Dataset for Benchmarking Diagnostic Reasoning in Clinically Realistic EHR Settings", "abstract": "Large language models (LLMs) show promise for clinical reasoning and decision support, but evaluation in realistic, electronic health record-congruent settings remains limited. Existing benchmarks often rely on static datasets or unstructured inputs that do not reflect the structured, interoperable data formats used in clinical systems. We introduce a pipeline for generating clinically realistic HL7 FHIR R4 bundles from unstructured text, enabling controllable evaluation of clinical decision support systems. The pipeline combines staged LLM generation with terminology-grounded validation and repair to reduce hallucinated codes and enforce structural and semantic consistency. Applying this approach to MedCaseReasoning, we construct MedCase-Structured, a synthetic dataset aligned with clinician-authored diagnostic cases, achieving valid FHIR generation for 82.5% of cases. Evaluation on MedCase-Structured reveals consistently lower diagnostic accuracy for LLMs on structured FHIR inputs than with plain text, highlighting the importance of deployment-aligned benchmarking.", "date": "2026-05-28", "categories": []}
{"id": "2601.12154", "title": "Analyzing Cancer Patients' Experiences with Embedding-based Topic Modeling and LLMs", "abstract": "This study investigates the use of neural topic modeling and LLMs to uncover meaningful themes from patient storytelling data, to offer insights that could contribute to more patient-oriented healthcare practices. We analyze a collection of transcribed interviews with cancer patients (132,722 words in 13 interviews). We first evaluate BERTopic and Top2Vec for individual interview summarization by using similar preprocessing, chunking, and clustering configurations to ensure a fair comparison on Keyword Extraction. LLMs (GPT4) are then used for the next step topic labeling. Their outputs for a single interview (I0) are rated through a small-scale human evaluation, focusing on {coherence}, {clarity}, and {relevance}. Based on the preliminary results and evaluation, BERTopic shows stronger performance and is selected for further experimentation using three {clinically oriented embedding} models. We then analyzed the full interview collection with the best model setting. Results show that domain-specific embeddings improved topic \\textit{precision} and \\textit{interpretability}, with BioClinicalBERT producing the most consistent results across transcripts. The global analysis of the full dataset of 13 interviews, using the BioClinicalBERT embedding model, reveals the most dominant topics throughout all 13 interviews, namely ``Coordination and Communication in Cancer Care Management\" and ``Patient Decision-Making in Cancer Treatment Journey''. Although the interviews are machine translations from Dutch to English, and clinical professionals are not involved in this evaluation, the findings suggest that neural topic modeling, particularly BERTopic, can help provide useful feedback to clinicians from patient interviews. This pipeline could support more efficient document navigation and strengthen the role of patients' voices in healthcare workflows.", "date": "2026-01-17", "categories": []}
{"id": "2604.14356", "title": "When PCOS Meets Eating Disorders: An Explainable AI Approach to Detecting the Hidden Triple Burden", "abstract": "Women with polycystic ovary syndrome (PCOS) face substantially elevated risks of body image distress, disordered eating, and metabolic challenges, yet existing natural language processing approaches for detecting these conditions lack transparency and cannot identify co-occurring presentations. We developed small, open-source language models to automatically detect this triple burden in social media posts with grounded explainability. We collected 1,000 PCOS-related posts from six subreddits, with two trained annotators labeling posts using guidelines operationalizing Lee et al. (2017) clinical framework. Three models (Gemma-2-2B, Qwen3-1.7B, DeepSeek-R1-Distill-Qwen-1.5B) were fine-tuned using Low-Rank Adaptation to generate structured explanations with textual evidence. The best model achieved 75.3 percent exact match accuracy on 150 held-out posts, with robust comorbidity detection and strong explainability. Performance declined with diagnostic complexity, indicating their best use is for screening rather than autonomous diagnosis.", "date": "2026-04-15", "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": "2605.28060", "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": "2605.28375", "title": "PrionNER: A Named Entity Recognition Dataset for Prion Disease Biomedical Literature", "abstract": "Prion diseases are rare, rapidly progressive, and fatal neurodegenerative disorders that remain difficult to diagnose, particularly in their early stages because of nonspecific clinical presentations. However, to our knowledge, there is no publicly available prion-disease-focused dataset designed to capture a broad range of clinically relevant entities from the biomedical literature. We introduce PrionNER, a manually annotated named entity recognition dataset for prion disease clinical information in PubMed abstracts. The current release comprises 317 abstracts, 2,943 sentences, and 6,955 text-bound entity annotations spanning 15 coarse-grained and 31 fine-grained clinically oriented entity types covering diseases, symptoms, diagnostics, findings, anatomy, treatments, and temporal and statistical evidence. Inter-annotator agreement reaches 81.78 exact-match F1, indicating strong annotation consistency. We benchmark supervised BERT baselines, W2NER, and zero-shot extractors on PrionNER. W2NER is the strongest supervised model, and Gemma-4-31B is the strongest zero-shot model, but the benchmark remains challenging, especially for structurally complex mentions and fine-grained clinically adjacent label distinctions. PrionNER provides a clinically grounded benchmark for prion-disease information extraction and supports research on rare-disease biomedical NLP under low-resource, fine-grained, and non-flat extraction conditions. The dataset, annotation guidelines, and evaluation scripts are available at https://github.com/daotuanan/PrionNER/.", "date": "2026-05-27", "categories": []}
{"id": "2605.28521", "title": "ClinicalEncoder26AM: A Multlilingual Diagnosable ColBERT Model; Evidences from the MultiClinNER Shared Task", "abstract": "ClinicalEncoder26AM is a multilingual Diagnosable ColBERT for clinical and biomedical texts, which aligns at multiple levels its token-level semantic with ClinicalMap25, a clinical latent space inspired by BioLORD-2023 and enriched with synthetic and annotated supervision. The post-training recipe builds upon BGE-M3, and combines synthetic clinical notes, patient--doctor conversations, and annotated resources such as MedMentions, while considering both named-entity-level and sentence-level representations in a multi-adapter distillation, along with a ColBERT-style retrieval objective. In this system demonstration paper, we evaluate the model in the MultiClinNER shared task by finetuning it as a BIO tagger for patient symptoms, disorders, and procedure spans, using a lightweight two-layer CNN head to improve local boundary detection. The resulting system remains simple, processes most documents in a single 8192-token window, and achieves state-of-the-art multilingual entity recall, while achieving Top 5 overall across all entity types and languages in Character-weighted F1 scores. Training curves further show that ClinicalEncoder26AM is markedly more data-efficient than the base M3 model, supporting the usefulness of its clinical post-training for downstream information extraction.\n  The model can be downloaded on https://huggingface.co/Parallia/ClinicalEncoder26AM-Diagnosable-Colbert-L2-for-multilingual-medical-texts", "date": "2026-05-27", "categories": []}
{"id": "2601.01668", "title": "EHRSummarizer: A Privacy-Aware, FHIR-Native Reference Architecture for Source-Grounded EHR Summarization", "abstract": "Clinicians routinely navigate fragmented electronic health record (EHR) interfaces to assemble a coherent picture of a patient's problems, medications, recent encounters, and longitudinal trends. This manuscript describes EHRSummarizer, a privacy-aware, FHIR-native reference architecture for structured EHR summarization. The architecture retrieves a targeted set of high-yield HL7 FHIR R4 resources, normalizes them into a clinical context package, and uses a constrained summarization stage to produce source-grounded summaries intended to support chart review. The architecture further clarifies missing-data status handling, medication-status ambiguity, controlled use of narrative clinical documents when available, and future source-to-summary traceability. The manuscript describes a reference architecture and prototype behavior rather than a validated clinical intervention, autonomous clinical decision-support system, or evidence of clinical benefit. Prototype demonstrations on synthetic and test FHIR environments illustrate end-to-end behavior and output formats; however, this manuscript does not report clinical outcomes, controlled workflow studies, or benchmark results. We outline an evaluation plan centered on faithfulness, omission risk, temporal correctness, usability, privacy, and operational monitoring to guide future institutional assessment.", "date": "2026-01-04", "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": "2604.13502", "title": "Using reasoning LLMs to extract SDOH events from clinical notes", "abstract": "Social Determinants of Health (SDOH) refer to environmental, behavioral, and social conditions that influence how individuals live, work, and age. SDOH have a significant impact on personal health outcomes, and their systematic identification and management can yield substantial improvements in patient care. However, SDOH information is predominantly captured in unstructured clinical notes within electronic health records, which limits its direct use as machine-readable entities. To address this issue, researchers have employed Natural Language Processing (NLP) techniques using pre-trained BERT-based models, demonstrating promising performance but requiring sophisticated implementation and extensive computational resources. In this study, we investigated prompt engineering strategies for extracting structured SDOH events utilizing LLMs with advanced reasoning capabilities. Our method consisted of four modules: 1) developing concise and descriptive prompts integrated with established guidelines, 2) applying few-shot learning with carefully curated examples, 3) using a self-consistency mechanism to ensure robust outputs, and 4) post-processing for quality control. Our approach achieved a micro-F1 score of 0.866, demonstrating competitive performance compared to the leading models. The results demonstrated that LLMs with reasoning capabilities are effective solutions for SDOH event extraction, offering both implementation simplicity and strong performance.", "date": "2026-04-15", "categories": []}
{"id": "2605.26346", "title": "The Daily Dose: Workflow-Integrated Large Language Model Automation for Clinical Summarization and Trial Identification in Radiation Oncology", "abstract": "Objective: To describe the design and early clinical evaluation of The Daily Dose (TDD), an LLM-driven, automated clinical summarization and clinical-trial identification system integrated into routine radiation oncology practice. Design: Mixed-methods evaluation using a cross-sectional, anonymous clinician survey administered after 1 month of system deployment. Exposure: Daily automated delivery of physician-specific email summaries generated using RadOnc-GPT, including patient schedules, concise EHR-derived clinical-status summaries, and automated identification of potentially relevant clinical trials for new or consult visits. Main Outcomes and Measures: Primary outcomes included self-reported usability, satisfaction, perceived usefulness, perceived impact on workflow, time savings, and intention for continued use. Internal consistency reliability was assessed using Cronbach's $\\alpha$. Results: Among 55 respondents, 52 (94.5\\%) worked in radiation oncology, and 38 (69.1\\%) were attending physicians. Most participants (83.6\\%) reported using TDD daily or several times per week. Mean (SD) scores were 3.89 (1.04) for usability and satisfaction, 3.43 (1.24) for perceived usefulness, and 3.80 (1.17) for impact and future use (5-point Likert scale). Overall satisfaction was positively associated with perceived time savings ($p < .001$). Participants reported variable time savings, with 27\\% estimating $\\geq 10$ minutes saved per day. The questionnaire demonstrated excellent internal consistency (overall Cronbach's $\\alpha$ = 0.97).", "date": "2026-05-25", "categories": []}
{"id": "2605.26463", "title": "Towards Error-Free EHRs: Reasoning-Intensive Consistency Verification Between Clinical Notes and Structured Tables in Electronic Health Records", "abstract": "Data consistency between unstructured clinical notes and structured tables in Electronic Health Records (EHRs) is essential for patient safety and clinical decision-making. However, existing work on note-table consistency verification mainly relies on surface-level matching of numeric values or simple events. Such approaches fail to capture the reasoning underlying real-world EHR documentation, including clinical interpretation, event relations, and temporal changes. To address this gap, we introduce EHR-ReasonCon, a reasoning-intensive benchmark for note-table consistency verification. Built on MIMIC-III with expert-guided annotations, it comprises 8,048 entities derived from clinical notes and provides high-quality ground-truth labels. The annotation protocol is supported by specialized table-exploration tools to ensure systematic evidence retrieval and reliable consistency assessment. We also propose EHR-Inspector, an LLM-based framework that segments notes, extracts anchor entities and temporal references, and uses table-exploration tools to verify consistency against structured tables. Evaluated using expert-validated LLM-as-a-judge metrics under harsh and lenient criteria, EHR-Inspector achieves state-of-the-art performance across multiple model backbones. Analyses further demonstrate the effectiveness of its components and highlight differences from human verification.", "date": "2026-05-26", "categories": []}
{"id": "2605.26560", "title": "Reliable Extraction of Clinical Follow-Up Instructions: A Hybrid Neural-Symbolic Pipeline", "abstract": "Objective. Outpatient notes carry follow-up instructions pairing actions with future times (\"MRI brain in two weeks\"). Extracting (action, date) pairs supports scheduling and audit, but generative extractors miss the date because linking and arithmetic are implicit in decoding. We test a hybrid neural-symbolic pipeline against direct generation. Methods. We define TestSpecification and TimeSpecification entities and a ScheduledFor relation. BioBERT feeds BIO tagging and a biaffine linker; entities are canonicalized via a 28-action ontology and times normalized to day offsets deterministically. We evaluate on a 2,000-note synthetic outpatient corpus with action-disjoint splits (18 train, 6 OOV-test) against zero-shot GPT-4o-mini and LoRA-fine-tuned LLaMA-3 8B with note-level bootstrap 95% CIs. Results. On 259-note seen and OOV splits the hybrid pipeline achieves Test-Time Pair F1 of 0.997 and 0.986 with 0.00-day MAE. Baselines reach high action F1 (LLaMA-3 0.992; GPT-4o-mini 0.963 seen) but Pair F1 stays at 0.51-0.57 (LLaMA-3) and 0.53 (GPT-4o-mini), CIs non-overlapping with the hybrid. Conclusion. Separating learned entity extraction from deterministic date arithmetic outperforms generation on this benchmark, generalizes to held-out actions, and exposes failure modes. Transfer to real EHR notes is the next validation; a first-pass realism check is in Limitations.", "date": "2026-05-26", "categories": []}
{"id": "2410.01648", "title": "DeIDClinic: A Risk-Aware Pseudonymization Framework for Clinical Text De-identification and Re-identification Risk Assessment", "abstract": "The increasing availability of sensitive textual data has created an urgent need for robust de-identification methods that enable compliant data sharing while preserving downstream utility. This paper presents DeID-Clinic, a multi-layered framework for automated pseudonymization and re-identification risk assessment of clinical free-text data. Our approach integrates domain-adapted transformer models, including BioBERT and ClinicalBERT, into the MASK de-identification framework to improve the detection and masking of protected health information (PHI). Beyond entity recognition, we introduce a novel document-level risk assessment module that quantifies residual re-identification risk using a combination of k-anonymity, l-diversity, t-closeness, contextual similarity, and entity co-occurrence analysis. Experiments conducted on the i2b2 2014 de-identification dataset demonstrate strong performance, achieving macro-level F1 scores above 0.96 for several entity categories, while enabling quantitative prioritization of high-risk documents for further review. Our results highlight the effectiveness of combining neural de-identification with explicit risk modeling, supporting privacy-preserving data sharing in sensitive domains. Although evaluated on clinical text, the proposed framework is generalizable to other privacy-critical domains such as legal and administrative documents, where reliable pseudonymization and risk-aware anonymization are essential. Keywords{Automated De-Identification, Risk Assessment, Patient Privacy, Pseudonymization, Personal Health Information}", "date": "2024-10-02", "categories": []}
{"id": "2601.05847", "title": "Schema-Grounded LLM Extraction for FHIR Patient Digital Twins", "abstract": "We revisit the problem of constructing interoperable patient digital twins from unstructured electronic health records (EHRs) and argue that the task is better cast not as a cascade of extraction modules but as constrained generation of a valid FHIR bundle. We introduce SG-LLM, a schema-grounded LLM extractor that (i) augments the prompt with candidate SNOMED-CT, RxNorm, and LOINC codes retrieved through a SapBERT index, (ii) decodes under a JSON Schema derived directly from FHIR R4 StructureDefinitions, and (iii) closes a validator-in-the-loop repair stage whose diagnostics are fed back as structured error messages. We argue that the twin's usefulness, not only span-level F1, is the right object of evaluation, and operationalize this with a clinical-utility experiment that measures the gap in 30-day readmission AUROC between classifiers trained on SG-LLM-generated FHIR bundles versus expert-curated ones. On MIMIC-IV and n2c2 2018 Track 2 benchmarks, SG-LLM matches or exceeds strong joint-extraction and vanilla-LLM baselines while producing substantially more valid bundles. Ablations isolate the contributions of retrieval, schema constraint, and the repair loop. All code, prompts, and schemas are released.", "date": "2026-01-09", "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": "2605.24172", "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": "2605.24755", "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": "2605.24902", "title": "When Reasoning Hurts: Source-Aware Evaluation of Frontier LLMs for Clinical SOAP Note Generation", "abstract": "Reasoning-enabled LLMs perform strongly on medical reasoning benchmarks, but it remains unclear whether these gains transfer to structured clinical documentation; we investigate this question using SOAP note generation from clinical dialogue in a source-aware benchmark spanning OMI Health, ACI-Bench, and PriMock57. We evaluate GPT-5.4, DeepSeek-V4-Flash, and Gemma-4-E4B in a controlled 2x2 design that independently toggles provider-native reasoning and same-source retrieval-augmented generation (RAG). Outputs are assessed using seven automatic metrics alongside two reference-aware LLM judges. Both evaluation approaches agree that a non-reasoning GPT-5.4 configuration achieves the highest overall quality, while DeepSeek-V4-Flash performs best among reasoning-enabled configurations. Enabling reasoning significantly degrades GPT-5.4 performance across all three datasets, whereas same-source RAG yields smaller, model-dependent improvements. Overall, the findings indicate that stronger reasoning capability should not be assumed to improve fidelity-sensitive SOAP note generation without dedicated, task-specific evaluation.", "date": "2026-05-24", "categories": []}
{"id": "2605.24907", "title": "Overview of the PsyDefDetect Shared Task at BioNLP 2026: Detecting Levels of Psychological Defense Mechanisms in Supportive Conversations", "abstract": "We present an overview of PsyDefDetect, the shared task on detecting levels of psychological defense mechanisms in emotional support dialogues, co-located with BioNLP@ACL 2026. Grounded in the clinically validated Defense Mechanism Rating Scales (DMRS) framework, the task asks systems to classify a target seeker utterance, given its preceding dialogue context, into one of nine categories: seven hierarchical DMRS levels plus two auxiliary labels. Participants worked on PsyDefConv, a newly released corpus of 200 dialogues and 2336 help-seeker utterances annotated under DMRS with substantial inter-annotator agreement. The task attracted 172 participants on CodaBench who produced 563 submissions, with 21 teams officially registering their results for the final ranking. The best system achieved a macro F1-score of 0.420, surpassing the strongest fine-tuned baseline reported in the dataset paper by a notable margin, yet leaving clear headroom. Our analysis highlights (i) a persistent tendency to over-predict the majority High-Adaptive class, (ii) a widening gap between accuracy and macro-F1 that reveals class-imbalance sensitivity, and (iii) the value of theory-aware and LLM-based approaches for fine-grained defensive-function classification. We release all task materials and invite the community to continue work on this novel intersection of clinical psychology and NLP.", "date": "2026-05-24", "categories": []}
{"id": "2605.25038", "title": "TRACE: A taxonomy-grounded synthetic dataset for teaching-program generation and session interpretation in Applied Behavior Analysis", "abstract": "Applied Behavior Analysis (ABA) is a clinical discipline whose documentation, teaching programs and multi-session behavioral logs, is formulaic and high-volume, yet real session data is HIPAA-protected and bound by professional confidentiality rules, blocking the release of a training corpus. We present TRACE (Taxonomy-Referenced ABA Clinical Examples), a 2,999-example synthetic instruction-tuning dataset covering two ABA tasks: teaching-program generation across Discrete Trial Training, Natural Environment Teaching, and Task Analysis; and multi-session behavioral interpretation across twelve trajectory patterns and thirteen target behaviors. Every example is produced by a deterministic taxonomy-driven generator grounded in the canonical ABA literature, and every example carries complete sampling provenance, the exact taxonomy cells that produced it. The dataset is released under CC BY-NC 4.0 for data and MIT for code, with stratified train (2,549), validation (149), test (281), and sanity (20) splits. TRACE is a research artifact and has not been clinically validated.", "date": "2026-05-24", "categories": []}
{"id": "2605.25440", "title": "A Multi-Agent LLM Framework for Rating the Quality of Surgical Feedback", "abstract": "Verbal feedback delivered by attending surgeons in the operating room plays a critical formative role in resident trainee skill acquisition. Yet, assessing the quality of trainer feedback and its effectiveness in influencing trainee behavior during live surgery remains a challenge. Prior studies assessed feedback content relying on extensive manual annotation by expert human raters and focused on developing broad taxonomies that overlook the qualitative aspects of feedback delivery such as clarity or urgency. Limited existing automated methods, including keyword analysis and topic modeling, also fail to capture these nuanced aspects. We introduce a two-stage LLM-based framework that discovers interpretable feedback quality criteria grounded in the context of surgical training. Our method uses multi-agent prompting and surgical domain knowledge injection to discover a small set of human interpretable scoring criteria (e.g., Encouraging, Urgent, Clear). These criteria are then used to automatically score live surgical feedback via an LLM-as-a-judge approach. Evaluation on 4.2k trainer feedback instances demonstrates that our AI-discovered criteria outperform prior content-based frameworks in predicting feedback effectiveness, including observed trainee behavioral adjustments and trainer approval. This work advances scalable, human-aligned assessment of communication quality in the operating room and provides a foundation for improving surgical teaching practices.", "date": "2026-05-25", "categories": []}
{"id": "2605.25463", "title": "A Lightweight Hybrid Transformer-CRF Architecture for Multi-Type Bangla Medical Entity Recognition", "abstract": "MedER refers to the identification of medical entities. It is crucial for extracting structured clinical information from unstructured medical text. Many existing systems rely on transformer-based models, which are computationally expensive and difficult to deploy in resource-constrained environments. Furthermore, earlier works often use relaxed evaluation metrics that artificially inflate performance by rewarding correct prediction of dominant \"Outside\" (O) tokens. In this paper, we propose a lightweight Medical Entity Recognition (MedER) framework for the Bangla language. We establish a rigorous baseline using a 12-layer BanglaBERT model combined with a Conditional Random Field (CRF) layer for exact-boundary entity detection. To address deployment constraints, we compress this teacher model into a 4-layer student network through Knowledge Distillation (KD), where the student learns from the teacher's pre-CRF soft emission logits. Finally, we apply INT8 dynamic quantization to further reduce model size and inference cost. Our final quantized student achieves an 8.6x CPU speedup while requiring nearly 48 percent less storage than the CRF teacher model.", "date": "2026-05-25", "categories": []}
{"id": "2605.25566", "title": "Uncertainty Reasoning with Large Language Models for Explainable Disease Diagnosis", "abstract": "Clinical decision-making requires reasoning over incomplete, imprecise, and linguistically expressed patient narratives. While large language models (LLMs) excel at extracting latent information from natural language, they lack the verifiability and interpretability essential for trustworthy medical AI. We propose a neuro-symbolic reasoning framework that aligns LLMs with formal logic to enable explainable and formally verifiable medical diagnosis. Patient descriptions and clinical guidelines are embedded into a neural knowledge base, where LLMs extract structured medical entities, temporal relations, and fuzzy symptom patterns, which are decoded into a symbolic knowledge base expressed in fuzzy logic and declarative rules. We perform two-stage reasoning: (1) inductive symbolic generalization to capture diagnostic patterns from encoded narratives, and (2) inference verification via a logic programming engine to derive and validate diagnoses consistent with clinical standards. Each symptom is treated as a fuzzy predicate with probabilistic weights, and inference paths are auditable, adjustable, and compatible with physician feedback. Unlike purely statistical methods, our system supports iterative refinement: misalignment between LLM-generated diagnoses and ground truth can be traced, explained, and corrected through formal rules. By combining logic-based transparency, LLM adaptability, and probabilistic robustness, the framework enables human-aligned healthcare inference with strong generalization and verifiable, step-by-step reasoning chains. We validate our framework on public benchmarks, demonstrating effective reconciliation of symbolic reasoning and LLMs with real-world clinical narratives. Results show performance comparable to state-of-the-art LLMs, while additionally providing interpretable reasoning paths and formally verifiable diagnostic conclusions.", "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": "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": "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": "2605.21845", "title": "Comparing LLM and Fine-Tuned Model Performance on NVDRS Circumstance Extraction with Varying Prompt Complexity", "abstract": "Suicide is a leading cause of death in the United States, and understanding the circumstances that precede it requires extracting structured information from death investigation narratives. Many of these circumstances require semantic inference beyond simple keyword matching. We develop a ``Complexity Score'' algorithm that analyzes coding manual structure to predict when detailed prompts with full coding guidelines improve over name-only prompts. We then construct a hybrid approach that selects prompt strategy per circumstance. We evaluate large language models (LLMs) against fine-tuned RoBERTa on 25 inferentially complex circumstances from the National Violent Death Reporting System (NVDRS). We found that LLMs substantially outperform on low-prevalence circumstances where training data is insufficient. We further demonstrate that our framework generalizes across frontier LLMs, with GPT-5.2, Gemini 2.5 Pro and Llama-3 70B showing consistent performance patterns. These findings support a hybrid architecture where LLMs handle rare, inferentially complex circumstances while fine-tuned models handle common ones.", "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": "2507.01053", "title": "M3: Conversational LLMs Simplify Secure Clinical Data Access, Understanding, and Analysis", "abstract": "Large-scale clinical databases offer opportunities for medical research, but their complexity creates barriers to effective use. The Medical Information Mart for Intensive Care (MIMIC-IV), one of the world's largest open-source electronic health record databases, traditionally requires both SQL proficiency and clinical domain expertise. We introduce M3, a system that enables natural language querying of MIMIC-IV data through the Model Context Protocol. With a single command, M3 retrieves MIMIC-IV from PhysioNet, launches a local SQLite instance or connects to hosted BigQuery, and allows researchers to pose clinical questions in plain English. We evaluated M3 using samples from the EHRSQL 2024 benchmark with two language models. On one hundred answerable questions, the proprietary Claude Sonnet 4 achieved 94% accuracy and the open-weights gpt-oss-20B (deployable locally on consumer hardware) achieved 93%; on a matched sample of one hundred unanswerable questions, where correct behavior is to abstain rather than produce SQL, gpt-oss-20B correctly abstained on 69%. Both models translate natural language into SQL, execute queries against MIMIC-IV, and return structured results alongside the underlying query for verification. Error analysis revealed that most failures stemmed from complex temporal reasoning or ambiguous question phrasing rather than fundamental architectural limitations. The comparable performance of a smaller open-weights model demonstrates that privacy-preserving local deployment is viable for sensitive clinical data analysis. M3 lowers technical barriers to critical care data analysis and is designed with security measures including OAuth2 authentication, query validation, and audit logging.", "date": "2025-06-27", "categories": []}
{"id": "2603.00086", "title": "Iterative LLM-based improvement for French Clinical Interview Transcription and Speaker Diarization", "abstract": "Automatic speech recognition for French medical conversations remains challenging, with word error rates often exceeding 30% in spontaneous clinical speech. This study proposes a multi-pass LLM post-processing architecture alternating between Speaker Recognition and Word Recognition passes to improve transcription accuracy and speaker attribution. Ablation studies on two French clinical datasets (suicide prevention telephone counseling and preoperative awake neurosurgery consultations) investigate four design choices: model selection, prompting strategy, pass ordering, and iteration depth. Using Qwen3-Next-80B, Wilcoxon signed-rank tests confirm significant WDER reductions on suicide prevention conversations (p<0.05, n=18), while maintaining stability on awake neurosurgery consultations (n=10), with zero output failures and acceptable computational cost (RTF 0.32), suggesting feasibility for offline clinical deployment, pending validation on larger corpora.", "date": "2026-02-16", "categories": []}
{"id": "2605.20052", "title": "PromptRad: Knowledge-Enhanced Multi-Label Prompt-Tuning for Low-Resource Radiology Report Labeling", "abstract": "Automatic report labeling facilitates the identification of clinical findings from unstructured text and enables large-scale annotation for medical imaging research. Existing rule-based labelers struggle with the diverse descriptions in clinical reports, while fine-tuning pre-trained language models (PLMs) requires large amounts of labeled data that are often unavailable in clinical settings. In this paper, we propose PromptRad, a knowledge-enhanced multi-label \\textbf{prompt}-tuning approach for \\textbf{rad}iology report labeling under low-resource settings. PromptRad reformulates multi-label classification as masked language modeling and incorporates synonyms from the UMLS Metathesaurus into a multi-word verbalizer to enrich category representations. By fine-tuning the PLM without additional classification layers, PromptRad requires substantially less labeled data than conventional fine-tuning. Experiments on liver CT (computed tomography) reports show that PromptRad outperforms dictionary-based and fine-tuning baselines with only 32 labeled training examples, and achieves competitive performance with GPT-4 despite using a much smaller model. Further analysis demonstrates that PromptRad captures complex negation patterns more effectively than existing methods, making it a promising solution for report labeling in data-scarce clinical scenarios. Our code is available at https://github.com/ila-lab/PromptRad.", "date": "2026-05-19", "categories": []}
{"id": "2605.20197", "title": "MedicalBench: Evaluating Large Language Models Toward Improved Medical Concept Extraction", "abstract": "Medical concept extraction from electronic health records underpins many downstream applications, yet remains challenging because medically meaningful concepts are frequently implied rather than explicitly stated in medical narratives. Existing benchmarks with human-annotated evidence spans underscore the importance of grounding extracted concepts in medical text. However, they predominantly focus on explicitly stated concepts instead of implicit concepts. We present MedicalBench, a benchmark for medical concept extraction with evidence grounding that evaluates implicit medical reasoning. MedicalBench formulates medical concept extraction as a verification task over medical note-concept pairs, coupled with sentence-level evidence identification. Built from MIMIC-IV discharge summaries and human-verified ICD-10 codes, the dataset is curated through a multi-stage large language model (LLM) triage pipeline followed by medical annotation and expert review. It deliberately includes implicit positives, semantically confusable negatives, and cases where LLM judgments disagree with medical expert assessments. We define two complementary evaluation tasks: (1) medical concept extraction and (2) sentence-level evidence retrieval, enabling assessment of both correctness and interpretability. Benchmarking state-of-the-art LLMs reveals that performance remains modest, highlighting the difficulty of extracting implicitly expressed concepts. We further show that performance is largely invariant to note length, indicating that MedicalBench isolates reasoning difficulty rather than superficial confounders. MedicalBench provides the first systematic benchmark for implicit, evidence-grounded medical concept extraction, offering a foundation for developing medical language models that can both identify medically relevant concepts and justify their predictions in a transparent and medically faithful manner.", "date": "2026-04-05", "categories": []}
{"id": "2605.21154", "title": "Automated ICD Classification of Psychiatric Diagnoses: From Classical NLP to Large Language Models", "abstract": "Mental health has become a global priority, leading to a massive administrative burden in the coding of clinical diagnoses. This study proposes the automation of psychiatric diagnostic analysis by mapping free-text descriptions to the International Classification of Diseases (ICD) using Natural Language Processing (NLP) and Machine Learning (ML) techniques. Utilizing a specialized dataset of 145,513 Spanish psychiatric descriptions, various text representation paradigms were evaluated, ranging from classical frequency-based models (BoW, TF-IDF) to state-of-the-art Large Language Models (LLMs) such as e5\\_large, BioLORD, and Llama-3-8B. Results indicate that transformer-based embeddings consistently outperform traditional methods by capturing implicit semantic cues and nuanced medical terminology. The e5\\_large model, through end-to-end fine-tuning, achieved the highest performance with a $F1_{micro}$ score of 0.866. This research demonstrates that adapting LLMs to specific clinical nomenclature is essential for overcoming the challenges of ``long-tail'' label distributions and the inherent ambiguity of psychiatric discourse.", "date": "2026-05-20", "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": "2507.03122", "title": "Federated Learning for ICD Classification with Lightweight Models and Pretrained Embeddings", "abstract": "This study investigates the feasibility and performance of federated learning (FL) for multi-label ICD code classification using clinical notes from the MIMIC-IV dataset. Unlike previous approaches that rely on centralized training or fine-tuned large language models, we propose a lightweight and scalable pipeline combining frozen text embeddings with simple multilayer perceptron (MLP) classifiers. This design offers a privacy-preserving and deployment-efficient alternative for clinical NLP applications, particularly suited to distributed healthcare settings. Extensive experiments across both centralized and federated configurations were conducted, testing six publicly available embedding models from Massive Text Embedding Benchmark leaderboard and three MLP classifier architectures under two medical coding (ICD-9 and ICD-10). Additionally, ablation studies over ten random stratified splits assess performance stability. Results show that embedding quality substantially outweighs classifier complexity in determining predictive performance, and that federated learning can closely match centralized results in idealized conditions. While the models are orders of magnitude smaller than state-of-the-art architectures and achieved competitive micro and macro F1 scores, limitations remain including the lack of end-to-end training and the simplified FL assumptions. Nevertheless, this work demonstrates a viable way toward scalable, privacy-conscious medical coding systems and offers a step toward for future research into federated, domain-adaptive clinical AI.", "date": "2025-07-03", "categories": []}
{"id": "2509.04471", "title": "MOSAIC: A Multilingual, Taxonomy-Agnostic, and Computationally Efficient Approach for Radiological Report Classification", "abstract": "Radiology reports contain rich clinical information that can be used to train imaging models without relying on costly manual annotation. However, existing approaches face critical limitations: rule-based methods struggle with linguistic variability, supervised models require large annotated datasets, and recent LLM-based systems depend on closed-source or resource-intensive models that are unsuitable for clinical use. Moreover, current solutions are largely restricted to English and single-modality, single-taxonomy datasets. We introduce MOSAIC, a multilingual, taxonomy-agnostic, and computationally efficient approach for radiological report classification. Built on a compact open-access language model (MedGemma-4B), MOSAIC supports both zero-/few-shot prompting and lightweight fine-tuning, enabling deployment on consumer-grade GPUs. We evaluate MOSAIC across seven datasets in English, Spanish, French, and Danish, spanning multiple imaging modalities and label taxonomies. The model achieves a mean macro F1 score of 88 across five chest X-ray datasets, approaching or exceeding expert-level performance, while requiring only 24 GB of GPU memory. With data augmentation, as few as 80 annotated samples are sufficient to reach a weighted F1 score of 82 on Danish reports, compared to 86 with the full 1600-sample training set. MOSAIC offers a practical alternative to large or proprietary LLMs in clinical settings. Code and models are open-source. We invite the community to evaluate and extend MOSAIC on new languages, taxonomies, and modalities.", "date": "2025-08-29", "categories": []}
{"id": "2601.09722", "title": "ADMEDTAGGER: an annotation framework for distillation of expert knowledge for the Polish medical language", "abstract": "In this work, we present an annotation framework that demonstrates how a multilingual LLM pretrained on a large corpus can be used as a teacher model to distill the expert knowledge needed for tagging medical texts in Polish. This work is part of a larger project called ADMEDVOICE, within which we collected an extensive corpus of medical texts representing five clinical categories - Radiology, Oncology, Cardiology, Hypertension, and Pathology. Using this data, we had to develop a multi-class classifier, but the fundamental problem turned out to be the lack of resources for annotating an adequate number of texts. Therefore, in our solution, we used the multilingual Llama3.1 model to annotate an extensive corpus of medical texts in Polish. Using our limited annotation resources, we verified only a portion of these labels, creating a test set from them. The data annotated in this way were then used for training and validation of 3 different types of classifiers based on the BERT architecture - the distilled DistilBERT model, BioBERT fine-tuned on medical data, and HerBERT fine-tuned on the Polish language corpus. Among the models we trained, the DistilBERT model achieved the best results, reaching an F1 score > 0.80 for each clinical category and an F1 score > 0.93 for 3 of them. In this way, we obtained a series of highly effective classifiers that represent an alternative to large language models, due to their nearly 500 times smaller size, 300 times lower GPU VRAM consumption, and several hundred times faster inference.", "date": "2025-12-27", "categories": []}
{"id": "2601.19667", "title": "SynCABEL: Synthetic Contextualized Augmentation for Biomedical Entity Linking", "abstract": "We present SynCABEL (Synthetic Contextualized Augmentation for Biomedical Entity Linking), a framework that addresses a central bottleneck in supervised biomedical entity linking (BEL): the scarcity of expert-annotated training data. SynCABEL leverages large language models to generate context-rich synthetic training examples for all candidate concepts in a target knowledge base, providing broad supervision without manual annotation. We demonstrate that SynCABEL, when combined with decoder-only models and guided inference, establishes new state-of-the-art results across three widely used multilingual benchmarks: MedMentions for English, QUAERO for French, and SPACCC for Spanish. Evaluating data efficiency, we show that SynCABEL reaches the performance of full human supervision using up to 60% less annotated data, substantially reducing reliance on labor-intensive and costly expert labeling. Finally, acknowledging that standard evaluation based on exact code matching often underestimates clinically valid predictions due to ontology redundancy, we introduce an LLM-as-a-judge protocol. This analysis reveals that SynCABEL significantly improves the rate of clinically valid predictions. Our synthetic datasets, models, and code are released to support reproducibility and future research.", "date": "2026-01-27", "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": "2605.17228", "title": "Artificial Intolerance: Stigmatizing Language in Clinical Documentation Skews Large Language Model Decision-Making", "abstract": "Large Language Models (LLMs) are increasingly deployed in high-stakes domains such as clinical decision support and medical documentation. However, the robustness of these models against subtle linguistic variations, specifically stigmatizing language (SL) commonly found in human-authored clinical notes, remains critically under-explored. In this work, we investigate whether frontier LLMs inherit and propagate this human bias when processing clinical text. We systematically evaluate nine frontier LLMs across four stigmatized medical conditions, utilizing clinical vignettes injected with varying intensities and phenotypes of SL (doubt, blame, and maligning). Our results demonstrate that all evaluated models exhibit substantial bias, with clinical decision-making significantly skewed towards less aggressive patient management. Notably, we observe a high sensitivity to linguistic framing, where a single SL sentence is sufficient to alter model outputs, revealing a clear dose-response relationship. Furthermore, we evaluate standard prompt-based mitigation strategies, including Chain-of-Thought (CoT) reasoning and model self-debiasing. These approaches show limited efficacy; models struggle to explicitly identify SL while remaining implicitly influenced by it. Our findings expose a critical vulnerability in current LLMs regarding fairness and robustness in clinical NLP, underscoring the need for rigorous algorithmic guardrails to prevent the automation of health disparities.", "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": "2605.17775", "title": "Systematic Evaluation of the Quality of Synthetic Clinical Notes Rephrased by LLMs at Million-Note Scale", "abstract": "Large language models (LLMs) can generate or synthesize clinical text for a wide range of applications, from improving clinical documentation to augmenting clinical text analytics. Yet evaluations typically focus on a narrow aspect -- such as similarity or utility comparisons -- even though these aspects are complementary and best viewed in parallel. In this study, we aim to conduct a systematic evaluation of LLM-generated clinical text, which includes intrinsic, extrinsic, and factuality evaluations of synthetic clinical notes rephrased from MIMIC databases at million-note scale. Our analysis demonstrates that synthetic notes preserve core clinical information and predictive utility for coarse-grained tasks despite substantial linguistic changes, but lose fine-grained details for task like ICD coding. We show this loss of detail can be substantially mitigated by rephrasing notes by chunks rather than by the whole note, but at the cost of reduced factual precision under incomplete context. Through fact-checking and error analysis, we further find that synthesis errors are dominated by misinterpretation of clinical context, alongside temporal confusion, measurement errors, and fabricated claims. Finally, we show that the synthetic notes -- despite their task-agnostic nature -- can effectively augment task-specific training for rare ICD codes.", "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": "2605.15467", "title": "Retrieval-Augmented Large Language Models for Schema-Constrained Clinical Information Extraction", "abstract": "Conversational nurse-patient transcripts contain actionable observations, but converting these transcripts into structured representations at scale remains challenging. Documentation burden is substantial, with prior studies showing clinicians spend large portions of their workday on documentation and related desk work rather than direct patient care. MEDIQA-SYNUR focuses on observation extraction from conversational nurse-patient transcripts, requiring systems to normalize these narratives into a predefined schema with value-type constraints. We propose a modular retrieval-augmented generation (RAG) pipeline that uses the training set as an exemplar corpus, combines schema-constrained prompting (full schema vs. pruned candidate schema), deterministic schema-based postprocessing, and a second-pass audit, with two LLM backbones: Llama-4-Scout-17B-16E-Instruct and GPT-5.2 with corresponding embedding models for RAG. Our best configuration uses GPT-5.2 with full schema, RAG, and a second-pass auditing, achieving 80.36% F1 score. Overall, our results show that RAG consistently improves performance, while the optimal degree of schema constraint depends on the model, and second-pass auditing yields modest additional gains by correcting residual schema-adherence errors.", "date": "2026-05-14", "categories": []}
{"id": "2605.15680", "title": "Few-Shot Large Language Models for Actionable Triage Categorization of Online Patient Inquiries", "abstract": "Online patient inquiries are often informal, incomplete, and written before professional assessment, yet they must still be routed to an appropriate level of clinical follow-up. We study this as a four-class actionable triage task -- self-care, schedule-visit, urgent-clinician-review, or emergency-referral, and ask whether prompted large language models (LLMs) can support such routing under low-resource labeling conditions. Using the public HealthCareMagic-100K corpus, we construct a 300-example human calibrated gold evaluation set, a 700-example auto-labeled silver training set, and a 40-example few-shot pool. We compare Term Frequency-Inverse Document Frequency (TF-IDF) and Bidirectional Encoder Representations from Transformers for Biomedical Text Mining (BioBERT) baselines train on silver labels against six prompted LLMs under 0-shot, 4-shot, and 12-shot conditions respectively. Accordingly, we evaluate with macro-$F_1$ alongside safety-aware metrics, including emergency-recall, under-triage rate, and severe under-triage rate. The strongest LLM (Claude Haiku 4.5, 12-shot) reaches macro-$F_1$ 0.475, exceeding the best supervised baseline (BioBERT, 0.378) on point estimate, with overlapping confidence intervals. Few-shot prompting and two-model agreement help in label-dependent ways: self-care agreement is reliable, urgent-clinician-review is not. We conclude that LLMs can support triage prioritization and selective human review, but not autonomous deployment.", "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": "2602.17949", "title": "CUICurate: A GraphRAG-based Framework for Automated Clinical Concept Curation for NLP applications", "abstract": "Background: Clinical named entity recognition tools commonly map free text to Unified Medical Language System (UMLS) Concept Unique Identifiers (CUIs). For many downstream tasks, however, the clinically meaningful unit is not a single CUI but a concept set comprising related synonyms, subtypes, and associated concepts. Constructing these sets is labour-intensive, inconsistently performed, and poorly supported by existing tools. Methods We present CUICurate, a graph-based retrieval-augmented generation (GraphRAG) framework for automated UMLS concept set curation. A UMLS knowledge graph (KG) was constructed and embedded for semantic retrieval. Candidate CUIs were retrieved using graph-based expansion and then filtered and classified using large language models (GPT-5 and Qwen3-32B). The framework was evaluated on five lexically heterogeneous clinical concepts against a manually curated concept sets and gold-standard concept sets. Results CUICurate produced substantially larger and more complete concept sets than the manual benchmarks. A single retrieval configuration across concepts achieved high recall of definitive concepts with manageable candidate sets. GPT-5 outperformed manual curation for all concepts and retained at least 95% of definitive gold-standard CUIs, while Qwen3-32B achieved comparable but slightly lower performance. Many missed concepts were not observed in 10,000 MIMIC-III notes. CUICurate infrastructure and end-to-end processing was inexpensive and stable across runs. Conclusions CUICurate offers a scalable, reproducible and cost-efficient approach for generating clinician-reviewable UMLS concept sets tailored to clinical natural language processing and phenotyping applications.", "date": "2026-02-20", "categories": []}
{"id": "2605.14380", "title": "Mitigating Data Scarcity in Psychological Defense Classification with Context-Aware Synthetic Augmentation", "abstract": "Psychological defense mechanisms (PDMs) are unconscious cognitive processes that modulate how individuals perceive and respond to emotional distress. Automatically classifying PDMs from text is clinically valuable but severely hindered by data scarcity and class imbalance, challenges which generative augmentation alone cannot resolve without psychological grounding. In this work, we address these challenges in the PsyDefDetect shared task (BioNLP@ACL 2026) by proposing a context-aware synthetic augmentation framework combined with a hybrid classification model. Our hybrid model integrates contextual language representations with basic clinical features, along with 150 annotated defense items. Experiments demonstrate that definition quality in prompting directly governs generation fidelity and downstream performance. Our method surpasses DMRS Co-Pilot, reaching an accuracy of 58.26% (+40.25%) and a macro-F1 of 24.62% (+15.99%), thereby establishing a strong baseline for psychologically grounded defense mechanism classification in low-resource settings. Source code is available at: https://github.com/htdgv/CASA-PDC.", "date": "2026-05-14", "categories": []}
{"id": "2605.14543", "title": "RxEval: A Prescription-Level Benchmark for Evaluating LLM Medication Recommendation", "abstract": "Inpatient medication recommendation requires clinicians to repeatedly select specific medications, doses, and routes as a patient's condition evolves. Existing benchmarks formulate this task as admission-level prediction over coarse drug codes with multi-hot diagnostic and procedure code inputs, failing to capture the per-timepoint, information-rich nature of real prescribing. We propose RxEval, a prescription-level benchmark that evaluates LLM prescribing capability by multiple-choice questions: each question presents a detailed patient profile and time-ordered clinical trajectory, requiring selection of specific medication-dose-route triples from real prescriptions and patient-specific distractors generated via reasoning-chain perturbation. RxEval comprises 1,547 questions spanning 584 patients, 18 diagnostic categories, and 969 unique medications. Evaluation of 16 LLMs shows that RxEval is both challenging and discriminative: F1 ranges from 45.18 to 77.10 across models, and the best Exact Match is only 46.10%. Error analysis reveals that even frontier models may overlook stated patient information and fail to derive clinical conclusions.", "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": "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": "2507.15867", "title": "RDMA: Cost Effective Agent-Driven Rare Disease Mining from Electronic Health Records", "abstract": "Rare diseases affect 1 in 10 Americans yet remain systematically underdocumented in clinical records. ICD-based systems cannot capture their breadth, over 50\\% of Orphanet codes lack a direct ICD mapping and only 2.2\\% of HPO codes have matching ICD codes, leaving patient populations invisible and delaying diagnosis. Mining unstructured clinical notes offers a direct path forward, but real notes are long, noisy, and abbreviation-dense, and limited annotations make fine-tuning infeasible, demanding approaches that generalize without task-specific training. We present Rare Disease Mining Agents (RDMA), an agentic framework equipping smaller quantized LLMs with tools for abbreviation resolution, implicit phenotype reasoning, and ontology grounding against Orphanet and HPO. RDMA substantially outperforms fine-tuned and RAG-based baselines across benchmarks with different data characteristics, without any task-specific training. A small quantized model achieves maximal performance, reducing inference costs by up to 10x and local hardware costs by up to 17x, enabling private deployment on standard hardware without cloud-based PHI exposure. RDMA's uncertainty-flagging mechanism further reduces expert annotation burden while preserving agreement quality, supporting scalable rare disease documentation in clinical practice. Available at https://github.com/jhnwu3/RDMA.", "date": "2025-07-14", "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": "2605.13046", "title": "An Agentic LLM-Based Framework for Population-Scale Mental Health Screening", "abstract": "Mental health disorders affect millions worldwide, and healthcare systems are increasingly overwhelmed by the volume of clinical data generated from electronic records, telemedicine platforms, and population-level screening programs. At the same time, the emergence of novel AI-based approaches in healthcare calls for intelligent frameworks capable of processing domain-specific unstructured clinical information while adapting to patient-specific needs. This paper proposes an agentic framework for building robust LLM-based pipelines, where each stage is encapsulated as a LangChain agent governed by explicit policies and proxy-guided evaluation. Stages are incrementally locked once validated, ensuring that later adaptations cannot overwrite configurations without demonstrated improvement. The proposed framework evolves from feature-level exploration, through proxy-based tuning and freeze/rollback mechanisms, to full orchestration by an Orchestrator Agent that coordinates preprocessing, retrieval, selection, diversity, threshold optimization, and decoding. A proof-of-concept in transcript-based depression detection demonstrates that the framework converges to stable configurations, such as cosine similarity, dynamic Top-k, and threshold 0.75, while controlling evaluation costs and avoiding regressions. These results highlight the potential of agentic AI to enable population-level mental health screening over large clinical datasets, addressing critical challenges in trustworthiness, reproducibility, and adaptability required in healthcare environments.", "date": "2026-05-13", "categories": []}
{"id": "2605.13542", "title": "RealICU: Do LLM Agents Understand Long-Context ICU Data? A Benchmark Beyond Behavior Imitation", "abstract": "Intensive care units (ICU) generate long, dense and evolving streams of clinical information, where physicians must repeatedly reassess patient states under time pressure, underscoring a clear need for reliable AI decision support. Existing ICU benchmarks typically treat historical clinician actions as ground truth. However, these actions are made under incomplete information and limited temporal context of the underlying patient state, and may therefore be suboptimal, making it difficult to assess the true reasoning capabilities of AI systems. We introduce RealICU, a hindsight-annotated benchmark for evaluating large language models (LLMs) under realistic ICU conditions, where labels are created after senior physicians review the full patient trajectory. We formulate four physician-motivated tasks: assess Patient Status, Acute Problems, Recommended Actions, and Red Flag actions that risk unsafe outcomes. We partition each trajectory with 30-min windows and release two datasets: RealICU-Gold with 930-window annotations from 94 MIMIC-IV patients, and RealICU-Scale with 11,862 windows extended by Oracle, a physician-validated LLM hindsight labeler. Existing LLMs including memory-augmented ones performed poorly on RealICU, exposing two failure modes: a recall-safety tradeoff for clinical recommendations, and an anchoring bias to early interpretations of the patient. We further introduce ICU-Evo to study structured-memory agents that improves long-horizon reasoning but does not fully eliminate safety failures. Together, RealICU provides a clinically grounded testbed for measuring and improving AI sequential decision-support in high-stakes care. Project page: https://chengzhi-leo.github.io/RealICU-Bench/", "date": "2026-05-13", "categories": []}
{"id": "2504.12326", "title": "Reconstructing Sepsis Trajectories from Clinical Case Reports using LLMs: the Textual Time Series Corpus for Sepsis", "abstract": "Clinical case reports and discharge summaries may be the most complete and accurate summarization of patient encounters, yet they are finalized, i.e., timestamped after the encounter. Complementary structured data streams become available sooner but suffer from incompleteness. To train models and algorithms on more complete and temporally fine-grained data, we construct a pipeline to phenotype, extract, and annotate time-localized findings within case reports using large language models. We apply our pipeline to generate an open-access textual time series corpus for Sepsis-3 comprising 2,139 case reports from the PubMed-Open Access (PMOA) Subset. To validate our system, we apply it to PMOA and timeline annotations from i2b2/MIMIC-IV and compare the results to physician-expert annotations. We show high recovery rates of clinical findings (event match rates: GPT-5--0.93, Llama 3.3 70B Instruct--0.76) and strong temporal ordering (concordance: GPT-5--0.965, Llama 3.3 70B Instruct--0.908). Our work characterizes the ability of LLMs to time-localize clinical findings in text, illustrating the limitations of LLM use for temporal reconstruction and providing several potential avenues of improvement via multimodal integration.", "date": "2025-04-12", "categories": []}
{"id": "2601.03627", "title": "Evaluating the Pre-Consultation Ability of LLMs using Diagnostic Guidelines", "abstract": "We introduce EPAG, a benchmark dataset and framework designed for Evaluating the Pre-consultation Ability of LLMs using diagnostic Guidelines. LLMs are evaluated directly through HPI-diagnostic guideline comparison and indirectly through disease diagnosis. In our experiments, we observe that small open-source models fine-tuned with a well-curated, task-specific dataset can outperform frontier LLMs in pre-consultation. Additionally, we find that increased amount of HPI (History of Present Illness) does not necessarily lead to improved diagnostic performance. Further experiments reveal that the language of pre-consultation influences the characteristics of the dialogue. By open-sourcing our dataset and evaluation pipeline on https://github.com/seemdog/EPAG, we aim to contribute to the evaluation and further development of LLM applications in real-world clinical settings.", "date": "2026-01-07", "categories": []}
{"id": "2605.09769", "title": "UTS at PsyDefDetect: Multi-Agent Councils and Absence-Based Reasoning for Defense Mechanism Classification", "abstract": "This paper describes our system for classifying psychological defense mechanisms in emotional support dialogues using the Defense Mechanism Rating Scales (DMRS), placing second (F1 0.406) among 64 teams. A central insight is that defense mechanisms are defined by what is absent: missing affect, blocked cognition, denied reality. We encode this as an affect-cognition integration spectrum in prompt-level clinical rules, which account for the largest single gain (+11.4pp F1). Our architecture is a multi-phase deliberative council of Gemini 2.5 agents where class-specific advocates rate evidence strength rather than voting, achieving F1 0.382 with no fine-tuning - a top-5 result on its own. We find, however, that the council is confidently wrong about minority classes: 59-80% of stable minority predictions are incorrect, driven by a systematic \"L7 attractor\" in which emotional content defaults to the majority class. A targeted override ensemble from three fine-tuned Qwen3.5 models applies 16 overrides (+2.4pp), selected by a structured multi-agent system (builder, critic, regression guard) that produced a larger F1 gain in one iteration than 8 prior attempts combined.", "date": "2026-05-10", "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": "2605.11398", "title": "AcuityBench: Evaluating Clinical Acuity Identification and Uncertainty Alignment", "abstract": "We introduce AcuityBench, a benchmark for evaluating whether language models identify the appropriate urgency of care from user medical presentations. Existing health benchmarks emphasize medical question answering, broad health interactions, or narrow workflow-specific triage tasks, but they do not offer a unified evaluation of acuity identification across these settings. AcuityBench addresses this gap by harmonizing five public datasets spanning user conversations, online forum posts, clinical vignettes, and patient portal messages under a shared four-level acuity framework ranging from home monitoring to immediate emergency care. The benchmark contains 914 cases, including 697 consensus cases for standard accuracy evaluation and 217 physician-confirmed ambiguous cases for uncertainty-aware evaluation. It supports two complementary task formats: explicit four-way classification in a QA setting, and free-form conversational responses evaluated with a rubric-based judge anchored to the same framework. Across 12 frontier proprietary and open-weight models, we find substantial variation in clear-case acuity accuracy and error direction. Comparing task formats reveals a systematic tradeoff: conversational responses reduce over-triage but increase under-triage relative to QA, especially in higher-acuity cases. In ambiguous cases, no model closely matches the distribution of physician judgments, and model predictions are more concentrated than expert clinical uncertainty. We also compare expert and model adjudication on a subset of maximally ambiguous cases, using those cases to examine the role of clinical uncertainty in label disagreement. Together, these results position acuity identification as a distinct safety-critical capability and show that AcuityBench enables systematic comparison and stress-testing of how well models guide users to the right level of care in real-world health use.", "date": "2026-05-12", "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": "2605.08482", "title": "ShifaMind: A Multiplicative Concept Bottleneck for Interpretable ICD-10 Coding", "abstract": "Automated ICD-10 coding from clinical discharge summaries requires models that are both accurate on long-tailed multi-label classification tasks and interpretable to clinicians. Concept Bottleneck Models (CBMs) offer a principled framework for interpretability by routing predictions through human-interpretable concepts, but this transparency often comes at a cost: compressing rich clinical text representations into a narrow concept layer can restrict gradient flow and limit predictive capacity. We present ShifaMind, a concept-grounded architecture built around a Multiplicative Concept Bottleneck (MCB), which changes the form, rather than the width, of the bottleneck. Instead of projecting through a narrow concept layer, ShifaMind uses a learned multiplicative gate over a concept-grounded representation while retaining a scalar concept interface for inspection. On MIMIC-IV top-50 ICD-10 coding, ShifaMind achieves performance competitive with LAAT, the strongest baseline, across F1, AUC, and ranking metrics, while outperforming five additional ICD-coding baselines and providing concept-mediated explanations. Its substantial gains over a capacity-matched Vanilla CBM in both predictive performance and interpretability-oriented metrics highlight the importance of the bottleneck design.", "date": "2026-05-08", "categories": []}
{"id": "2605.09440", "title": "Key Coverage Matters: Semi-Structured Extraction of OCR Clinical Reports", "abstract": "Clinical reports are often fragmented across healthcare institutions because privacy regulations and data silos limit direct information sharing. When patients seek care at a different hospital, they often carry paper or scanned reports from prior visits. This hinders EHR integration and longitudinal review, and downstream applications that depend on more complete patient records, such as patient management, follow-up care, real-world studies, and clinical-trial matching. Although OCR can digitize such reports, reliable extraction remains challenging because clinical documents are heterogeneous, OCR text is noisy, and many healthcare settings require low-cost on-premise deployment. We formulate this problem as canonical key-conditioned extractive question answering over OCR-derived clinical reports. Because the key fields are neither fixed nor known in advance, the key space is open. We maintain a canonical key inventory through iterative key mining, normalization, clustering, and lightweight human verification, and introduce key coverage as a metric to quantify inventory completeness. Using a 0.2B BERT-based model, experiments on real-world reports from more than 20 hospitals show performance improves monotonically with key coverage. The model achieves F1 scores of 0.839 and 0.893 under exact match and boundary-tolerant matching, respectively, once the Top-90 canonical keys are covered. These results show that key coverage is a dominant factor for end-to-end performance. At Top-90 coverage, our model outperforms a fine-tuned Qwen3-0.6B baseline under exact match. Although our annotated corpus is Chinese, the method relies on the language-agnostic key-value organization of semi-structured clinical reports and can be adapted to other settings given an appropriate canonical key inventory and alias mapping.", "date": "2026-05-10", "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": "2605.09838", "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": "2605.10025", "title": "Medical Incident Causal Factors and Preventive Measures Generation Using Tag-based Example Selection in Few-shot Learning", "abstract": "In high-stakes domains such as healthcare, the reliability of Large Language Models (LLMs) is critical, particularly when generating clinical insights from incident reports. This study proposes a tag-based few-shot example selection method for prompting LLMs to generate background/causal factors and preventive measures from details of the medical incidents. For our experiments, we use the Japanese Medical Incident Dataset (JMID), a structured dataset of 3,884 real-world medical accident and near-miss reports. These reports are variably annotated with a wide range of tags--some include descriptive information (e.g., \"medications,\" \"blood transfusion therapy\"). We compare three few-shot example selection strategies--random sampling, cosine similarity-based selection, and our proposed tag-based method--using GPT-4o and LLaMA 3.3. Results show that the tag-based approach achieves the highest precision and most stable generation behavior, while similarity-based selection often leads to unintended outputs and safety filter activation. These findings suggest that selecting examples based on human-interpretable dataset tags can improve generation precision and stability in clinical LLM applications.", "date": "2026-05-11", "categories": []}
{"id": "2605.10877", "title": "Neural at ArchEHR-QA 2026: One Method Fits All: Unified Prompt Optimization for Clinical QA over EHRs", "abstract": "Automated question answering (QA) over electronic health records (EHRs) demands precise evidence retrieval, faithful answer generation, and explicit grounding of answers in clinical notes. In this work, we present Neural1.5, our method for the ArchEHR-QA 2026 shared task at CL4Health@LREC 2026, which comprises four subtasks: question interpretation, evidence identification, answer generation, and evidence alignment. Our approach decouples the task into independent, modular stages and employs DSPy\"s MIPROv2 optimizer to automatically discover high-performing prompts, jointly tuning instructions and few-shot demonstrations for each stage. Within every stage, self-consistency voting over multiple stochastic inference runs suppresses spurious errors and improves reliability, while stage-specific verification mechanisms (e.g., self-reflection and chain-of-verification for alignment) further refine output quality. Among all teams that participated in all four subtasks, our method ranks second overall (mean rank 4.00), placing 4th, 1st, 4th, and 7th on Subtasks 1-4, respectively. These results demonstrate that systematic, per-stage prompt optimization combined with self-consistency mechanisms is a cost-effective alternative to model fine-tuning for multifaceted clinical QA.", "date": "2026-05-11", "categories": []}
{"id": "2605.07606", "title": "N\\\"urnberg NLP at PsyDefDetect: Multi-Axis Voter Ensembles for Psychological Defence Mechanism Classification", "abstract": "Detecting levels of psychological defence mechanisms in supportive conversations is inherently ambiguous. In the PsyDefDetect shared task at BioNLP 2026 the eight positive defence categories share surface language and differ only in pragmatic function and trained raters reach only moderate inter-annotator agreement. On such a task the decisive lever is not a stronger single model but error independence, since any single representation will waver on the overlapping defence boundaries. We translate this insight into a 9-voter ensemble spanning three orthogonal axes: class granularity (all nine classes for the gatekeeper, only the eight defence classes for the specialists), training method (generative and discriminative) and base model. The system reaches $F1_{test}{=}.420$ on the hidden test set, placing first among 21 registered teams.", "date": "2026-05-08", "categories": []}
{"id": "2605.08045", "title": "Uncertainty-Aware Structured Data Extraction from Full CMR Reports via Distilled LLMs", "abstract": "Converting free-text cardiac magnetic resonance (CMR) reports into auditable structured data remains a bottleneck for cohort assembly, longitudinal curation, and clinical decision support. We present CMR-EXTR, a lightweight framework that converts free-text CMR reports into structured data and assigns per-field confidence for quality control. A teacher-student distillation pipeline enables fully offline inference while limiting manual annotation. Uncertainty integrates three complementary principles -- distribution plausibility, sampling stability, and cross-field consistency -- to triage human review. Experiments show that CMR-EXTR achieves 99.65% variable-level accuracy, demonstrating both reliable extraction and informative confidence scores. To our knowledge, this is the first CMR-specific extraction system with integrated confidence estimation. The code is available at https://github.com/yuyi1005/CMR-EXTR.", "date": "2026-05-08", "categories": []}
{"id": "2502.16022", "title": "Enhancing LLMs for Identifying and Prioritizing Important Medical Jargons from Electronic Health Record Notes Utilizing Data Augmentation", "abstract": "OpenNotes enables patients to access EHR notes, but medical jargon can hinder comprehension. To improve understanding, we evaluated closed- and open-source LLMs for extracting and prioritizing key medical terms using prompting, fine-tuning, and data augmentation. We assessed LLMs on 106 expert-annotated EHR notes, experimenting with (i) general vs. structured prompts, (ii) zero-shot vs. few-shot prompting, (iii) fine-tuning, and (iv) data augmentation. To enhance open-source models in low-resource settings, we used ChatGPT for data augmentation and applied ranking techniques. We incrementally increased the augmented dataset size (10 to 10,000) and conducted 5-fold cross-validation, reporting F1 score and Mean Reciprocal Rank (MRR). Our result show that fine-tuning and data augmentation improved performance over other strategies. GPT-4 Turbo achieved the highest F1 (0.433), while Mistral7B with data augmentation had the highest MRR (0.746). Open-source models, when fine-tuned or augmented, outperformed closed-source models. Notably, the best F1 and MRR scores did not always align. Few-shot prompting outperformed zero-shot in vanilla models, and structured prompts yielded different preferences across models. Fine-tuning improved zero-shot performance but sometimes degraded few-shot performance. Data augmentation performed comparably or better than other methods. Our evaluation highlights the effectiveness of prompting, fine-tuning, and data augmentation in improving model performance for medical jargon extraction in low-resource scenarios.", "date": "2025-02-22", "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": "2512.20822", "title": "MediEval: A Unified Medical Benchmark for Patient-Contextual and Knowledge-Grounded Reasoning in LLMs", "abstract": "Large Language Models (LLMs) are increasingly applied to medicine, yet their adoption is limited by concerns over reliability and safety. Existing evaluations either test factual medical knowledge in isolation or assess patient-level reasoning without verifying correctness, leaving a critical gap. We introduce MediEval, a benchmark that links MIMIC-IV electronic health records (EHRs) to a unified knowledge base built from UMLS and other biomedical vocabularies. MediEval generates diverse factual and counterfactual medical statements within real patient contexts, enabling systematic evaluation across a 4-quadrant framework that jointly considers knowledge grounding and contextual consistency. Using this framework, we identify critical failure modes, including hallucinated support and truth inversion, that current proprietary, open-source, and domain-specific LLMs frequently exhibit. To address these risks, we propose Counterfactual Risk-Aware Fine-tuning (CoRFu), a DPO-based method with an asymmetric penalty targeting unsafe confusions. CoRFu improves by +16.4 macro-F1 points over the base model and eliminates truth inversion errors, demonstrating both higher accuracy and substantially greater safety.", "date": "2025-12-23", "categories": []}
{"id": "2603.09986", "title": "Quantifying Hallucinations in Language Language Models on Medical Textbooks", "abstract": "Hallucinations, the tendency for large language models to provide responses with factually incorrect and unsupported claims, is a serious problem within natural language processing for which we do not yet have an effective solution to mitigate against. Existing benchmarks for medical QA rarely evaluate this behavior against a fixed evidence source. We ask how often hallucinations occur on textbook-grounded QA and how responses to medical QA prompts vary across models. We conduct two experiments, the first experiment to determine the prevalence of hallucinations for a prominent open source large language model (LLaMA-70B-Instruct) in medical QA given closed-source zero-shot prompts, and the second experiment to determine the prevalence of hallucinations and clinician preference to model responses. We observed, in experiment one, with the passages provided, LLaMA-70B-Instruct hallucinated in 19.7\\% of answers (95\\% CI 18.6 to 20.7) even though 98.8\\% of prompt responses received maximal plausibility, and observed in experiment two, across models, lower hallucination rates aligned with higher usefulness scores ($\\rho=-0.71$, $p=0.058$). Clinicians produced high agreement (quadratic weighted $\\kappa=0.92$) and ($\\tau_b=0.06$ to $0.18$, $\\kappa=0.57$ to $0.61$) for experiments 1 and 2 respectively. Our findings indicate that, across all scales and architectures tested, current large language models remain unfit for unsupervised clinical deployment, and that human expert oversight is both necessary and the dominant cost driver.", "date": "2026-02-12", "categories": []}
{"id": "2603.15270", "title": "From Documents to Spans: Scalable Supervision for Evidence-Based ICD Coding with LLMs", "abstract": "International Classification of Diseases (ICD) coding assigns diagnosis codes to clinical documents and is essential for healthcare billing and clinical analysis. Reliable coding requires that each predicted code be supported by explicit textual evidence. However, existing public datasets provide only code labels, without evidence annotations, limiting models' ability to learn evidence-grounded predictions. In this work, we argue that dense, document-level evidence annotation is not always necessary for learning evidence-based coding. Instead, models can learn code-specific evidence patterns from local spans and use these patterns to support document-level evidence-based coding. Based on this insight, we propose Span-Centric Learning (SCL), a training framework that strengthens LLMs' coding ability at the span level and transfers this capability to full clinical documents. Specifically, we use a small set of annotated documents to supervise evidence recognition, aggregation, and code assignment, while leveraging a large collection of lightweight evidence spans to reinforce span-level reasoning. Due to their compactness, span annotations are scalable and can be further augmented through synthesis. Under the same Llama3.1-8B backbone, our approach achieves an 8.2-point improvement in macro-F1 at only 20% of the training cost of standard SFT, and provides explicit supporting evidence for each predicted code, enabling human auditing and revision.", "date": "2026-03-16", "categories": []}
{"id": "2605.06191", "title": "Systematic Evaluation of Large Language Models for Post-Discharge Clinical Action Extraction", "abstract": "The work in this paper evaluates zero-shot and few-shot large language models (LLMs) for safety-critical clinical action extraction using the CLIP discharge-note dataset, with particular emphasis on transitions of care and post-discharge patient safety. To manage the complexity of clinical documentation, we introduce a two-stage extraction framework that decomposes discharge notes, that are written in narrative form, into fine-grained, explicitly actionable clinical tasks through a staged prompting strategy. Our contributions include a systematic assessment of generative LLMs for clinical action extraction, a detailed comparison between general-purpose LLMs and task-specific supervised BERT-based models, and an analysis of annotation inconsistencies across different action categories. We show that contemporary LLMs achieve performance comparable to or exceeding supervised models on binary actionability detection, while supervised baselines retain a meaningful advantage on fine-grained multi-label category classification, despite the absence of task-specific fine-tuning and under strict data-privacy constraints. Qualitative error analysis reveals that many failures stem from misalignment between model reasoning and dataset annotation conventions, particularly in cases involving implicit clinical actions and rigid structural labeling rules. These results indicate that reported performance reflects model limitations due to lack of clinical reasoning, that is not captured by plain annotations. Labels without rationales make it impossible to distinguish clinical reasoning failures from annotation convention mismatches. Advancing clinical NLP requires reasoning-annotated datasets that document why specific spans are actionable, not merely which spans were labeled, enabling proper evaluation of model clinical understanding.", "date": "2026-05-07", "categories": []}
{"id": "2605.06594", "title": "Automated Clinical Report Generation for Remote Cognitive Remediation: Comparing Knowledge-Engineered Templates and LLMs in Low-Resource Settings", "abstract": "The growing demand for cognitive remediation therapy, combined with limited speech therapist availability, has accelerated the adoption of remote rehabilitation tools. These systems generate large volumes of interaction data that are difficult for clinicians to review efficiently. This paper investigates automated clinical report generation for avatar-guided, home-based cognitive remediation sessions in a low-resource setting with no reference reports. We present and compare two approaches: (1) a rule-based template system encoding speech therapy domain knowledge as explicit decision rules and validated templates, ensuring clinical reliability and traceability; and (2) a zero-shot LLM-based approach (GPT-4) aimed at more fluent and concise output. Both systems use identical pre-extracted, expert-validated structured variables, enabling a controlled factual comparison. Outputs were evaluated by eight speech therapists and final-year students using a nine-criterion questionnaire. Results reveal a clear trade-off between clinical reliability and linguistic quality. The template-based system scored higher on fluidity, coherence, and results presentation, while GPT-4 produced more concise output. Directional differences are consistent across evaluation dimensions, though no comparison reached statistical significance after correction, reflecting the scale constraints of expert clinical evaluation. Based on evaluator feedback, we derive eight design recommendations for clinical reporting systems in remote rehabilitation settings. More broadly, this work contributes a replicable methodology combining expert elicitation, taxonomy-driven generation, and multi-dimensional human evaluation for clinical NLG in low-resource settings, and illustrates how controlled comparisons can inform the responsible adoption of generative AI in healthcare.", "date": "2026-05-07", "categories": []}
{"id": "2512.20074", "title": "Reason2Decide: Rationale-Driven Multi-Task Learning", "abstract": "Despite the wide adoption of Large Language Models (LLM)s, clinical decision support systems face a critical challenge: achieving high predictive accuracy while generating explanations aligned with the predictions. Current approaches suffer from exposure bias leading to misaligned explanations. We propose Reason2Decide, a two-stage training framework that addresses key challenges in self-rationalization, including exposure bias and task separation. In Stage-1, our model is trained on rationale generation, while in Stage-2, we jointly train on label prediction and rationale generation, applying scheduled sampling to gradually transition from conditioning on gold labels to model predictions. We evaluate Reason2Decide on three medical datasets, including a proprietary triage dataset and public biomedical QA datasets. Across model sizes, Reason2Decide outperforms other fine-tuning baselines and some zero-shot LLMs in prediction (F1) and rationale fidelity (BERTScore, BLEU, LLM-as-a-Judge). In triage, Reason2Decide is rationale source-robust across LLM-generated, nurse-authored, and nurse-post-processed rationales. In our experiments, while using only LLM-generated rationales in Stage-1, Reason2Decide outperforms other fine-tuning variants. This indicates that LLM-generated rationales are suitable for pretraining models, reducing reliance on human annotations. Remarkably, Reason2Decide achieves these gains with models 40x smaller than contemporary foundation models, making clinical reasoning more accessible for resource-constrained deployments while still providing explainable decision support.", "date": "2025-12-23", "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": "2605.03212", "title": "ADAPTS: Agentic Decomposition for Automated Protocol-agnostic Tracking of Symptoms", "abstract": "Modeling latent clinical constructs from unconstrained clinical interactions is a unique challenge in affective computing. We present ADAPTS (Agentic Decomposition for Automated Protocol-agnostic Tracking of Symptoms), a framework for automated rating of depression and anxiety severity using a mixture-of-agents LLM architecture. This approach decomposes long-form clinical interviews into symptom-specific reasoning tasks, producing auditable justifications while preserving temporal and speaker alignment. Generalization was evaluated across two independent datasets ($N=204$) with distinct interview structures. On high-discrepancy interviews, automated ratings approximated expert benchmarks ($\\text{absolute error}=22$) more closely than original human ratings ($\\text{absolute error}=26$). Implementing an ``extended'' protocol that incorporates qualitative clinical conventions significantly stabilized ratings, with absolute agreement reaching $\\text{ICC(2,1)} = 0.877$. These findings suggest that the ADAPTS framework enables promising evaluations of psychiatric severity. While the current implementation is purely text-based, the underlying architecture is readily extensible to multimodal inputs, including acoustic and visual features. By approximating expert-level precision in a protocol-agnostic manner, this framework provides a foundation for objective and scalable psychiatric assessment, especially in resource-limited settings.", "date": "2026-05-04", "categories": []}
{"id": "2605.03103", "title": "MedStruct-S: A Benchmark for Key Discovery, Key-Conditioned QA and Semi-Structured Extraction from OCR Clinical Reports", "abstract": "Semi-structured information extraction (IE) from OCR-derived clinical reports is crucial for efficiently reconstructing patients' longitudinal medical histories. In practice, this scenario commonly involves three tasks: (i) field-header (key) discovery, (ii) key-conditioned question answering (QA), and (iii) end-to-end key-value pair extraction. However, existing evaluations often under-model two factors: heterogeneous and incompletely known key representations, and OCR-induced noise. This makes it difficult to assess model robustness in real-world settings.\n  We present MedStruct-S, a benchmark specifically designed to evaluate these tasks under unknown keys and OCR noise. MedStruct-S contains 3,582 annotated real-world clinical report pages. Using MedStruct-S, we benchmark two representative paradigms: encoder-only sequence labeling with post-processing and decoder-only structured generation, covering four encoder-only and five decoder-only models spanning 0.11B to 103B parameters. Our results show that encoder-only models achieve the best performance for non-null-value key-conditioned QA despite being substantially smaller than decoder-only models. When comparing models of similar order of magnitude, encoder-only models still perform better overall. Without controlling for model scale, fine-tuned decoder-only models deliver the strongest overall results. These findings show that the benchmark provides a reliable and practical basis for selecting and comparing models across different semi-structured IE settings.", "date": "2026-05-04", "categories": []}
{"id": "2605.03301", "title": "SHIELD: A Diverse Clinical Note Dataset and Distilled Small Language Models for Enterprise-Scale De-identification", "abstract": "De-identification of clinical text remains essential for secondary use of electronic health records (EHRs), yet public benchmarks such as i2b2 2006/2014 are over a decade old and lack the semantic and demographic diversity of modern narratives. While Large Language Models (LLMs) achieve state-of-the-art zero-shot extraction, enterprise deployment is hindered by compute costs and governance restricting Protected Health Information (PHI) from cloud APIs. We introduce SHIELD (Synthetic Human-annotated Identifier-replaced Entries for Learning and De-identification), a diverse dataset of 1,394 notes with 10,505 gold-standard PHI spans across 9 categories, built via set-cover diversity sampling with human-in-the-loop adjudication. We evaluate four LLMs (two proprietary, two open-weight) to establish a performance ceiling, then distill these capabilities into locally deployable Small Language Models (SLMs). Distributional analysis using Frechet Text Distance and Jensen-Shannon Divergence confirms SHIELD occupies a distinct region of biomedical embedding and vocabulary space versus legacy benchmarks. Our best distilled model matches its teacher on structured PHI categories (DATE, DOCTOR, ID, PATIENT, PHONE) and achieves micro-averaged span-level precision of 0.88 and recall of 0.86 on standard workstation hardware. Cross-dataset evaluation shows diversity-trained models generalize well on universal structured PHI, while institution-specific entities remain hard to transfer, suggesting optimal deployment combines broad-coverage models with specialized models for high-volume notes. We publicly release the SHIELD dataset and the distilled DeBERTa v3 model.", "date": "2026-05-05", "categories": []}
{"id": "2605.03476", "title": "CuraView: A Multi-Agent Framework for Medical Hallucination Detection with GraphRAG-Enhanced Knowledge Verification", "abstract": "Discharge summaries require extracting critical information from lengthy electronic health records (EHRs), a process that is labor-intensive when performed manually. Large language models (LLMs) can improve generation efficiency; however, they are prone to producing faithfulness hallucinations, statements that contradict source records, posing direct risks to patient safety. To address this, we present CuraView, a multi-agent framework for sentence-level detection and evidence-grounded explanation of faithfulness hallucinations in discharge summaries. CuraView constructs a GraphRAG-based knowledge graph from patient-level EHRs and implements a closed-loop generation-detection pipeline with sentence-level evidence retrieval and classification spanning four evidence grades from strong support to direct contradiction (E1-E4), yielding structured and interpretable evidence chains.\n  We evaluate CuraView on a subset of 250 patients from the Discharge-Me benchmark, with 50 patients held out for testing. Our fine-tuned Qwen3-14B detection model achieves an F1 of 0.831 on the safety-critical E4 metric (90.9% recall, 76.5% precision) and an F1 of 0.823 on E3+E4, representing a 50.0% relative improvement over the base model and outperforming RAGTruth-style and QAGS-style baselines. These results demonstrate that evidence-chain-based graph retrieval verification substantially improves the factual reliability of clinical documentation, while simultaneously producing reusable annotated datasets for downstream model training and distillation.", "date": "2026-05-05", "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": "2605.04039", "title": "Safety and accuracy follow different scaling laws in clinical large language models", "abstract": "Clinical LLMs are often scaled by increasing model size, context length, retrieval complexity, or inference-time compute, with the implicit expectation that higher accuracy implies safer behavior. This assumption is incomplete in medicine, where a few confident, high-risk, or evidence-contradicting errors can matter more than average benchmark performance. We introduce SaFE-Scale, a framework for measuring how clinical LLM safety changes across model scale, evidence quality, retrieval strategy, context exposure, and inference-time compute. To instantiate this framework, we introduce RadSaFE-200, a Radiology Safety-Focused Evaluation benchmark of 200 multiple-choice questions with clinician-defined clean evidence, conflict evidence, and option-level labels for high-risk error, unsafe answer, and evidence contradiction. We evaluated 34 locally deployed LLMs across six deployment conditions: closed-book prompting (zero-shot), clean evidence, conflict evidence, standard RAG, agentic RAG, and max-context prompting. Clean evidence produced the strongest improvement, increasing mean accuracy from 73.5% to 94.1%, while reducing high-risk error from 12.0% to 2.6%, contradiction from 12.7% to 2.3%, and dangerous overconfidence from 8.0% to 1.6%. Standard RAG and agentic RAG did not reproduce this safety profile: agentic RAG improved accuracy over standard RAG and reduced contradiction, but high-risk error and dangerous overconfidence remained elevated. Max-context prompting increased latency without closing the safety gap, and additional inference-time compute produced only limited gains. Worst-case analysis showed that clinically consequential errors concentrated in a small subset of questions. Clinical LLM safety is therefore not a passive consequence of scaling, but a deployment property shaped by evidence quality, retrieval design, context construction, and collective failure behavior.", "date": "2026-05-05", "categories": []}
{"id": "2605.00421", "title": "RadLite: Multi-Task LoRA Fine-Tuning of Small Language Models for CPU-Deployable Radiology AI", "abstract": "Large language models (LLMs) show promise in radiology but their deployment is limited by computational requirements that preclude use in resource-constrained clinical environments. We investigate whether small language models (SLMs) of 3-4 billion parameters can achieve strong multi-task radiology performance through LoRA fine-tuning, enabling deployment on consumer-grade CPUs. We train Qwen2.5-3B-Instruct and Qwen3-4B on 162K samples spanning 9 radiology tasks - RADS classification across 10 systems, impression generation, temporal comparison, radiology NLI, NER, abnormality detection, N/M staging, and radiology Q&A - compiled from 12 public datasets. Both models are evaluated on up to 500 held-out test samples per task with standardized metrics. Our key findings are: (1) LoRA fine-tuning dramatically improves performance over zero-shot baselines (RADS accuracy +53%, NLI +60%, N-staging +89%); (2) the two models exhibit complementary strengths - Qwen2.5 excels at structured generation tasks while Qwen3 dominates extractive tasks; (3) a task-outed oracle ensemble combining both models achieves the best performance across all tasks; (4) few-shot prompting with fine-tuned models hurts performance, demonstrating that LoRA adaptation is more effective than in-context learning for specialized domains; and (5) models can be quantized to GGUF format (~1.8-2.4GB) for CPU deployment at 4-8 tokens/second on consumer hardware. Our work demonstrates that small, efficiently fine-tuned models - which we collectively call RadLite - can serve as practical multi-task radiology AI assistants deployable entirely on consumer hardware without GPU requirements. Code and models are available at https://github.com/RadioX-Labs/RadLite", "date": "2026-05-01", "categories": []}
{"id": "2605.01077", "title": "Teaching LLMs Brazilian Healthcare: Injecting Knowledge from Official Clinical Guidelines", "abstract": "Brazil's Unified Health System (SUS) relies on official clinical guidelines that define diagnostic criteria, treatments, dosages, and monitoring procedures for over 200 million citizens. Yet current LLMs perform poorly on this guideline-specific knowledge, and no benchmark evaluates clinical recall grounded in Brazilian Portuguese protocols. We address this gap by adapting Qwen2.5-14B-Instruct to the Brazilian clinical domain. From 178 official guidelines (~5.4M tokens), we generate ~70M tokens of synthetic data in three formats -- rephrases, wiki-style articles, and question-answer pairs -- using four generator LLMs. We then apply continual pre-training followed by Group Relative Policy Optimization (GRPO). We introduce HealthBench-BR, with 1,780 balanced true/false clinical assertions, and PCDT-QA, with 890 open-ended clinical questions scored by an LLM judge. Our best model achieves 83.9% on HealthBench-BR and 85.4% on PCDT-QA, outperforming GPT-5.2, Claude Sonnet 4.6, Gemini 3.1 Pro, and Google AI Overview's web-grounded RAG despite having only 14B parameters. Ablations show that generator diversity and reinforcement learning are critical to these gains. We release all datasets, benchmarks, and model weights to support reproducible clinical NLP research for Brazilian Portuguese. Code, data, and model weights are available at https://github.com/hugoabonizio/clinical-protocols-br", "date": "2026-05-01", "categories": []}
{"id": "2605.01474", "title": "ReMedi: Reasoner for Medical Clinical Prediction", "abstract": "Predicting future clinical outcomes from electronic health records (EHR) remains challenging due to the complexity and heterogeneity of patient data. LLMs have shown strong potential for such predictive tasks, yet existing approaches mainly focus on enhancing medical knowledge through distillation or RAG while relying on the model's internal ability to interpret contextual information. In this work, we present ReMedi (Reasoner for Medical Clinical Prediction), a framework for improving clinical outcome prediction from EHR. ReMedi generates rationale-answer pairs using a challenging sample regeneration mechanism for complex clinical questions, which leverages ground-truth answers as hints to enhance reasoning for further fine-tuning and preference tuning. ReMedi integrates ground-truth outcome guidance into the preference data construction loop, regenerating rationale-answer variants. By tuning on these rationale-answer pairs, the model improves its predictive performance. Experiments on multiple EHR prediction tasks demonstrate substantial gains of up to 19.9 percent over state-of-the-art baselines in terms of F1 score, underscoring ReMedi's effectiveness in real-world clinical prediction.", "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": "2602.00074", "title": "Adoption and Use of LLMs at an Academic Medical Center", "abstract": "While large language models (LLMs) can support clinical documentation needs, standalone tools struggle with \"workflow friction\" from manual data entry. We developed ChatEHR, a system that enables the use of LLMs with the entire patient timeline spanning several years. ChatEHR enables automations - which are static combinations of prompts and data that perform a fixed task - and interactive use in the electronic health record (EHR) via a user interface (UI). The resulting ability to sift through patient medical records for diverse use-cases such as pre-visit chart review, screening for transfer eligibility, monitoring for surgical site infections, and chart abstraction, redefines LLM use as an institutional capability. This system, accessible after user-training, enables continuous monitoring and evaluation of LLM use.\n  In 1.5 years, we built 7 automations and 1075 users have trained to become routine users of the UI, engaging in 23,000 sessions in the first 3 months of launch. For automations, being model-agnostic and accessing multiple types of data was essential for matching specific clinical or administrative tasks with the most appropriate LLM. Benchmark-based evaluations proved insufficient for monitoring and evaluation of the UI, requiring new methods to monitor performance. Generation of summaries was the most frequent task in the UI, with an estimated 0.73 hallucinations and 1.60 inaccuracies per generation. The resulting mix of cost savings, time savings, and revenue growth required a value assessment framework to prioritize work as well as quantify the impact of using LLMs. Initial estimates are $6M savings in the first year of use, without quantifying the benefit of the better care offered. Such a \"build-from-within\" strategy provides an opportunity for health systems to maintain agency via a vendor-agnostic, internally governed LLM platform.", "date": "2026-01-21", "categories": []}
{"id": "2605.00336", "title": "Budget-Aware Routing for Long Clinical Text", "abstract": "A key challenge for large language models is token cost per query and overall deployment cost. Clinical inputs are long, heterogeneous, and often redundant, while downstream tasks are short and high stakes. We study budgeted context selection, where a subset of document units is chosen under a strict token budget so an off-the-shelf generator can meet fixed cost and latency constraints. We cast this as a knapsack-constrained subset selection problem with two design choices, unitization that defines document segmentation and selection that determines which units are kept.\n  We propose \\textbf{RCD}, a monotone submodular objective that balances relevance, coverage, and diversity. We compare sentence, section, window, and cluster-based unitization, and introduce a routing heuristic that adapts to the budget regime. Experiments on MIMIC discharge notes, Cochrane abstracts, and L-Eval show that optimal strategies depend on the evaluation setting. Positional heuristics perform best at low budgets in extractive tasks, while diversity-aware methods such as MMR improve LLM generation. Selector choice matters more than unitization, with cluster-based grouping reducing performance and other schemes behaving similarly. ROUGE saturates for LLM summaries, while BERTScore better reflects quality differences. We release our code at https://github.com/stone-technologies/ACL_budget_paper.", "date": "2026-05-01", "categories": []}
{"id": "2604.27045", "title": "Detecting Clinical Discrepancies in Health Coaching Agents: A Dual-Stream Memory and Reconciliation Architecture", "abstract": "As Large Language Model (LLM) agents transition from single-session tools to persistent systems managing longitudinal healthcare journeys, their memory architectures face a critical challenge: reconciling two imperfect sources of truth. The patient's evolving self-report is current but prone to recall bias, while the Electronic Health Record (EHR) is medically validated but frequently stale. General-purpose agent memory systems optimize for coherence by overwriting older facts with the user's latest statement, a pattern that risks safety failures when applied to clinical data. We introduce a Dual-Stream Memory Architecture that strictly separates the patient narrative from the structured clinical record (FHIR), governed by a dedicated Reconciliation Engine that evaluates every extracted memory against the patient's FHIR profile and classifies discrepancies by type, severity, and the specific FHIR resources involved. We evaluate this architecture on 26 patients across 675 longitudinal wellness coaching sessions, using a hybrid dataset that interleaves real provider-patient transcripts with synthetic, FHIR-grounded clinical scenarios. In isolated testing, the engine detects 84.4% of designed clinical discrepancies with 86.7% safety-critical recall. By coupling extraction and reconciliation evaluation on the same data, we directly quantify a 13.6% error cascade, tracing the degradation to clinical details lost during memory extraction from unstructured conversation rather than to downstream classification errors. These findings establish that validating patient-reported memories against clinical records is both feasible and necessary for safe deployment of longitudinal health agents.", "date": "2026-04-29", "categories": []}
{"id": "2505.21190", "title": "Lunguage: A Benchmark for Structured and Sequential Chest X-ray Interpretation", "abstract": "Radiology reports convey detailed clinical observations and capture diagnostic reasoning that evolves over time. However, existing evaluation methods are limited to single-report settings and rely on coarse metrics that fail to capture fine-grained clinical semantics and temporal dependencies. We introduce LUNGUAGE, a benchmark dataset for structured radiology report generation that supports both single-report evaluation and longitudinal patient-level assessment across multiple studies. It contains 1,473 annotated chest X-ray reports, each reviewed by experts, and 186 of them contain longitudinal annotations to capture disease progression and inter-study intervals, also reviewed by experts. Using this benchmark, we develop a two-stage structuring framework that transforms generated reports into fine-grained, schema-aligned structured reports, enabling longitudinal interpretation. We also propose LUNGUAGESCORE, an interpretable metric that compares structured outputs at the entity, relation, and attribute level while modeling temporal consistency across patient timelines. These contributions establish the first benchmark dataset, structuring framework, and evaluation metric for sequential radiology reporting, with empirical results demonstrating that LUNGUAGESCORE effectively supports structured report evaluation. The code is available at: https://github.com/SuperSupermoon/Lunguage", "date": "2025-05-27", "categories": []}
{"id": "2601.03423", "title": "Training-Free Adaptation of New-Generation LLMs using Legacy Clinical Models", "abstract": "Adapting language models to the clinical domain through continued pretraining and instruction tuning requires costly retraining for each new model generation. We propose Cross-Architecture Proxy Tuning (CAPT), a model-ensembling approach that enables training-free adaptation of state-of-the-art general-domain models using existing clinical models. CAPT supports models with disjoint vocabularies, leveraging contrastive decoding to selectively inject clinically relevant signals while preserving the general-domain model's reasoning and fluency. On six clinical classification and text-generation tasks, CAPT with a new-generation general-domain model and an older-generation clinical model consistently outperforms both models individually and state-of-the-art ensembling approaches (average +17.6\\% over UniTE, +41.4\\% over proxy tuning across tasks). Through token-level analysis and physician case studies, we demonstrate that CAPT amplifies clinically actionable language, reduces context errors, and increases clinical specificity. This technique especially benefits healthcare institutions with constrained computational capacity that cannot support iterative clinical training and want to adopt emerging general-domain model advances.", "date": "2026-01-06", "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": "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": "2604.26880", "title": "HealthNLP_Retrievers at ArchEHR-QA 2026: Cascaded LLM Pipeline for Grounded Clinical Question Answering", "abstract": "Patient portals now give individuals direct access to their electronic health records (EHRs), yet access alone does not ensure patients understand or act on the complex clinical information contained in these records. The ArchEHR-QA 2026 shared task addresses this challenge by focusing on grounded question answering over EHRs, and this paper presents the system developed by the HealthNLP_Retrievers team for this task. The proposed approach uses a multi-stage cascaded pipeline powered by the Gemini 2.5 Pro large language model to interpret patient-authored questions and retrieve relevant evidence from lengthy clinical notes. Our architecture comprises four integrated modules: (1) a few-shot query reformulation unit which summarizes verbose patient queries; (2) a heuristic-based evidence scorer which ranks clinical sentences to prioritize recall; (3) a grounded response generator which synthesizes professional-caliber answers restricted strictly to identified evidence; and (4) a high-precision many-to-many alignment framework which links generated answers to supporting clinical sentences. This cascaded approach achieved competitive results. Across the individual tracks, the system ranked 1st in question interpretation, 5th in answer generation, 7th in evidence identification, and 9th in answer-evidence alignment. These results show that integrating large language models within a structured multi-stage pipeline improves grounding, precision, and the professional quality of patient-oriented health communication. To support reproducibility, our source code is publicly available in our GitHub repository", "date": "2026-04-29", "categories": []}
{"id": "2604.25374", "title": "Language corpora for the Dutch medical domain", "abstract": "\\textbf{Background:} Dutch medical corpora are scarce, limiting NLP development. \\\\ \\textbf{Methods:} We translated English datasets, identified medical text in generic corpora, and extracted open Dutch medical resources. \\\\ \\textbf{Results:} The resulting corpus comprises $\\pm$ 35 billion tokens across the medical domain in about 100 million documents, freely available on Hugging Face. \\\\ \\textbf{Conclusion:} This work establishes the first large-scale Dutch medical language corpus for pre-training and downstream NLP tasks.", "date": "2026-04-28", "categories": []}
{"id": "2604.25605", "title": "Health System Scale Semantic Search Across Unstructured Clinical Notes", "abstract": "Introduction: Semantic search, which retrieves documents based on conceptual similarity rather than keyword matching, offers substantial advantages for retrieval of clinical information. However, deploying semantic search across entire health systems, comprising hundreds of millions of clinical notes, presents formidable engineering, cost, and governance challenges that have prevented adoption. Methods: We deployed a semantic search system at a large children's hospital indexing 166 million clinical notes (484 million vectors) from 1.68 million patients. The system uses instruction-tuned qwen3-embedding-0.6B embeddings, stores vectors in a managed database with storage-optimized indexing, maintains full-text metadata in a low-latency key-value store, and operates within a HIPAA-compliant governance framework. We evaluated the system through three experiments: optimization of embedding model and chunking strategy using a physician-authored benchmark dataset, characterization of full-scale performance (cost, latency, retrieval quality), and clinical utility assessment via comparison of chart abstraction efficiency across three tasks. Results: The system delivers sub-second query latency (median 237 ms single-user, 451 ms 20-user concurrency) with monthly costs of approximately USD 4,000. Qwen3 embeddings with 300-token chunk size achieved 94.6% accuracy on a clinical question-answering benchmark. In clinical utility evaluation across three abstraction tasks, semantic search reduced time-to-completion by 24 to 89% compared to clinician-performed chart review while maintaining comparable inter-rater agreement. Conclusion: Health-system-scale semantic search is both technically and operationally feasible. The system provides infrastructure supporting interactive search, cohort generation, and downstream LLM-powered clinical applications without requiring specialized informatics expertise.", "date": "2026-04-28", "categories": []}
{"id": "2509.03525", "title": "Speech-Based Cognitive Screening: A Systematic Evaluation of LLM Adaptation Strategies", "abstract": "Over half of US adults with Alzheimer disease and related dementias remain undiagnosed, and speech-based screening offers a scalable detection approach. We compared large language model adaptation strategies for dementia detection using the DementiaBank speech corpus, evaluating nine text-only models and three multimodal audio-text models on recordings from DementiaBank speech corpus. Adaptations included in-context learning with different demonstration selection policies, reasoning-augmented prompting, parameter-efficient fine-tuning, and multimodal integration. Results showed that class-centroid demonstrations achieved the highest in-context learning performance, reasoning improved smaller models, and token-level fine-tuning generally produced the best scores. Adding a classification head substantially improved underperforming models. Among multimodal models, fine-tuned audio-text systems performed well but did not surpass the top text-only models. These findings highlight that model adaptation strategies, including demonstration selection, reasoning design, and tuning method, critically influence speech-based dementia detection, and that properly adapted open-weight models can match or exceed commercial systems.", "date": "2025-08-24", "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": "2604.23059", "title": "Implicit Framing in Obstetric Counseling Notes: A Grounded LLM Pipeline on a VBAC-Eligible Cohort", "abstract": "Clinical framing -- the linguistic manner in which clinical information is presented -- can influence patient understanding and decision-making, with important implications for healthcare outcomes. Obstetrics is a high-stakes domain in which physicians counsel patients on delivery mode choices such as vaginal birth after cesarean (VBAC) and repeat cesarean section (RCS), yet counseling language remains underexplored in large-scale clinical text analysis. In this work, we analyze physician counseling language in 2,024 obstetric history and physical narratives for a rigorously defined cohort of patients for whom both VBAC and RCS were clinically viable options. To control for confounding due to medical contraindications, we first construct a VBAC-eligible cohort using structured clinical data supplemented by a large language model (LLM)-based extraction pipeline constrained to grounded, verbatim evidence from free-text narratives. We then apply a zero-shot LLM framework to categorize counseling segments into predefined framing categories capturing how physicians linguistically present delivery options. Our analysis reveals a significant difference in counseling framing distributions between VBAC and RCS notes; risk-focused language accounts for a substantially larger share of counseling segments in RCS documentation than in VBAC, with category-level differences confirmed by statistical testing, highlighting the value of controlled LLM-based framing analysis in obstetric care.", "date": "2026-04-24", "categories": []}
{"id": "2604.23605", "title": "Thinking Like a Clinician: A Cognitive AI Agent for Clinical Diagnosis via Panoramic Profiling and Adversarial Debate", "abstract": "The application of large language models (LLMs) in clinical decision support faces significant challenges of \"tunnel vision\" and diagnostic hallucinations present in their processing unstructured electronic health records (EHRs). To address these challenges, we propose a novel chain-based clinical reasoning framework, called DxChain, which transforms the diagnostic workflow into an iterative process by mirroring a clinician's cognitive trajectory that consists of \"Memory Anchoring\", \"Navigation\" and \"Verification\" phases. DxChain introduces three key methodological innovations to elicit the potential of LLM: (i) a Profile-Then-Plan paradigm to mitigate cold-start hallucinations by establishing a panoramic patient baseline, (ii) a Medical Tree-of-Thoughts (Med-ToT) algorithm for strategic look ahead planning and resource aware navigation, and (iii) a Dialectical Diagnostic Verification procedure utilizing \"Angel-Devil\" adversarial debates to resolve complex evidence conflicts. Evaluated on two real world benchmarks, MIMIC-IV-Ext Cardiac Disease and MIMIC-IV-Ext CDM, DxChain achieves state-of-the-art performances in both diagnostic accuracy and logical consistency, offering a modular and reliable architecture for next-generation clinical AI. The code is at https://anonymous.4open.science/r/Dx-Chain.", "date": "2026-04-26", "categories": []}
{"id": "2604.24001", "title": "CT-FineBench: A Diagnostic Fidelity Benchmark for Fine-Grained Evaluation of CT Report Generation", "abstract": "The evaluation of generated reports remains a critical challenge in Computed Tomography (CT) report generation, due to the large volume of text, the diversity and complexity of findings, and the presence of fine-grained, disease-oriented attributes. Conventional evaluation metrics offer only coarse measures of lexical overlap or entity matching and fail to reflect the granular diagnostic accuracy required for clinical use. To address this gap, we propose CT-FineBench, a benchmark built from CT-RATE and Merlin to evaluate the fine-grained factual consistency of CT reports, constructed from CT-RATE and Merlin. Our benchmark is constructed through a meticulous, Question-Answering (QA) based process: first, we identify and structure key, finding-specific clinical attributes (like location, size, margin). Second, we systematically transform these attributes into a QA dataset, where questions probe for specific clinical details grounded in gold-standard reports. The evaluation protocol for CT-FineBench involves using this QA dataset to query a machine-generated report and scoring the correctness of the answers. This allows for a comprehensive, interpretable, and clinically-relevant assessment, moving beyond superficial lexical overlap to pinpoint specific clinical errors. Experiments show that CT-FineBench correlates better with expert clinical assessment and is substantially more sensitive to fine-grained factual errors than prior metrics.", "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": "2604.14651", "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": "2604.21027", "title": "HypEHR: Hyperbolic Modeling of Electronic Health Records for Efficient Question Answering", "abstract": "Electronic health record (EHR) question answering is often handled by LLM-based pipelines that are costly to deploy and do not explicitly leverage the hierarchical structure of clinical data. Motivated by evidence that medical ontologies and patient trajectories exhibit hyperbolic geometry, we propose HypEHR, a compact Lorentzian model that embeds codes, visits, and questions in hyperbolic space and answers queries via geometry-consistent cross-attention with type-specific pointer heads. HypEHR is pretrained with next-visit diagnosis prediction and hierarchy-aware regularization to align representations with the ICD ontology. On two MIMIC-IV-based EHR-QA benchmarks, HypEHR approaches LLM-based methods while using far fewer parameters. Our code is publicly available at https://github.com/yuyuliu11037/HypEHR.", "date": "2026-04-22", "categories": []}
{"id": "2604.21076", "title": "Serialisation Strategy Matters: How FHIR Data Format Affects LLM Medication Reconciliation", "abstract": "Medication reconciliation at clinical handoffs is a high-stakes, error-prone process. Large language models are increasingly proposed to assist with this task using FHIR-structured patient records, but a fundamental and largely unstudied variable is how the FHIR data is serialised before being passed to the model. We present the first systematic comparison of four FHIR serialisation strategies (Raw JSON, Markdown Table, Clinical Narrative, and Chronological Timeline) across five open-weight models (Phi-3.5-mini, Mistral-7B, BioMistral-7B, Llama-3.1-8B, Llama-3.3-70B) on a controlled benchmark of 200 synthetic patients, totalling 4,000 inference runs. We find that serialisation strategy has a large, statistically significant effect on performance for models up to 8B parameters: Clinical Narrative outperforms Raw JSON by up to 19 F1 points for Mistral-7B (r = 0.617, p < 10^{-10}). This advantage reverses at 70B, where Raw JSON achieves the best mean F1 of 0.9956. In all 20 model and strategy combinations, mean precision exceeds mean recall: omission is the dominant failure mode, with models more often missing an active medication than fabricating one, which changes how clinical safety auditing priorities should be set. Smaller models plateau at roughly 7-10 concurrent active medications, leaving polypharmacy patients, the patients most at risk from reconciliation errors, systematically underserved. BioMistral-7B, a domain-pretrained model without instruction tuning, produces zero usable output in all conditions, showing that domain pretraining alone is not sufficient for structured extraction. These results offer practical, evidence-based format recommendations for clinical LLM deployment: Clinical Narrative for models up to 8B, Raw JSON for 70B and above. The complete pipeline is reproducible on open-source tools running on an AWS g6e.xlarge instance (NVIDIA L40S, 48 GB VRAM).", "date": "2026-04-22", "categories": []}
{"id": "2604.21421", "title": "Differentially Private De-identification of Dutch Clinical Notes: A Comparative Evaluation", "abstract": "Protecting patient privacy in clinical narratives is essential for enabling secondary use of healthcare data under regulations such as GDPR and HIPAA. While manual de-identification remains the gold standard, it is costly and slow, motivating the need for automated methods that combine privacy guarantees with high utility. Most automated text de-identification pipelines employed named entity recognition (NER) to identify protected entities for redaction. Although methods based on differential privacy (DP) provide formal privacy guarantees, more recently also large language models (LLMs) are increasingly used for text de-identification in the clinical domain. In this work, we present the first comparative study of DP, NER, and LLMs for Dutch clinical text de-identification. We investigate these methods separately as well as hybrid strategies that apply NER or LLM preprocessing prior to DP, and assess performance in terms of privacy leakage and extrinsic evaluation (entity and relation classification). We show that DP mechanisms alone degrade utility substantially, but combining them with linguistic preprocessing, especially LLM-based redaction, significantly improves the privacy-utility trade-off.", "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": "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": "2604.19774", "title": "Phase 1 Implementation of LLM-generated Discharge Summaries showing high Adoption in a Dutch Academic Hospital", "abstract": "Writing discharge summaries to transfer medical information is an important but time-consuming process that can be assisted by Large Language Models (LLMs). This prospective mixed methods pilot study evaluated an Electronic Health Record (EHR)-integrated LLM to generate discharge summaries drafts. In total, 379 discharge summaries were generated in clinical practice by 21 residents and 4 physician assistants during 9 weeks in our academic hospital. LLM-generated text was copied in 58.5% of admissions, and identifiable LLM content could be traced to 29.1% of final discharge letters. Notably, 86.9% of users self-reported a reduction in documentation time, and 60.9% a reduction in administrative workload. Intent to use after the pilot phase was high (91.3%), supporting further implementation of this use-case. Accurately measuring the documentation time of users on discharge summaries remains challenging, but will be necessary for future extrinsic evaluation of LLM-assisted documentation.", "date": "2026-03-27", "categories": []}
{"id": "2604.19776", "title": "Development and Preliminary Evaluation of a Domain-Specific Large Language Model for Tuberculosis Care in South Africa", "abstract": "Tuberculosis (TB) is one of the world's deadliest infectious diseases, and in South Africa, it contributes a significant burden to the country's health care system. This paper presents an experimental study on the development of a domain-specific Large Language Model (DS-LLM) for TB care that can help to alleviate the burden on patients and healthcare providers. To achieve this, a literature review was conducted to understand current LLM development strategies, specifically in the medical domain. Thereafter, data were collected from South African TB guidelines, selected TB literature, and existing benchmark medical datasets. We performed LLM fine-tuning by using the Quantised Low-Rank Adaptation (QLoRA) algorithm on a medical LLM (BioMistral-7B), and also implemented Retrieval-Augmented Generation using GraphRAG. The developed DS-LLM was evaluated against the base BioMistral-7B model and a general-purpose LLM using a mix of automated metrics and quantitative ratings. The results show that the DS-LLM had better performance compared to the base model in terms of its contextual alignment (lexical, semantic, and knowledge) for TB care in South Africa.", "date": "2026-03-28", "categories": []}
{"id": "2604.20560", "title": "LLM StructCore: Schema-Guided Reasoning Condensation and Deterministic Compilation", "abstract": "Automatically filling Case Report Forms (CRFs) from clinical notes is challenging due to noisy language, strict output contracts, and the high cost of false positives. We describe our CL4Health 2026 submission for Dyspnea CRF filling (134 items) using a contract-driven two-stage design grounded in Schema-Guided Reasoning (SGR). The key task property is extreme sparsity: the majority of fields are unknown, and official scoring penalizes both empty values and unsupported predictions. We shift from a single-step \"LLM predicts 134 fields\" approach to a decomposition where (i) Stage 1 produces a stable SGR-style JSON summary with exactly 9 domain keys, and (ii) Stage 2 is a fully deterministic, 0-LLM compiler that parses the Stage 1 summary, canonicalizes item names, normalizes predictions to the official controlled vocabulary, applies evidence-gated false-positive filters, and expands the output into the required 134-item format. On the dev80 split, the best teacher configuration achieves macro-F1 0.6543 (EN) and 0.6905 (IT); on the hidden test200, the submitted English variant scores 0.63 on Codabench. The pipeline is language-agnostic: Italian results match or exceed English with no language-specific engineering.", "date": "2026-04-22", "categories": []}
{"id": "2505.21242", "title": "Evaluation of LLMs in Medical Text Summarization: The Role of Vocabulary Adaptation in High OOV Settings", "abstract": "Large Language Models (LLMs) recently achieved great success in medical text summarization by simply using in-context learning. However, these recent efforts do not perform fine-grained evaluations under difficult settings where LLMs might fail. They typically report performance scores over the entire dataset. Through our benchmarking study, we show that LLMs show a significant performance drop for data points with high concentration of out-of-vocabulary (OOV) words or with high novelty. Vocabulary adaptation is an intuitive solution to this vocabulary mismatch issue where the LLM vocabulary gets updated with certain expert domain (here, medical) words or subwords. An interesting finding from our study is that Llama-3.1, even with a vocabulary size of around 128K tokens, still faces over-fragmentation issue with medical words. To that end, we show vocabulary adaptation helps improve the LLM summarization performance even in difficult settings. Through extensive experimentation of multiple vocabulary adaptation strategies, two continual pretraining strategies, and three benchmark medical summarization datasets, we gain valuable insights into the role of vocabulary adaptation strategies for customizing LLMs to the medical domain. We also performed a human evaluation study with medical experts where they found that vocabulary adaptation results in more relevant and faithful summaries. Our codebase is made publicly available at https://github.com/gb-kgp/LLM-MedicalSummarization-Benchmark.", "date": "2025-05-27", "categories": []}
{"id": "2604.14165", "title": "EviSearch: A Human in the Loop System for Extracting and Auditing Clinical Evidence for Systematic Reviews", "abstract": "We present EviSearch, a multi-agent extraction system that automates the creation of ontology-aligned clinical evidence tables directly from native trial PDFs while guaranteeing per-cell provenance for audit and human verification. EviSearch pairs a PDF-query agent (which preserves rendered layout and figures) with a retrieval-guided search agent and a reconciliation module that forces page-level verification when agents disagree. The pipeline is designed for high-precision extraction across multimodal evidence sources (text, tables, figures) and for generating reviewer-actionable provenance that clinicians can inspect and correct. On a clinician-curated benchmark of oncology trial papers, EviSearch substantially improves extraction accuracy relative to strong parsed-text baselines while providing comprehensive attribution coverage. By logging reconciler decisions and reviewer edits, the system produces structured preference and supervision signals that bootstrap iterative model improvement. EviSearch is intended to accelerate living systematic review workflows, reduce manual curation burden, and provide a safe, auditable path for integrating LLM-based extraction into evidence synthesis pipelines.", "date": "2026-03-23", "categories": []}
{"id": "2604.19060", "title": "Reinforcement Learning Improves LLM Accuracy and Reasoning in Disease Classification from Radiology Reports", "abstract": "Accurate disease classification from radiology reports is essential for many applications. While supervised fine-tuning (SFT) of lightweight LLMs improves accuracy, it can degrade reasoning. We propose a two-stage approach: SFT on disease labels followed by Group Relative Policy Optimization (GRPO) to refine predictions by optimizing accuracy and format without reasoning supervision. Across three radiologist-annotated datasets, SFT outperformed baselines and GRPO further improved classification and enhanced reasoning recall and comprehensiveness.", "date": "2026-04-21", "categories": []}
{"id": "2507.12261", "title": "Infherno: End-to-end Agent-based FHIR Resource Synthesis from Free-form Clinical Notes", "abstract": "For clinical data integration and healthcare services, the HL7 FHIR standard has established itself as a desirable format for interoperability between complex health data. Previous attempts at automating the translation from free-form clinical notes into structured FHIR resources address narrowly defined tasks and rely on modular approaches or LLMs with instruction tuning and constrained decoding. As those solutions frequently suffer from limited generalizability and structural inconformity, we propose an end-to-end framework powered by LLM agents, code execution, and healthcare terminology database tools to address these issues. Our solution, called Infherno, is designed to adhere to the FHIR document schema and competes well with a human baseline in predicting FHIR resources from unstructured text. The implementation features a front end for custom and synthetic data and both local and proprietary models, supporting clinical data integration processes and interoperability across institutions. Gemini 2.5-Pro excels in our evaluation on synthetic and clinical datasets, yet ambiguity and feasibility of collecting ground-truth data remain open problems.", "date": "2025-07-16", "categories": []}
{"id": "2601.16397", "title": "From Attribution to Abstention: Training-Free Attention-Based Auditing for Clinical Summarization", "abstract": "Deploying multimodal large language models (MLLMs) for clinical summarization demands not only fluent generation but also transparency about where each statement originates-and a mechanism to flag when statements lack evidential support. We present ClinTrace, a training-free framework that extracts two clinically useful signals from the decoder attention weights that every transformer-based MLLM already produces during generation: (i) fine-grained source attributions linking each output sentence to supporting text spans or images, and (ii) per-sentence groundedness scores that identify poorly supported claims as candidate hallucinations. Both signals are derived from the same attention tensors in a single pass, requiring no retraining, no auxiliary models, and no additional inference cost. We evaluate on two clinical summarization tasks: doctor-patient dialogue summarization (CliConSummation) and radiology report summarization (MIMIC-CXR) using a general-purpose MLLM (Qwen3-8B) and a medical-finetuned model (HuatuoGPT-Vision-7B). For source attribution, ClinTrace achieves over 92% text F1 on radiology and 88% on dialogue summarization, substantially outperforming embedding-based and self-attribution baselines. For hallucination detection, groundedness scores achieve 0.77 AUROC with the medical-finetuned model: competitive with embedding-based confidence at zero additional cost, and enable an abstention mechanism that improves faithfulness from 61.7% to 72.6% by withholding the least: grounded 20% of output for clinician review. Notably, medical finetuning substantially improves the reliability of attention-based hallucination detection, suggesting that domain adaptation produces more semantically structured attention patterns amenable to self-auditing.", "date": "2026-01-23", "categories": []}
{"id": "2603.20435", "title": "Deep reflective reasoning in interdependence constrained structured data extraction from clinical notes for digital health", "abstract": "Extracting structured information from clinical notes requires navigating a dense web of interdependent variables where the value of one attribute logically constrains others. Existing Large Language Model (LLM)-based extraction pipelines often struggle to capture these dependencies, leading to clinically inconsistent outputs. We propose deep reflective reasoning, a large language model agent framework that iteratively self-critiques and revises structured outputs by checking consistency among variables, the input text, and retrieved domain knowledge, stopping when outputs converge. We extensively evaluate the proposed method in three diverse oncology applications: (1) On colorectal cancer synoptic reporting from gross descriptions (n=217), reflective reasoning improved average F1 across eight categorical synoptic variables from 0.828 to 0.911 and increased mean correct rate across four numeric variables from 0.806 to 0.895; (2) On Ewing sarcoma CD99 immunostaining pattern identification (n=200), the accuracy improved from 0.870 to 0.927; (3) On lung cancer tumor staging (n=100), tumor stage accuracy improved from 0.680 to 0.833 (pT: 0.842 -> 0.884; pN: 0.885 -> 0.948). The results demonstrate that deep reflective reasoning can systematically improve the reliability of LLM-based structured data extraction under interdependence constraints, enabling more consistent machine-operable clinical datasets and facilitating knowledge discovery with machine learning and data science towards digital health.", "date": "2026-03-20", "categories": []}
{"id": "2604.07549", "title": "EMSDialog: Synthetic Multi-person Emergency Medical Service Dialogue Generation from Electronic Patient Care Reports via Multi-LLM Agents", "abstract": "Conversational diagnosis prediction requires models to track evolving evidence in streaming clinical conversations and decide when to commit to a diagnosis. Existing medical dialogue corpora are largely dyadic or lack the multi-party workflow and annotations needed for this setting. We introduce an ePCR-grounded, topic-flow-based multi-agent generation pipeline that iteratively plans, generates, and self-refines dialogues with rule-based factual and topic flow checks. The pipeline yields EMSDialog, a dataset of 4,414 synthetic multi-speaker EMS conversations based on a real-world ePCR dataset, annotated with 43 diagnoses, speaker roles, and turn-level topics. Human and LLM evaluations confirm high quality and realism of EMSDialog using both utterance- and conversation-level metrics. Results show that EMSDialog-augmented training improves accuracy, timeliness, and stability of EMS conversational diagnosis prediction. Our datasets and code are publicly available at https://uva-dsa.github.io/EMSDialog", "date": "2026-04-08", "categories": []}
{"id": "2604.11137", "title": "From Answers to Arguments: Toward Trustworthy Clinical Diagnostic Reasoning with Toulmin-Guided Curriculum Goal-Conditioned Learning", "abstract": "The integration of Large Language Models (LLMs) into clinical decision support is critically obstructed by their opaque and often unreliable reasoning. In the high-stakes domain of healthcare, correct answers alone are insufficient; clinical practice demands full transparency to ensure patient safety and enable professional accountability. A pervasive and dangerous weakness of current LLMs is their tendency to produce \"correct answers through flawed reasoning.\" This issue is far more than a minor academic flaw; such process errors signal a fundamental lack of robust understanding, making the model prone to broader hallucinations and unpredictable failures when faced with real-world clinical complexity. In this paper, we establish a framework for trustworthy clinical argumentation by adapting the Toulmin model to the diagnostic process. We propose a novel training pipeline: Curriculum Goal-Conditioned Learning (CGCL), designed to progressively train LLM to generate diagnostic arguments that explicitly follow this Toulmin structure. CGCL's progressive three-stage curriculum systematically builds a solid clinical argument: (1) extracting facts and generating differential diagnoses; (2) justifying a core hypothesis while rebutting alternatives; and (3) synthesizing the analysis into a final, qualified conclusion. We validate CGCL using T-Eval, a quantitative framework measuring the integrity of the diagnosis reasoning. Experiments show that our method achieves diagnostic accuracy and reasoning quality comparable to resource-intensive Reinforcement Learning (RL) methods, while offering a more stable and efficient training pipeline.", "date": "2026-04-13", "categories": []}
{"id": "2604.16364", "title": "Clinical Note Bloat Reduction for Efficient LLM Use", "abstract": "Health systems are rapidly deploying large language models (LLMs) that use clinical notes for clinical decision support applications. However, modern documentation practices rely heavily on templates, copy--paste shortcuts, and auto-populated fields, producing extensive duplicated text (``note bloat'') that dilutes clinically meaningful signal and substantially increases the computational cost of LLM use. We introduce TRACE, a scalable preprocessing pipeline that removes note bloat by leveraging EHR attribution metadata to identify templated and copied content and applying frequency-based deduplication when metadata are unavailable. We evaluated TRACE across four real--world clinical cohorts spanning liver transplantation, obstetrics, and inpatient care (5.3 million notes) using blinded physician review and downstream modeling tasks. TRACE removed 47.3% of chart text while preserving performance for information extraction and clinical outcome prediction. At a large academic medical center, this reduction corresponds to an estimated $9.5 million annual decrease in LLM inference costs assuming one query per encounter. These findings show how underutilized EHR metadata can enable more scalable and cost-efficient deployment of LLM-based clinical systems.", "date": "2026-03-21", "categories": []}
{"id": "2604.17214", "title": "Beyond the Basics: Leveraging Large Language Model for Fine-Grained Medical Entity Recognition", "abstract": "Extracting clinically relevant information from unstructured medical narratives such as admission notes, discharge summaries, and emergency case histories remains a challenge in clinical natural language processing (NLP). Medical Entity Recognition (MER) identifies meaningful concepts embedded in these records. Recent advancements in large language models (LLMs) have shown competitive MER performance; however, evaluations often focus on general entity types, offering limited utility for real-world clinical needs requiring finer-grained extraction. To address this gap, we rigorously evaluated the open-source LLaMA3 model for fine-grained medical entity recognition across 18 clinically detailed categories. To optimize performance, we employed three learning paradigms: zero-shot, few-shot, and fine-tuning with Low-Rank Adaptation (LoRA). To further enhance few-shot learning, we introduced two example selection methods based on token- and sentence-level embedding similarity, utilizing a pre-trained BioBERT model. Unlike prior work assessing zero-shot and few-shot performance on proprietary models (e.g., GPT-4) or fine-tuning different architectures, we ensured methodological consistency by applying all strategies to a unified LLaMA3 backbone, enabling fair comparison across learning settings. Our results showed that fine-tuned LLaMA3 surpasses zero-shot and few-shot approaches by 63.11% and 35.63%, respectivel respectively, achieving an F1 score of 81.24% in granular medical entity extraction.", "date": "2026-04-19", "categories": []}
{"id": "2604.15646", "title": "FD-NL2SQL: Feedback-Driven Clinical NL2SQL that Improves with Use", "abstract": "Clinicians exploring oncology trial repositories often need ad-hoc, multi-constraint queries over biomarkers, endpoints, interventions, and time, yet writing SQL requires schema expertise. We demo FD-NL2SQL, a feedback-driven clinical NL2SQL assistant for SQLite-based oncology databases. Given a natural-language question, a schema-aware LLM decomposes it into predicate-level sub-questions, retrieves semantically similar expert-verified NL2SQL exemplars via sentence embeddings, and synthesizes executable SQL conditioned on the decomposition, retrieved exemplars, and schema, with post-processing validity checks. To improve with use, FD-NL2SQL incorporates two update signals: (i) clinician edits of generated SQL are approved and added to the exemplar bank; and (ii) lightweight logic-based SQL augmentation applies a single atomic mutation (e.g., operator or column change), retaining variants only if they return non-empty results. A second LLM generates the corresponding natural-language question and predicate decomposition for accepted variants, automatically expanding the exemplar bank without additional annotation. The demo interface exposes decomposition, retrieval, synthesis, and execution results to support interactive refinement and continuous improvement.", "date": "2026-04-17", "categories": []}
{"id": "2604.16175", "title": "MARCH: Multi-Agent Radiology Clinical Hierarchy for CT Report Generation", "abstract": "Automated 3D radiology report generation often suffers from clinical hallucinations and a lack of the iterative verification found in human practice. While recent Vision-Language Models (VLMs) have advanced the field, they typically operate as monolithic \"black-box\" systems without the collaborative oversight characteristic of clinical workflows. To address these challenges, we propose MARCH (Multi-Agent Radiology Clinical Hierarchy), a multi-agent framework that emulates the professional hierarchy of radiology departments and assigns specialized roles to distinct agents. MARCH utilizes a Resident Agent for initial drafting with multi-scale CT feature extraction, multiple Fellow Agents for retrieval-augmented revision, and an Attending Agent that orchestrates an iterative, stance-based consensus discourse to resolve diagnostic discrepancies. On the RadGenome-ChestCT dataset, MARCH significantly outperforms state-of-the-art baselines in both clinical fidelity and linguistic accuracy. Our work demonstrates that modeling human-like organizational structures enhances the reliability of AI in high-stakes medical domains.", "date": "2026-04-17", "categories": []}
{"id": "2601.13503", "title": "Anonpsy: A Graph-Based Framework for Structure-Preserving De-identification of Psychiatric Narratives", "abstract": "Psychiatric narratives encode patient identity not only through explicit identifiers but also through idiosyncratic life events embedded in their clinical structure. Existing de-identification approaches, including PHI masking and LLM-based synthetic rewriting, operate at the text level and offer limited control over which semantic elements are preserved or altered. We introduce Anonpsy, a de-identification framework that reformulates the task as graph-guided semantic rewriting. Anonpsy (1) converts each narrative into a semantic graph encoding clinical entities, temporal anchors, and typed relations; (2) applies graph-constrained perturbations that modify identifying context while preserving clinically essential structure; and (3) regenerates text via graph-conditioned LLM generation. Evaluated on 90 clinician-authored psychiatric case narratives, Anonpsy preserves diagnostic fidelity while achieving consistently low re-identification risk under expert, semantic, and GPT-5-based evaluations. Compared with a strong LLM-only rewriting baseline, Anonpsy yields substantially lower semantic similarity and identifiability. These results demonstrate that explicit structural representations combined with constrained generation provide an effective approach to de-identification for psychiatric narratives.", "date": "2026-01-20", "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": "2604.14616", "title": "Retrieve, Then Classify: Corpus-Grounded Automation of Clinical Value Set Authoring", "abstract": "Clinical value set authoring -- the task of identifying all codes in a standardized vocabulary that define a clinical concept -- is a recurring bottleneck in clinical quality measurement and phenotyping. A natural approach is to prompt a large language model (LLM) to generate the required codes directly, but structured clinical vocabularies are large, version-controlled, and not reliably memorized during pretraining. We propose Retrieval-Augmented Set Completion (RASC): retrieve the $K$ most similar existing value sets from a curated corpus to form a candidate pool, then apply a classifier to each candidate code. Theoretically, retrieve-and-select can reduce statistical complexity by shrinking the effective output space from the full vocabulary to a much smaller retrieved candidate pool. We demonstrate the utility of RASC on 11,803 publicly available VSAC value sets, constructing the first large-scale benchmark for this task. A cross-encoder fine-tuned on SAPBert achieves AUROC~0.852 and value-set-level F1~0.298, outperforming a simpler three-layer Multilayer Perceptron (AUROC~0.799, F1~0.250) and both reduce the number of irrelevant candidates per true positive from 12.3 (retrieval-only) to approximately 3.2 and 4.4 respectively. Zero-shot GPT-4o achieves value-set-level F1~0.105, with 48.6\\% of returned codes absent from VSAC entirely. This performance gap widens with increasing value set size, consistent with RASC's theoretical advantage. We observe similar performance gains across two other classifier model types, namely a cross-encoder initialized from pre-trained SAPBert and a LightGBM model, demonstrating that RASC's benefits extend beyond a single model class. The code to download and create the benchmark dataset, as well as the model training code is available at: \\href{https://github.com/mukhes3/RASC}{https://github.com/mukhes3/RASC}.", "date": "2026-04-16", "categories": []}
{"id": "2604.14815", "title": "Domain Fine-Tuning FinBERT on Finnish Histopathological Reports: Train-Time Signals and Downstream Correlations", "abstract": "In NLP classification tasks where little labeled data exists, domain fine-tuning of transformer models on unlabeled data is an established approach. In this paper we have two aims.\n  (1) We describe our observations from fine-tuning the Finnish BERT model on Finnish medical text data.\n  (2) We report on our attempts to predict the benefit of domain-specific pre-training of Finnish BERT from observing the geometry of embedding changes due to domain fine-tuning.\n  Our driving motivation is the common\\situation in healthcare AI where we might experience long delays in acquiring datasets, especially with respect to labels.", "date": "2026-04-16", "categories": []}
{"id": "2604.15203", "title": "MADE: A Living Benchmark for Multi-Label Text Classification with Uncertainty Quantification of Medical Device Adverse Events", "abstract": "Machine learning in high-stakes domains such as healthcare requires not only strong predictive performance but also reliable uncertainty quantification (UQ) to support human oversight. Multi-label text classification (MLTC) is a central task in this domain, yet remains challenging due to label imbalances, dependencies, and combinatorial complexity. Existing MLTC benchmarks are increasingly saturated and may be affected by training data contamination, making it difficult to distinguish genuine reasoning capabilities from memorization. We introduce MADE, a living MLTC benchmark derived from {m}edical device {ad}verse {e}vent reports and continuously updated with newly published reports to prevent contamination. MADE features a long-tailed distribution of hierarchical labels and enables reproducible evaluation with strict temporal splits. We establish baselines across more than 20 encoder- and decoder-only models under fine-tuning and few-shot settings (instruction-tuned/reasoning variants, local/API-accessible). We systematically assess entropy-/consistency-based and self-verbalized UQ methods. Results show clear trade-offs: smaller discriminatively fine-tuned decoders achieve the strongest head-to-tail accuracy while maintaining competitive UQ; generative fine-tuning delivers the most reliable UQ; large reasoning models improve performance on rare labels yet exhibit surprisingly weak UQ; and self-verbalized confidence is not a reliable proxy for uncertainty. Our work is publicly available at https://hhi.fraunhofer.de/aml-demonstrator/made-benchmark.", "date": "2026-04-16", "categories": []}
{"id": "2604.11133", "title": "How Robust Are Large Language Models for Clinical Numeracy? An Empirical Study on Numerical Reasoning Abilities in Clinical Contexts", "abstract": "Large Language Models (LLMs) are increasingly being explored for clinical question answering and decision support, yet safe deployment critically requires reliable handling of patient measurements in heterogeneous clinical notes. Existing evaluations of LLMs for clinical numerical reasoning provide limited operation-level coverage, restricted primarily to arithmetic computation, and rarely assess the robustness of numerical understanding across clinical note formats. We introduce ClinicNumRobBench, a benchmark of 1,624 context-question instances with ground-truth answers that evaluates four main types of clinical numeracy: value retrieval, arithmetic computation, relational comparison, and aggregation. To stress-test robustness, ClinicNumRobBench presents longitudinal MIMIC-IV vital-sign records in three semantically equivalent representations, including a real-world note-style variant derived from the Open Patients dataset, and instantiates queries using 42 question templates. Experiments on 17 LLMs show that value retrieval is generally strong, with most models exceeding 85% accuracy, while relational comparison and aggregation remain challenging, with some models scoring below 15%. Fine-tuning on medical data can reduce numeracy relative to base models by over 30%, and performance drops under note-style variation indicate LLM sensitivity to format. ClinicNumRobBench offers a rigorous testbed for clinically reliable numerical reasoning. Code and data URL are available on https://github.com/MinhVuong2000/ClinicNumRobBench.", "date": "2026-04-13", "categories": []}
{"id": "2604.13059", "title": "A Proactive EMR Assistant for Doctor-Patient Dialogue: Streaming ASR, Belief Stabilization, and Preliminary Controlled Evaluation", "abstract": "Most dialogue-based electronic medical record (EMR) systems still behave as passive pipelines: transcribe speech, extract information, and generate the final note after the consultation. That design improves documentation efficiency, but it is insufficient for proactive consultation support because it does not explicitly address streaming speech noise, missing punctuation, unstable diagnostic belief, objectification quality, or measurable next-action gains. We present an end-to-end proactive EMR assistant built around streaming speech recognition, punctuation restoration, stateful extraction, belief stabilization, objectified retrieval, action planning, and replayable report generation. The system is evaluated in a preliminary controlled setting using ten streamed doctor-patient dialogues and a 300-query retrieval benchmark aggregated across dialogues. The full system reaches state-event F1 of 0.84, retrieval Recall@5 of 0.87, and end-to-end pilot scores of 83.3% coverage, 81.4% structural completeness, and 80.0% risk recall. Ablations further suggest that punctuation restoration and belief stabilization may improve downstream extraction, retrieval, and action selection within this pilot. These results were obtained under a controlled simulated pilot setting rather than broad deployment claims, and they should not be read as evidence of clinical deployment readiness, clinical safety, or real-world clinical utility. Instead, they suggest that the proposed online architecture may be technically coherent and directionally supportive under tightly controlled pilot conditions. The present study should be read as a pilot concept demonstration under tightly controlled pilot conditions rather than as evidence of clinical deployment readiness or clinical generalizability.", "date": "2026-03-18", "categories": []}
{"id": "2604.13077", "title": "Can Large Language Models Reliably Extract Physiology Index Values from Coronary Angiography Reports?", "abstract": "Coronary angiography (CAG) reports contain clinically relevant physiological measurements, yet this information is typically in the form of unstructured natural language, limiting its use in research. We investigate the use of Large Language Models (LLMs) to automatically extract these values, along with their anatomical locations, from Portuguese CAG reports. To our knowledge, this study is the first addressing physiology indexes extraction from a large (1342 reports) corpus of CAG reports, and one of the few focusing on CAG or Portuguese clinical text.\n  We explore local privacy-preserving general-purpose and medical LLMs under different settings. Prompting strategies included zero-shot, few-shot, and few-shot prompting with implausible examples. In addition, we apply constrained generation and introduce a post-processing step based on RegEx. Given the sparsity of measurements, we propose a multi-stage evaluation framework separating format validity, value detection, and value correctness, while accounting for asymmetric clinical error costs.\n  This study demonstrates the potential of LLMs in for extracting physiological indices from Portuguese CAG reports. Non-medical models performed similarly, the best results were obtained with Llama with a zero-shot prompting, while GPT-OSS demonstrated the highest robustness to changes in the prompts. While MedGemma demonstrated similar results to non-medical models, MedLlama's results were out-of-format in the unconstrained setting, and had a significant lower performance in the constrained one. Changes in the prompt techinique and adding a RegEx layer showed no significant improvement across models, while using constrained generation decreased performance, although having the benefit of allowing the usage of specific models that are not able to conform with the templates.", "date": "2026-03-21", "categories": []}
{"id": "2604.13285", "title": "L2D-Clinical: Learning to Defer for Adaptive Model Selection in Clinical Text Classification", "abstract": "Clinical text classification requires choosing between specialized fine-tuned models (BERT variants) and general-purpose large language models (LLMs), yet neither dominates across all instances. We introduce Learning to Defer for clinical text (L2D-Clinical), a framework that learns when a BERT classifier should defer to an LLM based on uncertainty signals and text characteristics. Unlike prior L2D work that defers to human experts assumed universally superior, our approach enables adaptive deferral-improving accuracy when the LLM complements BERT. We evaluate on two English clinical tasks: (1) ADE detection (ADE Corpus V2), where BioBERT (F1=0.911) outperforms the LLM (F1=0.765), and (2) treatment outcome classification (MIMIC-IV with multi-LLM consensus ground truth), where GPT-5-nano (F1=0.967) outperforms ClinicalBERT (F1=0.887). On ADE, L2D-Clinical achieves F1=0.928 (+1.7 points over BERT) by selectively deferring 7% of instances where the LLM's high recall compensates for BERT's misses. On MIMIC, L2D-Clinical achieves F1=0.980 (+9.3 points over BERT) by deferring only 16.8\\% of cases to the LLM. The key insight is that L2D-Clinical learns to selectively leverage LLM strengths while minimizing API costs.", "date": "2026-04-14", "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": "2604.05795", "title": "Measuring What Matters!! Assessing Therapeutic Principles in Mental-Health Conversation", "abstract": "The increasing use of large language models in mental health applications calls for principled evaluation frameworks that assess alignment with psychotherapeutic best practices beyond surface-level fluency. While recent systems exhibit conversational competence, they lack structured mechanisms to evaluate adherence to core therapeutic principles. In this paper, we study the problem of evaluating AI-generated therapist-like responses for clinically grounded appropriateness and effectiveness. We assess each therapists utterance along six therapeutic principles: non-judgmental acceptance, warmth, respect for autonomy, active listening, reflective understanding, and situational appropriateness using a fine-grained ordinal scale. We introduce FAITH-M, a benchmark annotated with expert-assigned ordinal ratings, and propose CARE, a multi-stage evaluation framework that integrates intra-dialogue context, contrastive exemplar retrieval, and knowledge-distilled chain-of-thought reasoning. Experiments show that CARE achieves an F-1 score of 63.34 versus the strong baseline Qwen3 F-1 score of 38.56 which is a 64.26 improvement, which also serves as its backbone, indicating that gains arise from structured reasoning and contextual modeling rather than backbone capacity alone. Expert assessment and external dataset evaluations further demonstrate robustness under domain shift, while highlighting challenges in modelling implicit clinical nuance. Overall, CARE provides a clinically grounded framework for evaluating therapeutic fidelity in AI mental health systems.", "date": "2026-04-07", "categories": []}
{"id": "2604.12161", "title": "Development, Evaluation, and Deployment of a Multi-Agent System for Thoracic Tumor Board", "abstract": "Tumor boards are multidisciplinary conferences dedicated to producing actionable patient care recommendations with live review of primary radiology and pathology data. Succinct patient case summaries are needed to drive efficient and accurate case discussions. We developed a manual AI-based workflow to generate patient summaries to display live at the Stanford Thoracic Tumor board. To improve on this manually intensive process, we developed several automated AI chart summarization methods and evaluated them against physician gold standard summaries and fact-based scoring rubrics. We report these comparative evaluations as well as our deployment of the final state automated AI chart summarization tool along with post-deployment monitoring. We also validate the use of an LLM as a judge evaluation strategy for fact-based scoring. This work is an example of integrating AI-based workflows into routine clinical practice.", "date": "2026-04-14", "categories": []}
{"id": "2604.12721", "title": "InsightFlow: LLM-Driven Synthesis of Patient Narratives for Mental Health into Causal Models", "abstract": "Clinical case formulation organizes patient symptoms and psychosocial factors into causal models, often using the 5P framework. However, constructing such graphs from therapy transcripts is time consuming and varies across clinicians. We present InsightFlow, an LLM based approach that automatically generates 5P aligned causal graphs from patient-therapist dialogues. Using 46 psychotherapy intake transcripts annotated by clinical experts, we evaluate LLM generated graphs against human formulations using structural (NetSimile), semantic (embedding similarity), and expert rated clinical criteria. The generated graphs show structural similarity comparable to inter annotator agreement and high semantic alignment with human graphs. Expert evaluations rate the outputs as moderately complete, consistent, and clinically useful. While LLM graphs tend to form more interconnected structures compared to the chain like patterns of human graphs, overall complexity and content coverage are similar. These results suggest that LLMs can produce clinically meaningful case formulation graphs within the natural variability of expert practice. InsightFlow highlights the potential of automated causal modeling to augment clinical workflows, with future work needed to improve temporal reasoning and reduce redundancy.", "date": "2026-04-14", "categories": []}
{"id": "2509.02651", "title": "Bias Detection in Emergency Psychiatry: Linking Negative Language to Diagnostic Disparities", "abstract": "The emergency department (ED) is a high stress environment with increased risk of clinician bias exposure. In the United States, Black patients are more likely than other racial/ethnic groups to obtain their first schizophrenia (SCZ) diagnosis in the ED, a highly stigmatizing disorder. Therefore, understanding the link between clinician bias exposure and psychiatric outcomes is critical for promoting nondiscriminatory decision-making in the ED. This study examines the association between clinician bias exposure and psychiatric diagnosis using a sample of patients with anxiety, bipolar, depression, trauma, and SCZ diagnoses (N=29,005) from a diverse, large medical center. Clinician bias exposure was quantified as the ratio of negative to total number of sentences in psychiatric notes, labeled using a large language model (Mistral). We utilized logistic regression to predict SCZ diagnosis when controlling for patient demographics, risk factors, and negative sentence ratio (NSR). A high NSR significantly increased one's odds of obtaining a SCZ diagnosis and attenuated the effects of patient race. Black male patients with high NSR had the highest odds of being diagnosed with SCZ. Our findings suggest sentiment-based metrics can operationalize clinician bias exposure with real world data and reveal disparities beyond race or ethnicity.", "date": "2025-09-02", "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": "2604.09645", "title": "Generating High Quality Synthetic Data for Dutch Medical Conversations", "abstract": "Medical conversations offer insights into clinical communication often absent from Electronic Health Records. However, developing reliable clinical Natural Language Processing (NLP) models is hampered by the scarcity of domain-specific datasets, as clinical data are typically inaccessible due to privacy and ethical constraints. To address these challenges, we present a pipeline for generating synthetic Dutch medical dialogues using a Dutch fine-tuned Large Language Model, with real medical conversations serving as linguistic and structural reference. The generated dialogues were evaluated through quantitative metrics and qualitative review by native speakers and medical practitioners. Quantitative analysis revealed strong lexical variety and overly regular turn-taking, suggesting scripted rather than natural conversation flow. Qualitative review produced slightly below-average scores, with raters noting issues in domain specificity and natural expression. The limited correlation between quantitative and qualitative results highlights that numerical metrics alone cannot fully capture linguistic quality. Our findings demonstrate that generating synthetic Dutch medical dialogues is feasible but requires domain knowledge and carefully structured prompting to balance naturalness and structure in conversation. This work provides a foundation for expanding Dutch clinical NLP resources through ethically generated synthetic data.", "date": "2026-03-25", "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": "2604.10316", "title": "Comparative Analysis of Large Language Models in Healthcare", "abstract": "Background: Large Language Models (LLMs) are transforming artificial intelligence applications in healthcare due to their ability to understand, generate, and summarize complex medical text. They offer valuable support to clinicians, researchers, and patients, yet their deployment in high-stakes clinical environments raises critical concerns regarding accuracy, reliability, and patient safety. Despite substantial attention in recent years, standardized benchmarking of LLMs for medical applications has been limited. Objective: This study addresses the need for a standardized comparative evaluation of LLMs in medical settings. Method: We evaluate multiple models, including ChatGPT, LLaMA, Grok, Gemini, and ChatDoctor, on core medical tasks such as patient note summarization and medical question answering, using the open-access datasets, MedMCQA, PubMedQA, and Asclepius, and assess performance through a combination of linguistic and task-specific metrics. Results: The results indicate that domain-specific models, such as ChatDoctor, excel in contextual reliability, producing medically accurate and semantically aligned text, whereas general-purpose models like Grok and LLaMA perform better in structured question-answering tasks, demonstrating higher quantitative accuracy. This highlights the complementary strengths of domain-specific and general-purpose LLMs depending on the medical task. Conclusion: Our findings suggest that LLMs can meaningfully support medical professionals and enhance clinical decision-making; however, their safe and effective deployment requires adherence to ethical standards, contextual accuracy, and human oversight in relevant cases. These results underscore the importance of task-specific evaluation and cautious integration of LLMs into healthcare workflows.", "date": "2026-04-11", "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": "2604.08126", "title": "LLM-Based Data Generation and Clinical Skills Evaluation for Low-Resource French OSCEs", "abstract": "Objective Structured Clinical Examinations (OSCEs) are the standard method for assessing medical students' clinical and communication skills through structured patient interviews. In France, however, the organization of training sessions is limited by human and logistical constraints, restricting students' access to repeated practice and structured feedback. Recent advances in Natural Language Processing (NLP) and Large Language Models (LLMs) now offer the opportunity to automatically evaluate such medical interviews, thereby alleviating the need for human examiners during training. Yet, real French OSCE annotated transcripts remain extremely scarce, limiting reproducible research and reliable benchmarking. To address these challenges, we investigate the use of LLMs for both generating and evaluating French OSCE dialogues in a low-resource context. We introduce a controlled pipeline that produces synthetic doctor-patient interview transcripts guided by scenario-specific evaluation criteria, combining ideal and perturbed performances to simulate varying student skill levels. The resulting dialogues are automatically silver-labeled through an LLM-assisted framework supporting adjustable evaluation strictness. Benchmarking multiple open-source and proprietary LLMs shows that mid-size models ($\\le$32B parameters) achieve accuracies comparable to GPT-4o ($\\sim$90\\%) on synthetic data, highlighting the feasibility of locally deployable, privacy-preserving evaluation systems for medical education.", "date": "2026-04-09", "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": "2604.06197", "title": "Temporally Phenotyping GLP-1RA Case Reports with Large Language Models: A Textual Time Series Corpus and Risk Modeling", "abstract": "Type 2 diabetes case reports describe complex clinical courses, but their timelines are often expressed in language that is difficult to reuse in longitudinal modeling. To address this gap, we developed a textual time-series corpus of 136 PubMed Open Access single-patient case reports involving glucagon-like peptide 1 receptor agonists, with clinical events associated with their most probable reference times. We evaluated automated LLM timeline extraction against gold-standard timelines annotated by clinical domain experts, assessing how well systems recovered clinical events and their timings. The best-performing LLM produced high event coverage (GPT5; 0.871) and reliable temporal sequencing across symptoms (GPT5; 0.843), diagnoses, treatments, laboratory tests, and outcomes. As a downstream demonstration, time-to-event analyses in diabetes suggested lower risk of respiratory sequelae among GLP-1 users versus non-users (HR=0.259, p<0.05), consistent with prior reports of improved respiratory outcomes. Temporal annotations and code will be released upon acceptance.", "date": "2026-03-12", "categories": []}
{"id": "2604.06208", "title": "Extracting Breast Cancer Phenotypes from Clinical Notes: Comparing LLMs with Classical Ontology Methods", "abstract": "A significant amount of data held in Oncology Electronic Medical Records (EMRs) is contained in unstructured provider notes -- including but not limited to the chemotherapy (or cancer treatment) outcome, different biomarkers, the tumor's location, sizes, and growth patterns of a patient. The clinical studies show that the majority of oncologists are comfortable providing these valuable insights in their notes in a natural language rather than the relevant structured fields of an EMR. The major contribution of this research is to report an LLM-based framework to process provider notes and extract valuable medical knowledge and phenotype mentioned above, with a focus on the domain of oncology. In this paper, we focus on extracting phenotypes related to breast cancer using our LLM framework, and then compare its performance with earlier works that used knowledge-driven annotation system, paired with the NCIt Ontology Annotator. The results of the study show that an LLM-based information extraction framework can be easily adapted to extract phenotypes with an accuracy that is comparable to the classical ontology-based methods. However, once trained, they could be easily fine-tuned to cater for other cancer types and diseases.", "date": "2026-03-16", "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": "2604.06403", "title": "FMI@SU ToxHabits: Evaluating LLMs Performance on Toxic Habit Extraction in Spanish Clinical Texts", "abstract": "The paper presents an approach for the recognition of toxic habits named entities in Spanish clinical texts. The approach was developed for the ToxHabits Shared Task. Our team participated in subtask 1, which aims to detect substance use and abuse mentions in clinical case reports and classify them in four categories (Tobacco, Alcohol, Cannabis, and Drug). We explored various methods of utilizing LLMs for the task, including zero-shot, few-shot, and prompt optimization, and found that GPT-4.1's few-shot prompting performed the best in our experiments. Our method achieved an F1 score of 0.65 on the test set, demonstrating a promising result for recognizing named entities in languages other than English.", "date": "2026-04-07", "categories": []}
{"id": "2604.06424", "title": "Team Fusion@ SU@ BC8 SympTEMIST track: transformer-based approach for symptom recognition and linking", "abstract": "This paper presents a transformer-based approach to solving the SympTEMIST named entity recognition (NER) and entity linking (EL) tasks. For NER, we fine-tune a RoBERTa-based (1) token-level classifier with BiLSTM and CRF layers on an augmented train set. Entity linking is performed by generating candidates using the cross-lingual SapBERT XLMR-Large (2), and calculating cosine similarity against a knowledge base. The choice of knowledge base proves to have the highest impact on model accuracy.", "date": "2026-04-07", "categories": []}
{"id": "2604.06650", "title": "A Parameter-Efficient Transfer Learning Approach through Multitask Prompt Distillation and Decomposition for Clinical NLP", "abstract": "Existing prompt-based fine-tuning methods typically learn task-specific prompts independently, imposing significant computing and storage overhead at scale when deploying multiple clinical natural language processing (NLP) systems. We present a multitask prompt distillation and decomposition framework that learns a single shared metaprompt from 21 diverse clinical source tasks and adapts it to unseen target tasks with fewer than 0.05% trainable parameters. Evaluated across five clinical NLP task types (named entity recognition, relation extraction, question answering, natural language inference, and summarization) on 10 held-out target datasets using three backbone models (LLaMA 3.1 8B, Meditron3 8B, gpt-oss 20B), our framework consistently outperforms LoRA by 1.5~1.7% despite using orders of magnitude fewer parameters, and exceeds single-task prompt tuning by 6.1~6.6%. The gpt-oss 20B model achieves the highest overall performance, particularly on clinical reasoning tasks. The strong zero- and few-shot performance demonstrates better transferability of the shared prompt representation.", "date": "2026-04-08", "categories": []}
{"id": "2604.06758", "title": "Multilingual Cognitive Impairment Detection in the Era of Foundation Models", "abstract": "We evaluate cognitive impairment (CI) classification from transcripts of speech in English, Slovene, and Korean. We compare zero-shot large language models (LLMs) used as direct classifiers under three input settings -- transcript-only, linguistic-features-only, and combined -- with supervised tabular approaches trained under a leave-one-out protocol. The tabular models operate on engineered linguistic features, transcript embeddings, and early or late fusion of both modalities. Across languages, zero-shot LLMs provide competitive no-training baselines, but supervised tabular models generally perform better, particularly when engineered linguistic features are included and combined with embeddings. Few-shot experiments focusing on embeddings indicate that the value of limited supervision is language-dependent, with some languages benefiting substantially from additional labelled examples while others remain constrained without richer feature representations. Overall, the results suggest that, in small-data CI detection, structured linguistic signals and simple fusion-based classifiers remain strong and reliable signals.", "date": "2026-04-08", "categories": []}
{"id": "2604.07116", "title": "Yale-DM-Lab at ArchEHR-QA 2026: Deterministic Grounding and Multi-Pass Evidence Alignment for EHR Question Answering", "abstract": "We describe the Yale-DM-Lab system for the ArchEHR-QA 2026 shared task. The task studies patient-authored questions about hospitalization records and contains four subtasks (ST): clinician-interpreted question reformulation, evidence sentence identification, answer generation, and evidence-answer alignment. ST1 uses a dual-model pipeline with Claude Sonnet 4 and GPT-4o to reformulate patient questions into clinician-interpreted questions. ST2-ST4 rely on Azure-hosted model ensembles (o3, GPT-5.2, GPT-5.1, and DeepSeek-R1) combined with few-shot prompting and voting strategies. Our experiments show three main findings. First, model diversity and ensemble voting consistently improve performance compared to single-model baselines. Second, the full clinician answer paragraph is provided as additional prompt context for evidence alignment. Third, results on the development set show that alignment accuracy is mainly limited by reasoning. The best scores on the development set reach 88.81 micro F1 on ST4, 65.72 macro F1 on ST2, 34.01 on ST3, and 33.05 on ST1.", "date": "2026-04-08", "categories": []}
{"id": "2604.04168", "title": "A Semi-Automated Annotation Workflow for Paediatric Histopathology Reports Using Small Language Models", "abstract": "Electronic Patient Record (EPR) systems contain valuable clinical information, but much of it is trapped in unstructured text, limiting its use for research and decision-making. Large language models can extract such information but require substantial computational resources to run locally, and sending sensitive clinical data to cloud-based services, even when deidentified, raises significant patient privacy concerns. In this study, we develop a resource-efficient semi-automated annotation workflow using small language models (SLMs) to extract structured information from unstructured EPR data, focusing on paediatric histopathology reports. As a proof-of-concept, we apply the workflow to paediatric renal biopsy reports, a domain chosen for its constrained diagnostic scope and well-defined underlying biology. We develop the workflow iteratively with clinical oversight across three meetings, manually annotating 400 reports from a dataset of 2,111 at Great Ormond Street Hospital as a gold standard, while developing an automated information extraction approach using SLMs. We frame extraction as a Question-Answering task grounded by clinician-guided entity guidelines and few-shot examples, evaluating five instruction-tuned SLMs with a disagreement modelling framework to prioritise reports for clinical review. Gemma 2 2B achieves the highest accuracy at 84.3%, outperforming off-the-shelf models including spaCy (74.3%), BioBERT-SQuAD (62.3%), RoBERTa-SQuAD (59.7%), and GLiNER (60.2%). Entity guidelines improved performance by 7-19% over the zero-shot baseline, and few-shot examples by 6-38%, though their benefits do not compound when combined. These results demonstrate that SLMs can extract structured information from specialised clinical domains on CPU-only infrastructure with minimal clinician involvement. Our code is available at https://github.com/gosh-dre/nlp_renal_biopsy.", "date": "2026-04-05", "categories": []}
{"id": "2604.06028", "title": "A Multi-Stage Validation Framework for Trustworthy Large-scale Clinical Information Extraction using Large Language Models", "abstract": "Large language models (LLMs) show promise for extracting clinically meaningful information from unstructured health records, yet their translation into real-world settings is constrained by the lack of scalable and trustworthy validation approaches. Conventional evaluation methods rely heavily on annotation-intensive reference standards or incomplete structured data, limiting feasibility at population scale. We propose a multi-stage validation framework for LLM-based clinical information extraction that enables rigorous assessment under weak supervision. The framework integrates prompt calibration, rule-based plausibility filtering, semantic grounding assessment, targeted confirmatory evaluation using an independent higher-capacity judge LLM, selective expert review, and external predictive validity analysis to quantify uncertainty and characterize error modes without exhaustive manual annotation. We applied this framework to extraction of substance use disorder (SUD) diagnoses across 11 substance categories from 919,783 clinical notes. Rule-based filtering and semantic grounding removed 14.59% of LLM-positive extractions that were unsupported, irrelevant, or structurally implausible. For high-uncertainty cases, the judge LLM's assessments showed substantial agreement with subject matter expert review (Gwet's AC1=0.80). Using judge-evaluated outputs as references, the primary LLM achieved an F1 score of 0.80 under relaxed matching criteria. LLM-extracted SUD diagnoses also predicted subsequent engagement in SUD specialty care more accurately than structured-data baselines (AUC=0.80). These findings demonstrate that scalable, trustworthy deployment of LLM-based clinical information extraction is feasible without annotation-intensive evaluation.", "date": "2026-04-07", "categories": []}
{"id": "2603.01756", "title": "NeuroSymb-MRG: Differentiable Abductive Reasoning with Active Uncertainty Minimization for Radiology Report Generation", "abstract": "Automatic generation of radiology reports seeks to reduce clinician workload while improving documentation consistency. Existing methods that adopt encoder-decoder or retrieval-augmented pipelines achieve progress in fluency but remain vulnerable to visual-linguistic biases, factual inconsistency, and lack of explicit multi-hop clinical reasoning. We present NeuroSymb-MRG, a unified framework that integrates NeuroSymbolic abductive reasoning with active uncertainty minimization to produce structured, clinically grounded reports. The system maps image features to probabilistic clinical concepts, composes differentiable logic-based reasoning chains, decodes those chains into templated clauses, and refines the textual output via retrieval and constrained language-model editing. An active sampling loop driven by rule-level uncertainty and diversity guides clinician-in-the-loop adjudication and promptbook refinement. Experiments on standard benchmarks demonstrate consistent improvements in factual consistency and standard language metrics compared to representative baselines.", "date": "2026-03-02", "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": "2510.10415", "title": "CQA-Eval: Designing Reliable Evaluations of Multi-paragraph Clinical QA under Resource Constraints", "abstract": "Evaluating multi-paragraph clinical question answering (QA) systems is resource-intensive and challenging: accurate judgments require medical expertise and achieving consistent human judgments over multi-paragraph text is difficult. We introduce CQA-Eval, an evaluation framework and set of evaluation recommendations for limited-resource and high-expertise settings. Based on physician annotations of 300 real patient questions answered by physicians and LLMs, we compare coarse answer-level versus fine-grained sentence-level evaluation over the dimensions of correctness, relevance, and risk disclosure. We find that inter-annotator agreement (IAA) varies by dimension: fine-grained annotation improves agreement on correctness, coarse improves agreement on relevance, and judgments on communicates-risks remain inconsistent. Additionally, annotating only a small subset of sentences can provide reliability comparable to coarse annotations, reducing cost and effort.", "date": "2025-10-12", "categories": []}
{"id": "2604.02477", "title": "Guideline2Graph: Profile-Aware Multimodal Parsing for Executable Clinical Decision Graphs", "abstract": "Clinical practice guidelines are long, multimodal documents whose branching recommendations are difficult to convert into executable clinical decision support (CDS), and one-shot parsing often breaks cross-page continuity. Recent LLM/VLM extractors are mostly local or text-centric, under-specifying section interfaces and failing to consolidate cross-page control flow across full documents into one coherent decision graph. We present a decomposition-first pipeline that converts full-guideline evidence into an executable clinical decision graph through topology-aware chunking, interface-constrained chunk graph generation, and provenance-preserving global aggregation. Rather than relying on single-pass generation, the pipeline uses explicit entry/terminal interfaces and semantic deduplication to preserve cross-page continuity while keeping the induced control flow auditable and structurally consistent. We evaluate on an adjudicated prostate-guideline benchmark with matched inputs and the same underlying VLM backbone across compared methods. On the complete merged graph, our approach improves edge and triplet precision/recall from $19.6\\%/16.1\\%$ in existing models to $69.0\\%/87.5\\%$, while node recall rises from $78.1\\%$ to $93.8\\%$. These results support decomposition-first, auditable guideline-to-CDS conversion on this benchmark, while current evidence remains limited to one adjudicated prostate guideline and motivates broader multi-guideline validation.", "date": "2026-04-02", "categories": []}
{"id": "2604.02904", "title": "BioUNER: A Benchmark Dataset for Clinical Urdu Named Entity Recognition", "abstract": "In this article, we present a gold-standard benchmark dataset for Biomedical Urdu Named Entity Recognition (BioUNER), developed by crawling health-related articles from online Urdu news portals, medical prescriptions, and hospital health blogs and websites. After preprocessing, three native annotators with familiarity in the medical domain participated in the annotation process using the Doccano text annotation tool and annotated 153K tokens. Following annotation, the proposed BioiUNER dataset was evaluated both intrinsically and extrinsically. An inter-annotator agreement score of 0.78 was achieved, thereby validating the dataset as gold-standard quality. To demonstrate the utility and benchmarking capability of the dataset, we evaluated several machine learning and deep learning models, including Support Vector Machines (SVM), Long Short-Term Memory networks (LSTM), Multilingual BERT (mBERT), and XLM-RoBERTa. The gold-standard BioUNER dataset serves as a reliable benchmark and a valuable addition to Urdu language processing resources.", "date": "2026-04-03", "categories": []}
{"id": "2602.17194", "title": "What Makes a Good Doctor Response? A Study on Text-Based Telemedicine", "abstract": "Text-based telemedicine has become an increasingly used mode of care, requiring clinicians to deliver medical advice clearly and effectively in writing. As platforms increasingly rely on patient ratings and feedback, clinicians face growing pressure to maintain satisfaction scores, even though these evaluations often reflect communication quality more than clinical accuracy. We analyse patient satisfaction signals in Romanian text-based telemedicine. Using a sample of anonymised text-based telemedicine consultations, we model feedback as a binary outcome, treating thumbs-up responses as positive and grouping negative or absent feedback into the other class. We extract from doctor responses interpretable, predominantly language-agnostic features (e.g., length, structural characteristics, readability proxies), along with Romanian LIWC psycholinguistic features and politeness/hedging markers where available. We train a classifier with a time-based split and perform SHAP-based analyses, which indicate that metadata dominates prediction, functioning as a strong prior, while characteristics of the response text provide a smaller but actionable signal. In subgroup correlation analyses, politeness and hedging are consistently associated with positive patient feedback, whereas lexical diversity shows a negative association.", "date": "2026-02-19", "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": "2604.00027", "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": "2407.03004", "title": "SemioLLM: Evaluating Large Language Models for Diagnostic Reasoning from Unstructured Clinical Narratives in Epilepsy", "abstract": "Large Language Models (LLMs) have been shown to encode clinical knowledge. Many evaluations, however, rely on structured question-answer benchmarks, overlooking critical challenges of interpreting and reasoning about unstructured clinical narratives in real-world settings. In this study we task eight Large Language models including two medical models (GPT-3.5, GPT-4, Mixtral-8x7B, Qwen-72B, LlaMa2, LlaMa3, OpenBioLLM, Med42) with a core diagnostic task in epilepsy: mapping seizure description phrases, after targeted filtering and standardization, to one of seven possible seizure onset zones using likelihood estimates. Most models yield results that often match the ground truth and even approach clinician-level performance after prompt engineering. Specifically, clinician-guided chain-of-thought reasoning leading to the most consistent improvements. Performance was further strongly modulated by clinical in-context impersonation, narrative length and language context (13.7%, 32.7% and 14.2% performance variation, respectively). However, expert analysis of reasoning outputs revealed that correct prediction can be based on hallucinated knowledge and inaccurate source citation, underscoring the need to improve interpretability of LLMs in clinical use. Overall, SemioLLM provides a scalable, domain-adaptable framework for evaluating LLMs in clinical disciplines where unstructured verbal descriptions encode diagnostic information. By identifying both the strengths and limitations of LLMs, our work contributes to testing the applicability of foundational AI systems for healthcare.", "date": "2024-07-03", "categories": []}
{"id": "2511.16625", "title": "MedBayes-Lite: Bayesian Uncertainty Quantification for Safe Clinical Decision Support", "abstract": "We propose MedBayes-Lite, a lightweight Bayesian enhancement for transformer-based clinical language models that improves reliability through uncertainty-aware prediction. The framework operates without retraining, architectural modification, or additional trainable parameters, and integrates three components: Bayesian Embedding Calibration via Monte Carlo dropout, Uncertainty-Weighted Attention for reliability-aware token aggregation, and Confidence-Guided Decision Shaping for abstention under uncertainty. Across MedQA, PubMedQA, and MIMIC-III, MedBayes-Lite improves calibration and trustworthiness, reducing overconfidence by 32--48\\%. In simulated clinical settings, it further supports safer decision-making by flagging uncertain predictions for human review, particularly under distribution shift. For closed API models, the framework remains applicable through sampling-based predictive uncertainty and confidence-guided abstention, while full embedding- and attention-level uncertainty propagation is evaluated on open-weight transformer models.", "date": "2025-11-20", "categories": []}
{"id": "2512.10780", "title": "Script Gap: Evaluating LLM Triage on Indian Languages in Native vs Romanized Scripts in a Real World Setting", "abstract": "Large Language Models (LLMs) are increasingly deployed in high-stakes clinical applications in India. Speakers of Indian languages frequently communicate using romanized text rather than native scripts, yet existing research rarely quantifies or evaluates this orthographic variation in real world applications. We investigate how romanization impacts the reliability of LLMs in a critical domain: maternal and newborn healthcare triage. We benchmark leading LLMs on a real world dataset of user-generated health queries spanning five Indian languages and Nepali. Our results reveal consistent degradation in performance for romanized messages, with gap reaching up to 24 points across languages and models. We propose and evaluate an Uncertainty-based Selective Routing method to close this script gap. At our partner maternal health organization alone, this gap could cause nearly 2 million excess errors in triage. Our findings highlight a critical safety blind spot in LLM-based health systems: models that appear to understand romanized input may still fail to act on it reliably.", "date": "2025-12-11", "categories": []}
{"id": "2603.00314", "title": "When Metrics Disagree: Automatic Similarity vs. LLM-as-a-Judge for Clinical Dialogue Evaluation", "abstract": "As Large Language Models (LLMs) are increasingly integrated into healthcare to address complex inquiries, ensuring their reliability remains a critical challenge. Recent studies have highlighted that generic LLMs often struggle in clinical contexts, occasionally producing misleading guidance. To mitigate these risks, this research focuses on the domain-specific adaptation of \\textbf{Llama-2-7B} using the \\textbf{Low-Rank Adaptation (LoRA)} technique. By injecting trainable low-rank matrices into the Transformer layers, we efficiently adapted the model using authentic patient-physician transcripts while preserving the foundational knowledge of the base model. Our objective was to enhance precision and contextual relevance in responding to medical queries by capturing the specialized nuances of clinical discourse.\n  Due to the resource-intensive nature of large-scale human validation, the model's performance was evaluated through a dual-track framework: \\textbf{Track A} utilized traditional lexical similarity metrics (e.g., BLEU, ROUGE), while \\textbf{Track B} employed an \"LLM-as-a-Judge\" paradigm using GPT-4 for semantic assessment. Our results demonstrate that while the LoRA-enhanced model achieved significant improvements across all quantitative lexical dimensions, a profound disagreement surfaced in the GPT-4 evaluation, which marginally favored the baseline model's conversational flow. This metric divergence underscores a pivotal finding: traditional automated scores may not fully reflect clinical utility. Consequently, we propose that while automated metrics and LLM judges serve as valuable developmental proxies, rigorous validation by human medical experts remains an indispensable requirement for the safe deployment of LLMs in healthcare settings.", "date": "2026-02-27", "categories": []}
{"id": "2603.29366", "title": "AI-Generated Prior Authorization Letters: Strong Clinical Content, Weak Administrative Scaffolding", "abstract": "Prior authorization remains one of the most burdensome administrative processes in U.S. healthcare, consuming billions of dollars and thousands of physician hours each year. While large language models have shown promise across clinical text tasks, their ability to produce submission-ready prior authorization letters has received only limited attention, with existing work confined to single-case demonstrations rather than structured multi-scenario evaluation. We assessed three commercially available LLMs (GPT-4o, Claude Sonnet 4.5, and Gemini 2.5 Pro) across 45 physician-validated synthetic scenarios spanning rheumatology, psychiatry, oncology, cardiology, and orthopedics. All three models generated letters with strong clinical content: accurate diagnoses, well-structured medical necessity arguments, and thorough step therapy documentation. However, a secondary analysis of real-world administrative requirements revealed consistent gaps that clinical scoring alone did not capture, including absent billing codes, missing authorization duration requests, and inadequate follow-up plans. These findings reframe the question: the challenge for clinical deployment is not whether LLMs can write clinically adequate letters, but whether the systems built around them can supply the administrative precision that payer workflows require.", "date": "2026-03-31", "categories": []}
{"id": "2603.29709", "title": "Symphony for Medical Coding: A Next-Generation Agentic System for Scalable and Explainable Medical Coding", "abstract": "Medical coding translates free-text clinical documentation into standardized codes drawn from classification systems that contain tens of thousands of entries and are updated annually. It is central to billing, clinical research, and quality reporting, yet remains largely manual, slow, and error-prone. Existing automated approaches learn to predict a fixed set of codes from labeled data, thereby preventing adaptation to new codes or different coding systems without retraining on different data. They also provide no explanation for their predictions, limiting trust in safety-critical settings. We introduce Symphony for Medical Coding, a system that approaches the task the way expert human coders do: by reasoning over the clinical narrative with direct access to the coding guidelines. This design allows Symphony to operate across any coding system and to provide span-level evidence linking each predicted code to the text that supports it. We evaluate on two public benchmarks and three real-world datasets spanning inpatient, outpatient, emergency, and subspecialty settings across the United States and the United Kingdom. Symphony achieves state-of-the-art results across all settings, establishing itself as a flexible, deployment-ready foundation for automated clinical coding.", "date": "2026-03-31", "categories": []}
{"id": "2504.00780", "title": "Benchmarking NLP-supported Language Sample Analysis for Swiss Children's Speech", "abstract": "Language sample analysis (LSA) is a process that complements standardized psychometric tests for diagnosing, for example, developmental language disorder (DLD) in children. However, its labour-intensive nature has limited its use in speech-language pathology practice. We introduce an approach that leverages natural language processing (NLP) methods that do not rely on commercial large language models (LLMs) applied to transcribed speech data from 119 children in the German-speaking part of Switzerland with typical and atypical language development. This preliminary study aims to identify optimal practices that support speech-language pathologists in diagnosing DLD more efficiently with active involvement of human specialists. Preliminary findings underscore the potential of integrating locally deployed NLP methods into the process of semi-automatic LSA.", "date": "2025-04-01", "categories": []}
{"id": "2504.19467", "title": "BRIDGE: Benchmarking Large Language Models for Understanding Real-world Clinical Practice Text", "abstract": "Large language models (LLMs) hold great promise for medical applications and are evolving rapidly, with new models being released at an accelerated pace. However, benchmarking on large-scale real-world data such as electronic health records (EHRs) is critical, as clinical decisions are directly informed by these sources, yet current evaluations remain limited. Most existing benchmarks rely on medical exam-style questions or PubMed-derived text, failing to capture the complexity of real-world clinical data. Others focus narrowly on specific application scenarios, limiting their generalizability across broader clinical use. To address this gap, we present BRIDGE, a comprehensive multilingual benchmark comprising 87 tasks sourced from real-world clinical data sources across nine languages. It covers eight major task types spanning the entire continuum of patient care across six clinical stages and 20 representative applications, including triage and referral, consultation, information extraction, diagnosis, prognosis, and billing coding, and involves 14 clinical specialties. We systematically evaluated 95 LLMs (including DeepSeek-R1, GPT-4o, Gemini series, and Qwen3 series) under various inference strategies. Our results reveal substantial performance variation across model sizes, languages, natural language processing tasks, and clinical specialties. Notably, we demonstrate that open-source LLMs can achieve performance comparable to proprietary models, while medically fine-tuned LLMs based on older architectures often underperform versus updated general-purpose models. The BRIDGE and its corresponding leaderboard serve as a foundational resource and a unique reference for the development and evaluation of new LLMs in real-world clinical text understanding.\n  The BRIDGE leaderboard: https://huggingface.co/spaces/YLab-Open/BRIDGE-Medical-Leaderboard", "date": "2025-04-28", "categories": []}
{"id": "2506.04450", "title": "Learning to Diagnose Privately: DP-Powered LLMs for Radiology Report Classification", "abstract": "Large Language Models (LLMs) are increasingly adopted across domains such as education, healthcare, and finance. In healthcare, LLMs support tasks including disease diagnosis, abnormality classification, and clinical decision-making. Among these, multi-abnormality classification of radiology reports is critical for clinical workflow automation and biomedical research. Leveraging strong natural language processing capabilities, LLMs enable efficient processing of unstructured medical text and reduce the administrative burden of manual report analysis. To improve performance, LLMs are often fine-tuned on private, institution-specific datasets such as radiology reports. However, this raises significant privacy concerns: LLMs may memorize training data and become vulnerable to data extraction attacks, while sharing fine-tuned models risks exposing sensitive patient information. Despite growing interest in LLMs for medical text classification, privacy-preserving fine-tuning for multi-abnormality classification remains underexplored. To address this gap, we propose a differentially private (DP) fine-tuning framework for multi-abnormality classification from free-text radiology reports. Our approach integrates differential privacy with Low-Rank Adaptation (LoRA) to efficiently fine-tune LLMs on sensitive clinical data while mitigating leakage risks. We further employ labels generated by a larger LLM to train smaller models, enabling efficient inference under strong privacy guarantees. Experiments on MIMIC-CXR and CT-RATE demonstrate the effectiveness of our DP-LoRA framework across varying privacy regimes. On MIMIC-CXR, our method achieves weighted F1-scores up to 0.89 under moderate privacy budgets, approaching non-private LoRA (0.90) and full fine-tuning (0.96), confirming that strong privacy can be achieved with only modest performance trade-offs.", "date": "2025-06-04", "categories": []}
{"id": "2511.15211", "title": "OEMA: Ontology-Enhanced Multi-Agent Collaboration Framework for Zero-Shot Clinical Named Entity Recognition", "abstract": "With the rapid expansion of unstructured clinical texts in electronic health records (EHRs), clinical named entity recognition (NER) has become a crucial technique for extracting medical information. However, traditional supervised models such as CRF and BioClinicalBERT suffer from high annotation costs. Although zero-shot NER based on large language models (LLMs) reduces the dependency on labeled data, challenges remain in aligning example selection with task granularity and effectively integrating prompt design with self-improvement frameworks. To address these limitations, we propose OEMA, a novel zero-shot clinical NER framework based on multi-agent collaboration. OEMA consists of three core components: (1) a self-annotator that autonomously generates candidate examples; (2) a discriminator that leverages SNOMED CT to filter token-level examples by clinical relevance; and (3) a predictor that incorporates entity-type descriptions to enhance inference accuracy. Experimental results on two benchmark datasets, MTSamples and VAERS, demonstrate that OEMA achieves state-of-the-art performance under exact-match evaluation. Moreover, under related-match criteria, OEMA performs comparably to the supervised BioClinicalBERT model while significantly outperforming the traditional CRF method. OEMA improves zero-shot clinical NER, achieving near-supervised performance under related-match criteria. Future work will focus on continual learning and open-domain adaptation to expand its applicability in clinical NLP.", "date": "2025-11-19", "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": "2512.16145", "title": "MRG-R1: Reinforcement Learning for Clinically Aligned Medical Report Generation", "abstract": "Medical report generation aims to automatically produce radiology-style reports from medical images, supporting efficient and accurate clinical decision-making.However, existing approaches predominately rely on token-level likelihood training, which favors local lexical matching and leaves clinical correctness under-specified in the training objective. This behavior can be attributed to token-level likelihood optimization, which rewards surface-form agreement and therefore fails to directly encode constraints on medically accurate findings. To address this objective mismatch, we introduce a semantic-driven reinforcement learning (SRL) framework for medical report generation, named MRG-R1, which directly optimizes report-level clinical correctness rather than token-level likelihood. The key module is a clinically grounded report-level reward function, which reinforces semantic agreement in clinically relevant findings between generated and reference reports, thereby enabling learning signals that explicitly constrain medical correctness beyond surface linguistic alignment. Our evaluations show that the proposed framework improves the accuracy and coverage of clinically relevant findings in generated reports, and that MRG-R1 achieves state-of-the-art clinical efficacy on the IU X-Ray and MIMIC-CXR benchmark datasets.", "date": "2025-12-18", "categories": []}
{"id": "2603.10008", "title": "GATech at AbjadMed: Bidirectional Encoders vs. Causal Decoders: Insights from 82-Class Arabic Medical Classification", "abstract": "This paper presents system description for Arabic medical text classification across 82 distinct categories. Our primary architecture utilizes a fine-tuned AraBERTv2 encoder enhanced with a hybrid pooling strategies, combining attention and mean representations, and multi-sample dropout for robust regularization. We systematically benchmark this approach against a suite of multilingual and Arabic-specific encoders, as well as several large-scale causal decoders, including zero-shot re-ranking via Llama 3.3 70B and feature extraction from Qwen 3B hidden states. Our findings demonstrate that specialized bidirectional encoders significantly outperform causal decoders in capturing the precise semantic boundaries required for fine-grained medical text classification. We show that causal decoders, optimized for next-token prediction, produce sequence-biased embeddings that are less effective for categorization compared to the global context captured by bidirectional attention. Despite significant class imbalance and label noise identified within the training data, our results highlight the superior semantic compression of fine-tuned encoders for specialized Arabic NLP tasks. Final performance metrics on the test set, including Accuracy and Macro-F1, are reported and discussed.", "date": "2026-02-17", "categories": []}
{"id": "2603.26235", "title": "GS-BrainText: A Multi-Site Brain Imaging Report Dataset from Generation Scotland for Clinical Natural Language Processing Development and Validation", "abstract": "We present GS-BrainText, a curated dataset of 8,511 brain radiology reports from the Generation Scotland cohort, of which 2,431 are annotated for 24 brain disease phenotypes. This multi-site dataset spans five Scottish NHS health boards and includes broad age representation (mean age 58, median age 53), making it uniquely valuable for developing and evaluating generalisable clinical natural language processing (NLP) algorithms and tools. Expert annotations were performed by a multidisciplinary clinical team using an annotation schema, with 10-100% double annotation per NHS health board and rigorous quality assurance. Benchmark evaluation using EdIE-R, an existing rule-based NLP system developed in conjunction with the annotation schema, revealed some performance variation across health boards (F1: 86.13-98.13), phenotypes (F1: 22.22-100) and age groups (F1: 87.01-98.13), highlighting critical challenges in generalisation of NLP tools. The GS-BrainText dataset addresses a significant gap in available UK clinical text resources and provides a valuable resource for the study of linguistic variation, diagnostic uncertainty expression and the impact of data characteristics on NLP system performance.", "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": "2410.12476", "title": "Retrieval-Reasoning Large Language Model-based Synthetic Clinical Trial Generation", "abstract": "Machine learning (ML) holds great promise for clinical applications but is often hindered by limited access to high-quality data due to privacy concerns, high costs, and long timelines associated with clinical trials. While large language models (LLMs) have demonstrated strong performance in general-purpose generation tasks, their application to synthesizing realistic clinical trials remains underexplored. In this work, we propose a novel Retrieval-Reasoning framework that leverages few-shot prompting with LLMs to generate synthetic clinical trial reports annotated with binary success/failure outcomes. Our approach integrates a retrieval module to ground the generation on relevant trial data and a reasoning module to ensure domain-consistent justifications. Experiments conducted on real clinical trials from the ClinicalTrials.gov database demonstrate that the generated synthetic trials effectively augment real datasets. Fine-tuning a BioBERT classifier on synthetic data, real data, or their combination shows that hybrid fine-tuning leads to improved performance on clinical trial outcome prediction tasks. Our results suggest that LLM-based synthetic data can serve as a powerful tool for privacy-preserving data augmentation in clinical research. The code is available at https://github.com/XuZR3x/Retrieval_Reasoning_Clinical_Trial_Generation.", "date": "2024-10-16", "categories": []}
{"id": "2603.24651", "title": "When Consistency Becomes Bias: Interviewer Effects in Semi-Structured Clinical Interviews", "abstract": "Automatic depression detection from doctor-patient conversations has gained momentum thanks to the availability of public corpora and advances in language modeling. However, interpretability remains limited: strong performance is often reported without revealing what drives predictions. We analyze three datasets: ANDROIDS, DAIC-WOZ, E-DAIC and identify a systematic bias from interviewer prompts in semi-structured interviews. Models trained on interviewer turns exploit fixed prompts and positions to distinguish depressed from control subjects, often achieving high classification scores without using participant language. Restricting models to participant utterances distributes decision evidence more broadly and reflects genuine linguistic cues. While semi-structured protocols ensure consistency, including interviewer prompts inflates performance by leveraging script artifacts. Our results highlight a cross-dataset, architecture-agnostic bias and emphasize the need for analyses that localize decision evidence by time and speaker to ensure models learn from participants' language.", "date": "2026-03-25", "categories": []}
{"id": "2603.24772", "title": "Evaluating Fine-Tuned LLM Model For Medical Transcription With Small Low-Resource Languages Validated Dataset", "abstract": "Clinical documentation is a critical factor for patient safety, diagnosis, and continuity of care. The administrative burden of EHRs is a significant factor in physician burnout. This is a critical issue for low-resource languages, including Finnish. This study aims to investigate the effectiveness of a domain-aligned natural language processing (NLP); large language model for medical transcription in Finnish by fine-tuning LLaMA 3.1-8B on a small validated corpus of simulated clinical conversations by students at Metropolia University of Applied Sciences. The fine-tuning process for medical transcription used a controlled preprocessing and optimization approach. The fine-tuning effectiveness was evaluated by sevenfold cross-validation. The evaluation metrics for fine-tuned LLaMA 3.1-8B were BLEU = 0.1214, ROUGE-L = 0.4982, and BERTScore F1 = 0.8230. The results showed a low n-gram overlap but a strong semantic similarity with reference transcripts. This study indicate that fine-tuning can be an effective approach for translation of medical discourse in spoken Finnish and support the feasibility of fine-tuning a privacy-oriented domain-specific large language model for clinical documentation in Finnish. Beside that provide directions for future work.", "date": "2026-03-25", "categories": []}
{"id": "2602.23479", "title": "FHIRPath-QA: Executable Question Answering over FHIR Electronic Health Records", "abstract": "Though patients are increasingly granted digital access to their electronic health records (EHRs), existing interfaces may not support precise, trustworthy answers to patient-specific questions. Large language models (LLM) show promise in clinical question answering (QA), but retrieval-based approaches are computationally inefficient, prone to hallucination, and difficult to deploy over real-life EHRs. This work introduces FHIRPath-QA, the first open dataset and benchmark for patient-specific QA that includes open-standard FHIRPath queries over real-world clinical data. A text-to-FHIRPath QA paradigm is proposed that shifts reasoning from free-text generation to FHIRPath query synthesis. For o4-mini, this reduced average token usage by 391x relative to retrieval-first prompting (629,829 vs 1,609 tokens per question) and lowered failure rates from 0.36 to 0.09 on clinician-phrased questions. Built on MIMIC-IV on FHIR Demo, the dataset pairs over 14k natural language questions in patient and clinician phrasing with validated FHIRPath queries and answers. Empirically, the evaluated LLMs achieve at most 42% accuracy, highlighting the challenge of the task, but benefit strongly from supervised fine-tuning, with query synthesis accuracy improving from 27% to 79% for 4o-mini. These results highlight that text-to-FHIRPath synthesis has the potential to serve as a practical foundation for safe, efficient, and interoperable consumer health applications, and the FHIRPath-QA dataset and benchmark serve as a starting point for future research on the topic. The full dataset and generation code can be accessed at: https://github.com/mooshifrew/fhirpath-qa.", "date": "2026-02-26", "categories": []}
{"id": "2603.23513", "title": "Berta: an open-source, modular tool for AI-enabled clinical documentation", "abstract": "Commercial AI scribes cost \\$99-600 per physician per month, operate as opaque systems, and do not return data to institutional infrastructure, limiting organizational control over data governance, quality improvement, and clinical workflows. We developed Berta, an open-source modular scribe platform for AI-enabled clinical documentation, and deployed a customized implementation within Alberta Health Services (AHS) integrated with their existing Snowflake AI Data Cloud infrastructure. The system combines automatic speech recognition with large language models while retaining all clinical data within the secure AHS environment. During eight months (November 2024 to July 2025), 198 emergency physicians used the system in 105 urban and rural facilities, generating 22148 clinical sessions and more than 2800 hours of audio. The use grew from 680 to 5530 monthly sessions. Operating costs averaged less than \\$30 per physician per month, a 70-95% reduction compared to commercial alternatives. AHS has since approved expansion to 850 physicians. This is the first provincial-scale deployment of an AI scribe integrated with existing health system infrastructure. By releasing Berta as open source, we provide a reproducible, cost-effective alternative that health systems can adapt to their own secure environments, supporting data sovereignty and informed evaluation of AI documentation technology.", "date": "2026-03-05", "categories": []}
{"id": "2603.23515", "title": "Training a Large Language Model for Medical Coding Using Privacy-Preserving Synthetic Clinical Data", "abstract": "Improving the accuracy and reliability of medical coding reduces clinician burnout and supports revenue cycle processes, freeing providers to focus more on patient care. However, automating the assignment of ICD-10-CM and CPT codes from clinical documentation remains a challenge due to heterogeneous records, nuanced coding guidelines, and long-tail distributions. Large language models have been proposed to help or automate specific medical coding tasks. However, foundation models are not explicitly trained for medical coding and zero-shot coding has yielded poor results. We investigate whether a modern open-weight foundation model can be adapted for an expert-level medical coding task using privacy-preserving synthetic training data derived from electronic health records. We fine-tune Llama 3-70B on pairs of clinical notes and gold codes generated from EHR-grounded templates and coding policies, then evaluate exact-code prediction for ICD-10-CM and CPT. A zero-shot baseline with the unadapted model achieved an F1 score of 0.18 for exact code match. After fine-tuning on the synthetic corpus, exact-match F1 exceeded 0.70, representing a large absolute gain across both code systems. Notably, performance remained high on complex categories that often require multi-step clinical reasoning and code composition, including Advanced Illness and Frailty classes, and the model retained its performance on medical comprehension tasks. These results indicate that synthetic, policy-aware data can efficiently teach a general-purpose large language model to support precise medical coding without exposing protected health information. The approach offers a practical path for training coding agents safely and iteratively on specific tasks that represent real-world populations.", "date": "2026-03-06", "categories": []}
{"id": "2603.23678", "title": "PLACID: Privacy-preserving Large language models for Acronym Clinical Inference and Disambiguation", "abstract": "Large Language Models (LLMs) offer transformative solutions across many domains, but healthcare integration is hindered by strict data privacy constraints. Clinical narratives are dense with ambiguous acronyms, misinterpretation these abbreviations can precipitate severe outcomes like life-threatening medication errors. While cloud-dependent LLMs excel at Acronym Disambiguation, transmitting Protected Health Information to external servers violates privacy frameworks. To bridge this gap, this study pioneers the evaluation of small-parameter models deployed entirely on-device to ensure privacy preservation. We introduce a privacy-preserving cascaded pipeline leveraging general-purpose local models to detect clinical acronyms, routing them to domain-specific biomedical models for context-relevant expansions. Results reveal that while general instruction-following models achieve high detection accuracy (~0.988), their expansion capabilities plummet (~0.655). Our cascaded approach utilizes domain-specific medical models to increase expansion accuracy to (~0.81). This novel work demonstrates that privacy-preserving, on-device (2B-10B) models deliver high-fidelity clinical acronym disambiguation support.", "date": "2026-03-24", "categories": []}
{"id": "2603.21335", "title": "TimeTox: An LLM-Based Pipeline for Automated Extraction of Time Toxicity from Clinical Trial Protocols", "abstract": "Time toxicity, the cumulative healthcare contact days from clinical trial participation, is an important but labor-intensive metric to extract from protocol documents. We developed TimeTox, an LLM-based pipeline for automated extraction of time toxicity from Schedule of Assessments tables. TimeTox uses Google's Gemini models in three stages: summary extraction from full-length protocol PDFs, time toxicity quantification at six cumulative timepoints for each treatment arm, and multi-run consensus via position-based arm matching. We validated against 20 synthetic schedules (240 comparisons) and assessed reproducibility on 644 real-world oncology protocols. Two architectures were compared: single-pass (vanilla) and two-stage (structure-then-count). The two-stage pipeline achieved 100% clinically acceptable accuracy ($\\pm$3 days) on synthetic data (MAE 0.81 days) versus 41.5% for vanilla (MAE 9.0 days). However, on real-world protocols, the vanilla pipeline showed superior reproducibility: 95.3% clinically acceptable accuracy (IQR $\\leq$ 3 days) across 3 runs on 644 protocols, with 82.0% perfect stability (IQR = 0). The production pipeline extracted time toxicity for 1,288 treatment arms across multiple disease sites. Extraction stability on real-world data, rather than accuracy on synthetic benchmarks, is the decisive factor for production LLM deployment.", "date": "2026-03-22", "categories": []}
{"id": "2603.22625", "title": "Leveraging Large Language Models to Extract and Translate Medical Information in Doctors' Notes for Health Records and Diagnostic Billing Codes", "abstract": "Physician burnout in the United States has reached critical levels, driven in part by the administrative burden of Electronic Health Record (EHR) documentation and complex diagnostic codes. To relieve this strain and maintain strict patient privacy, this thesis explores an on-device, offline automatic medical coding system. The work focuses on using open-weight Large Language Models (LLMs) to extract clinical information from physician notes and translate it into ICD-10-CM diagnostic codes without reliance on cloud-based services.\n  A privacy-focused pipeline was developed using Ollama, LangChain, and containerized environments to evaluate multiple open-weight models, including Llama 3.2, Mistral, Phi, and DeepSeek, on consumer-grade hardware. Model performance was assessed for zero-shot, few-shot, and retrieval-augmented generation (RAG) prompting strategies using a novel benchmark of synthetic medical notes.\n  Results show that strict JSON schema enforcement achieved near 100% formatting compliance, but accurate generation of specific diagnostic codes remains challenging for smaller local models (7B-20B parameters). Contrary to common prompt-engineering guidance, few-shot prompting degraded performance through overfitting and hallucinations. While RAG enabled limited discovery of unseen codes, it frequently saturated context windows, reducing overall accuracy. The findings suggest that fully automated unsupervised coding with local open-source models is not yet reliable; instead, a human-in-the-loop assisted coding approach is currently the most practical path forward. This work contributes a reproducible local LLM architecture and benchmark dataset for privacy-preserving medical information extraction and coding.", "date": "2026-01-14", "categories": []}
{"id": "2603.22642", "title": "Multi-Method Validation of Large Language Model Medical Translation Across High- and Low-Resource Languages", "abstract": "Language barriers affect 27.3 million U.S. residents with non-English language preference, yet professional medical translation remains costly and often unavailable. We evaluated four frontier large language models (GPT-5.1, Claude Opus 4.5, Gemini 3 Pro, Kimi K2) translating 22 medical documents into 8 languages spanning high-resource (Spanish, Chinese, Russian, Vietnamese), medium-resource (Korean, Arabic), and low-resource (Tagalog, Haitian Creole) categories using a five-layer validation framework. Across 704 translation pairs, all models achieved high semantic preservation (LaBSE greater than 0.92), with no significant difference between high- and low-resource languages (p = 0.066). Cross-model back-translation confirmed results were not driven by same-model circularity (delta = -0.0009). Inter-model concordance across four independently trained models was high (LaBSE: 0.946), and lexical borrowing analysis showed no correlation between English term retention and fidelity scores in low-resource languages (rho = +0.018, p = 0.82). These converging results suggest frontier LLMs preserve medical meaning across resource levels, with implications for language access in healthcare.", "date": "2026-03-23", "categories": []}
{"id": "2603.22752", "title": "CLiGNet: Clinical Label-Interaction Graph Network for Medical Specialty Classification from Clinical Transcriptions", "abstract": "Automated classification of clinical transcriptions into medical specialties is essential for routing, coding, and clinical decision support, yet prior work on the widely used MTSamples benchmark suffers from severe data leakage caused by applying SMOTE oversampling before train test splitting. We first document this methodological flaw and establish a leakage free benchmark across 40 medical specialties (4966 records), revealing that the true task difficulty is substantially higher than previously reported.\n  We then introduce CLiGNet (Clinical Label Interaction Graph Network), a neural architecture that combines a Bio ClinicalBERT text encoder with a two layer Graph Convolutional Network operating on a specialty label graph constructed from semantic similarity and ICD 10 chapter priors. Per label attention gates fuse document and label graph representations, trained with focal binary cross entropy loss to handle extreme class imbalance (181 to 1 ratio).\n  Across seven baselines ranging from TF IDF classifiers to Clinical Longformer, CLiGNet without calibration achieves the highest macro F1 of 0.279, with an ablation study confirming that the GCN label graph provides the single largest component gain (increase of 0.066 macro F1). Adding per label Platt scaling calibration yields an expected calibration error of 0.007, demonstrating a principled trade off between ranking performance and probability reliability.\n  We provide comprehensive failure analysis covering pairwise specialty confusions, rare class behaviour, document length effects, and token level Integrated Gradients attribution, offering actionable insights for clinical NLP system deployment.", "date": "2026-03-24", "categories": []}
{"id": "2603.22820", "title": "RadTimeline: Timeline Summarization for Longitudinal Radiological Lung Findings", "abstract": "Tracking findings in longitudinal radiology reports is crucial for accurately identifying disease progression, and the time-consuming process would benefit from automatic summarization. This work introduces a structured summarization task, where we frame longitudinal report summarization as a timeline generation task, with dated findings organized in columns and temporally related findings grouped in rows. This structured summarization format enables straightforward comparison of findings across time and facilitates fact-checking against the associated reports. The timeline is generated using a 3-step LLM process of extracting findings, generating group names, and using the names to group the findings. To evaluate such systems, we create RadTimeline, a timeline dataset focused on tracking lung-related radiologic findings in chest-related imaging reports. Experiments on RadTimeline show tradeoffs of different-sized LLMs and prompting strategies. Our results highlight that group name generation as an intermediate step is critical for effective finding grouping. The best configuration has some irrelevant findings but very good recall, and grouping performance is comparable to human annotators.", "date": "2026-03-24", "categories": []}
{"id": "2603.22935", "title": "Ran Score: a LLM-based Evaluation Score for Radiology Report Generation", "abstract": "Chest X-ray report generation and automated evaluation are limited by poor recognition of low-prevalence abnormalities and inadequate handling of clinically important language, including negation and ambiguity. We develop a clinician-guided framework combining human expertise and large language models for multi-label finding extraction from free-text chest X-ray reports and use it to define Ran Score, a finding-level metric for report evaluation. Using three non-overlapping MIMIC-CXR-EN cohorts from a public chest X-ray dataset and an independent ChestX-CN validation cohort, we optimize prompts, establish radiologist-derived reference labels and evaluate report generation models. The optimized framework improves the macro-averaged score from 0.753 to 0.956 on the MIMIC-CXR-EN development cohort, exceeds the CheXbert benchmark by 15.7 percentage points on directly comparable labels, and shows robust generalization on the ChestX-CN validation cohort. Here we show that clinician-guided prompt optimization improves agreement with a radiologist-derived reference standard and that Ran Score enables finding-level evaluation of report fidelity, particularly for low-prevalence abnormalities.", "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": "2511.10065", "title": "RadHiera: Semantic Hierarchical Reinforcement Learning for Medical Report Generation", "abstract": "Vision-language models have shown promising results in radiology report generation. However, most existing methods generate reports as flat text and do not explicitly model the semantic dependency between the Findings and Impression sections, which can lead to inconsistencies between clinical observations and diagnostic conclusions. In this paper, we propose RadHiera, a semantic hierarchical reinforcement learning framework for radiology report generation. RadHiera follows the semantic organization of radiology reports by first optimizing overall report quality, then improving the diagnostic accuracy of the Impression section, and finally enforcing consistency between Findings and Impression so that diagnostic conclusions are supported by clinical evidence. Specifically, we begin with a base reward that combines linguistic quality and medical factuality to provide supervision on the whole report. On this basis, we introduce a severity-aware reward for the Impression section that places greater emphasis on errors involving clinically critical conditions, thereby reducing both missed diagnoses and overstatement. We further enforce cross-section consistency using Expert Model-derived label sets, with subset constraints and hallucination penalties to ensure that impressions remain faithful to the findings. Experiments on three public chest X-ray benchmarks show that RadHiera consistently improves diagnostic accuracy and inter-section consistency over state-of-the-art methods, while also demonstrating good adaptability to report generation in ultrasound report generation.", "date": "2025-11-13", "categories": []}
{"id": "2512.19735", "title": "Improving Fairness of Large Language Model-Based ICU Mortality Prediction via Case-Based Prompting", "abstract": "Accurately predicting mortality risk in intensive care unit (ICU) patients is essential for clinical decision-making. Although large language models (LLMs) show strong potential in structured medical prediction tasks, their outputs may exhibit biases related to demographic attributes such as sex, age, and race, limiting their reliability in fairness-critical clinical settings. Existing debiasing methods often degrade predictive performance, making it difficult to balance fairness and accuracy.\n  In this study, we systematically analyze fairness issues in LLM-based ICU mortality prediction and propose a clinically adaptive prompting framework that improves both performance and fairness without model retraining. We first design a multi-dimensional bias assessment scheme to identify subgroup disparities. Based on this, we introduce CAse Prompting (CAP), a training-free framework that integrates existing debiasing strategies and further guides models using similar historical misprediction cases paired with correct outcomes to correct biased reasoning.\n  We evaluate CAP on the MIMIC-IV dataset. Results show that AUROC improves from 0.806 to 0.873 and AUPRC from 0.497 to 0.694. Meanwhile, prediction disparities are substantially reduced across demographic groups, with reductions exceeding 90% in sex and certain White-Black comparisons. Feature reliance analysis further reveals highly consistent attention patterns across groups, with similarity above 0.98.\n  These findings demonstrate that fairness and performance in LLM-based clinical prediction can be jointly optimized through carefully designed prompting, offering a practical paradigm for developing reliable and equitable clinical decision-support systems.", "date": "2025-12-17", "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": "2603.20494", "title": "PARHAF, a human-authored corpus of clinical reports for fictitious patients in French", "abstract": "The development of clinical natural language processing (NLP) systems is severely hampered by the sensitive nature of medical records, which restricts data sharing under stringent privacy regulations, particularly in France and the broader European Union. To address this gap, we introduce PARHAF, a large open-source corpus of clinical documents in French. PARHAF comprises expert-authored clinical reports describing realistic yet entirely fictitious patient cases, making it anonymous and freely shareable by design. The corpus was developed using a structured protocol that combined clinician expertise with epidemiological guidance from the French National Health Data System (SNDS), ensuring broad clinical coverage. A total of 104 medical residents across 18 specialties authored and peer-reviewed the reports following predefined clinical scenarios and document templates.\n  The corpus contains 7394 clinical reports covering 5009 patient cases across a wide range of medical and surgical specialties. It includes a general-purpose component designed to approximate real-world hospitalization distributions, and four specialized subsets that support information-extraction use cases in oncology, infectious diseases, and diagnostic coding. Documents are released under a CC-BY open license, with a portion temporarily embargoed to enable future benchmarking under controlled conditions.\n  PARHAF provides a valuable resource for training and evaluating French clinical language models in a fully privacy-preserving setting, and establishes a replicable methodology for building shareable synthetic clinical corpora in other languages and health systems.", "date": "2026-03-20", "categories": []}
{"id": "2603.21529", "title": "SynSym: A Synthetic Data Generation Framework for Psychiatric Symptom Identification", "abstract": "Psychiatric symptom identification on social media aims to infer fine-grained mental health symptoms from user-generated posts, allowing a detailed understanding of users' mental states. However, the construction of large-scale symptom-level datasets remains challenging due to the resource-intensive nature of expert labeling and the lack of standardized annotation guidelines, which in turn limits the generalizability of models to identify diverse symptom expressions from user-generated text. To address these issues, we propose SynSym, a synthetic data generation framework for constructing generalizable datasets for symptom identification. Leveraging large language models (LLMs), SynSym constructs high-quality training samples by (1) expanding each symptom into sub-concepts to enhance the diversity of generated expressions, (2) producing synthetic expressions that reflect psychiatric symptoms in diverse linguistic styles, and (3) composing realistic multi-symptom expressions, informed by clinical co-occurrence patterns. We validate SynSym on three benchmark datasets covering different styles of depressive symptom expression. Experimental results demonstrate that models trained solely on the synthetic data generated by SynSym perform comparably to those trained on real data, and benefit further from additional fine-tuning with real data. These findings underscore the potential of synthetic data as an alternative resource to real-world annotations in psychiatric symptom modeling, and SynSym serves as a practical framework for generating clinically relevant and realistic symptom expressions.", "date": "2026-03-23", "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": "2503.21800", "title": "ELM: A Hybrid Ensemble of Language Models for Automated Tumor Group Classification in Population-Based Cancer Registries", "abstract": "Background: Population-based cancer registries (PBCRs) manually extract data from unstructured pathology reports, a labor-intensive process where assigning reports to tumor groups can consume 900 person-hours annually for approximately 100,000 reports at a medium-sized registry. Current automated rule-based systems fail to handle the linguistic complexity of this classification task.\n  Materials and Methods: We present ELM (Ensemble of Language Models), a novel hybrid approach combining small, encoder only language models and large language models (LLMs). ELM employs an ensemble of six fine-tuned encoder only models: three analyzing the top portion and three analyzing the bottom portion of each report to maximize text coverage given token limits. A tumor group is assigned when at least five of six models agree; otherwise, an LLM arbitrates using a carefully curated prompt constrained to likely tumor groups.\n  Results: On a held-out test set of 2,058 pathology reports spanning 19 tumor groups, ELM achieves weighted precision and recall of 0.94, representing a statistically significant improvement (p<0.001) over encoder-only ensembles (0.91 F1-score) and substantially outperforming rule-based approaches. ELM demonstrates particular gains for challenging categories including leukemia (F1: 0.76 to 0.88), lymphoma (0.76 to 0.89), and skin cancer (0.44 to 0.58).\n  Discussion: Deployed in production at British Columbia Cancer Registry, ELM has reduced manual review requirements by approximately 60-70%, saving an estimated 900 person-hours annually while maintaining data quality standards.\n  Conclusion: ELM represents the first successful deployment of a hybrid small, encoder only models-LLM architecture for tumor group classification in a real-world PBCR setting, demonstrating how strategic combination of language models can achieve both high accuracy and operational efficiency.", "date": "2025-03-24", "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": "2603.18327", "title": "Consumer-to-Clinical Language Shifts in Ambient AI Draft Notes and Clinician-Finalized Documentation: A Multi-level Analysis", "abstract": "Ambient AI generates draft clinical notes from patient-clinician conversations, often using lay or consumer-oriented phrasing to support patient understanding instead of standardized clinical terminology. How clinicians revise these drafts for professional documentation conventions remains unclear. We quantified clinician editing for consumer-to- clinical normalization using a dictionary-confirmed transformation framework. We analyzed 71,173 AI-draft and finalized-note section pairs from 34,726 encounters. Confirmed transformations were defined as replacing a consumer expression with its dictionary-mapped clinical equivalent in the same section. Editing significantly reduced terminology density across all sections (p < 0.001). The Assessment and Plan accounted for the largest transformation volume (59.3%). Our analysis identified 7,576 transformation events across 4,114 note sections (5.8%), representing 1.2% consumer-term deletions. Transformation intensity varied across individual clinicians (p < 0.001). Overall, clinician post-editing demonstrates consistent shifts from conversational phrasing toward standardized, section- appropriate clinical terminology, supporting section-aware ambient AI design.", "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": "2510.26969", "title": "Frame Semantic Patterns for Identifying Underreporting of Notifiable Events in Healthcare: The Case of Gender-Based Violence", "abstract": "We introduce a methodology for the identification of notifiable events in the domain of healthcare. The methodology harnesses semantic frames to define fine-grained patterns and search them in unstructured data, namely, open-text fields in e-medical records. We apply the methodology to the problem of underreporting of gender-based violence (GBV) in e-medical records produced during patients' visits to primary care units. A total of eight patterns are defined and searched on a corpus of 21 million sentences in Brazilian Portuguese extracted from e-SUS APS. The results are manually evaluated by linguists and the precision of each pattern measured. Our findings reveal that the methodology effectively identifies reports of violence with a precision of 0.726, confirming its robustness. Designed as a transparent, efficient, low-carbon, and language-agnostic pipeline, the approach can be easily adapted to other health surveillance contexts, contributing to the broader, ethical, and explainable use of NLP in public health systems.", "date": "2025-10-30", "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": "2603.17234", "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": "2603.00917", "title": "Prompt Sensitivity and Answer Consistency of Small Open-Source Language Models for Clinical Question Answering in Low-Resource Healthcare", "abstract": "Small open-source language models are gaining attention for healthcare applications in low-resource settings where cloud infrastructure and GPU hardware may be unavailable. However, the reliability of these models under different phrasings of the same clinical question remains poorly understood. We evaluate five open-source models (Gemma 2 2B, Phi-3 Mini 3.8B, Llama 3.2 3B, Mistral 7B, and Meditron-7B, a domain-pretrained model without instruction tuning) across three clinical question answering datasets (MedQA, MedMCQA, and PubMedQA) using five prompt styles: original, formal, simplified, roleplay, and direct. Model behavior is evaluated using consistency scores, accuracy, and instruction-following failure rates. All experiments were conducted locally on consumer CPU hardware without fine-tuning.\n  Consistency and accuracy were largely independent across models. Gemma 2 achieved the highest consistency (0.845-0.888) but the lowest accuracy (33.0-43.5%), while Llama 3.2 showed moderate consistency (0.774-0.807) alongside the highest accuracy (49.0-65.0%). Roleplay prompts consistently reduced accuracy across all models, with Phi-3 Mini dropping 21.5 percentage points on MedQA. Meditron-7B exhibited near-complete instruction-following failure on PubMedQA (99.0% UNKNOWN rate), indicating that domain pretraining alone is insufficient for structured clinical question answering.\n  These findings show that high consistency does not imply correctness: models can be reliably wrong, a dangerous failure mode in clinical AI. Llama 3.2 demonstrated the strongest balance of accuracy and reliability for low-resource deployment. Safe clinical AI requires joint evaluation of consistency, accuracy, and instruction adherence.", "date": "2026-03-01", "categories": []}
{"id": "2603.16002", "title": "RadAnnotate: Large Language Models for Efficient and Reliable Radiology Report Annotation", "abstract": "Radiology report annotation is essential for clinical NLP, yet manual labeling is slow and costly. We present RadAnnotate, an LLM-based framework that studies retrieval-augmented synthetic reports and confidence-based selective automation to reduce expert effort for labeling in RadGraph. We study RadGraph-style entity labeling (graph nodes) and leave relation extraction (edges) to future work. First, we train entity-specific classifiers on gold-standard reports and characterize their strengths and failure modes across anatomy and observation categories, with uncertain observations hardest to learn. Second, we generate RAG-guided synthetic reports and show that synthetic-only models remain within 1-2 F1 points of gold-trained models, and that synthetic augmentation is especially helpful for uncertain observations in a low-resource setting, improving F1 from 0.61 to 0.70. Finally, by learning entity-specific confidence thresholds, RadAnnotate can automatically annotate 55-90% of reports at 0.86-0.92 entity match score while routing low-confidence cases for expert review.", "date": "2026-03-16", "categories": []}
{"id": "2505.03025", "title": "A Typology of Synthetic Datasets for Dialogue Processing in Clinical Contexts", "abstract": "Synthetic data sets are used across linguistic domains and NLP tasks, particularly in scenarios where authentic data is limited (or even non-existent). One such domain is that of clinical (healthcare) contexts, where there exist significant and long-standing challenges (e.g., privacy, anonymization, and data governance) which have led to the development of an increasing number of synthetic datasets. One increasingly important category of clinical dataset is that of clinical dialogues which are especially sensitive and difficult to collect, and as such are commonly synthesized.\n  While such synthetic datasets have been shown to be sufficient in some situations, little theory exists to inform how they may be best used and generalized to new applications. In this paper, we provide an overview of how synthetic datasets are created, evaluated and being used for dialogue related tasks in the medical domain. Additionally, we propose a novel typology for use in classifying types and degrees of data synthesis, to facilitate comparison and evaluation.", "date": "2025-05-05", "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": "2603.08879", "title": "MultiGraSCCo: A Multilingual Anonymization Benchmark with Annotations of Personal Identifiers", "abstract": "Accessing sensitive patient data for machine learning is challenging due to privacy concerns. Datasets with annotations of personally identifiable information are crucial for developing and testing anonymization systems to enable safe data sharing that complies with privacy regulations. Since accessing real patient data is a bottleneck, synthetic data offers an efficient solution for data scarcity, bypassing privacy regulations that apply to real data. Moreover, neural machine translation can help to create high-quality data for low-resource languages by translating validated real or synthetic data from a high-resource language. In this work, we create a multilingual anonymization benchmark in ten languages, using a machine translation methodology that preserves the original annotations and renders names of cities and people in a culturally and contextually appropriate form in each target language. Our evaluation study with medical professionals confirms the quality of the translations, both in general and with respect to the translation and adaptation of personal information. Our benchmark with over 2,500 annotations of personal information can be used in many applications, including training annotators, validating annotations across institutions without legal complications, and helping improve the performance of automatic personal information detection. We make our benchmark and annotation guidelines available for further research.", "date": "2026-03-09", "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": "2603.14183", "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": "2208.10228", "title": "Review of Natural Language Processing in Pharmacology", "abstract": "Natural language processing (NLP) is an area of artificial intelligence that applies information technologies to process the human language, understand it to a certain degree, and use it in various applications. This area has rapidly developed in the last few years and now employs modern variants of deep neural networks to extract relevant patterns from large text corpora. The main objective of this work is to survey the recent use of NLP in the field of pharmacology. As our work shows, NLP is a highly relevant information extraction and processing approach for pharmacology. It has been used extensively, from intelligent searches through thousands of medical documents to finding traces of adversarial drug interactions in social media. We split our coverage into five categories to survey modern NLP methodology, commonly addressed tasks, relevant textual data, knowledge bases, and useful programming libraries. We split each of the five categories into appropriate subcategories, describe their main properties and ideas, and summarize them in a tabular form. The resulting survey presents a comprehensive overview of the area, useful to practitioners and interested observers.", "date": "2022-08-22", "categories": []}
{"id": "2603.12343", "title": "LLM-Augmented Therapy Normalization and Aspect-Based Sentiment Analysis for Treatment-Resistant Depression on Reddit", "abstract": "Treatment-resistant depression (TRD) is a severe form of major depressive disorder in which patients do not achieve remission despite multiple adequate treatment trials. Evidence across pharmacologic options for TRD remains limited, and trials often do not fully capture patient-reported tolerability. Large-scale online peer-support narratives therefore offer a complementary lens on how patients describe and evaluate medications in real-world use. In this study, we curated a corpus of 5,059 Reddit posts explicitly referencing TRD from 3,480 subscribers across 28 mental health-related subreddits from 2010 to 2025. Of these, 3,839 posts mentioned at least one medication, yielding 23,399 mentions of 81 generic-name medications after lexicon-based normalization of brand names, misspellings, and colloquialisms. We developed an aspect-based sentiment classifier by fine-tuning DeBERTa-v3 on the SMM4H 2023 therapy-sentiment Twitter corpus with large language model based data augmentation, achieving a micro-F1 score of 0.800 on the shared-task test set. Applying this classifier to Reddit, we quantified sentiment toward individual medications across three categories: positive, neutral, and negative, and tracked patterns by drug, subscriber, subreddit, and year. Overall, 72.1% of medication mentions were neutral, 14.8% negative, and 13.1% positive. Conventional antidepressants, especially SSRIs and SNRIs, showed consistently higher negative than positive proportions, whereas ketamine and esketamine showed comparatively more favorable sentiment profiles. These findings show that normalized medication extraction combined with aspect-based sentiment analysis can help characterize patient-perceived treatment experiences in TRD-related Reddit discourse, complementing clinical evidence with large-scale patient-generated perspectives.", "date": "2026-03-12", "categories": []}
{"id": "2410.09076", "title": "Llettuce: An Open Source Natural Language Processing Tool for the Translation of Medical Terms into Uniform Clinical Encoding", "abstract": "This paper introduces Llettuce, an open-source tool designed to address the complexities of converting medical terms into OMOP standard concepts. Unlike existing solutions such as the Athena database search and Usagi, which struggle with semantic nuances and require substantial manual input, Llettuce leverages advanced natural language processing, including large language models and fuzzy matching, to automate and enhance the mapping process. Developed with a focus on GDPR compliance, Llettuce can be deployed locally, ensuring data protection while maintaining high performance in converting informal medical terms to standardised concepts.", "date": "2024-10-04", "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": "2603.11597", "title": "Performance Evaluation of Open-Source Large Language Models for Assisting Pathology Report Writing in Japanese", "abstract": "The performance of large language models (LLMs) for supporting pathology report writing in Japanese remains unexplored. We evaluated seven open-source LLMs from three perspectives: (A) generation and information extraction of pathology diagnosis text following predefined formats, (B) correction of typographical errors in Japanese pathology reports, and (C) subjective evaluation of model-generated explanatory text by pathologists and clinicians. Thinking models and medical-specialized models showed advantages in structured reporting tasks that required reasoning and in typo correction. In contrast, preferences for explanatory outputs varied substantially across raters. Although the utility of LLMs differed by task, our findings suggest that open-source LLMs can be useful for assisting Japanese pathology report writing in limited but clinically relevant scenarios.", "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": "2603.08935", "title": "PathoScribe: Transforming Pathology Data into a Living Library with a Unified LLM-Driven Framework for Semantic Retrieval and Clinical Integration", "abstract": "Pathology underpins modern diagnosis and cancer care, yet its most valuable asset, the accumulated experience encoded in millions of narrative reports, remains largely inaccessible. Although institutions are rapidly digitizing pathology workflows, storing data without effective mechanisms for retrieval and reasoning risks transforming archives into a passive data repository, where institutional knowledge exists but cannot meaningfully inform patient care. True progress requires not only digitization, but the ability for pathologists to interrogate prior similar cases in real time while evaluating a new diagnostic dilemma. We present PathoScribe, a unified retrieval-augmented large language model (LLM) framework designed to transform static pathology archives into a searchable, reasoning-enabled living library. PathoScribe enables natural language case exploration, automated cohort construction, clinical question answering, immunohistochemistry (IHC) panel recommendation, and prompt-controlled report transformation within a single architecture. Evaluated on 70,000 multi-institutional surgical pathology reports, PathoScribe achieved perfect Recall@10 for natural language case retrieval and demonstrated high-quality retrieval-grounded reasoning (mean reviewer score 4.56/5). Critically, the system operationalized automated cohort construction from free-text eligibility criteria, assembling research-ready cohorts in minutes (mean 9.2 minutes) with 91.3% agreement to human reviewers and no eligible cases incorrectly excluded, representing orders-of-magnitude reductions in time and cost compared to traditional manual chart review. This work establishes a scalable foundation for converting digital pathology archives from passive storage systems into active clinical intelligence platforms.", "date": "2026-03-09", "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": "2603.10494", "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": "2603.10560", "title": "PET-F2I: A Comprehensive Benchmark and Parameter-Efficient Fine-Tuning of LLMs for PET/CT Report Impression Generation", "abstract": "PET/CT imaging is pivotal in oncology and nuclear medicine, yet summarizing complex findings into precise diagnostic impressions is labor-intensive. While LLMs have shown promise in medical text generation, their capability in the highly specialized domain of PET/CT remains underexplored. We introduce PET-F2I-41K (PET Findings-to-Impression Benchmark), a large-scale benchmark for PET/CT impression generation using LLMs, constructed from over 41k real-world reports. Using PET-F2I-41K, we conduct a comprehensive evaluation of 27 models across proprietary frontier LLMs, open-source generalist models, and medical-domain LLMs, and we develop a domain-adapted 7B model (PET-F2I-7B) fine-tuned from Qwen2.5-7B-Instruct via LoRA. Beyond standard NLG metrics (e.g., BLEU-4, ROUGE-L, BERTScore), we propose three clinically grounded metrics - Entity Coverage Rate (ECR), Uncovered Entity Rate (UER), and Factual Consistency Rate (FCR) - to assess diagnostic completeness and factual reliability. Experiments reveal that neither frontier nor medical-domain LLMs perform adequately in zero-shot settings. In contrast, PET-F2I-7B achieves substantial gains (e.g., 0.708 BLEU-4) and a 3.0x improvement in entity coverage over the strongest baseline, while offering advantages in cost, latency, and privacy. Beyond this modeling contribution, PET-F2I-41K establishes a standardized evaluation framework to accelerate the development of reliable and clinically deployable reporting systems for PET/CT.", "date": "2026-03-11", "categories": []}
{"id": "2603.10933", "title": "Bridging the Skill Gap in Clinical CBCT Interpretation with CBCTRepD", "abstract": "Generative AI has advanced rapidly in medical report generation; however, its application to oral and maxillofacial CBCT reporting remains limited, largely because of the scarcity of high-quality paired CBCT-report data and the intrinsic complexity of volumetric CBCT interpretation. To address this, we introduce CBCTRepD, a bilingual oral and maxillofacial CBCT report-generation system designed for integration into routine radiologist-AI co-authoring workflows. We curated a large-scale, high-quality paired CBCT-report dataset comprising approximately 7,408 studies, covering 55 oral disease entities across diverse acquisition settings, and used it to develop the system. We further established a clinically grounded, multi-level evaluation framework that assesses both direct AI-generated drafts and radiologist-edited collaboration reports using automatic metrics together with radiologist- and clinician-centered evaluation. Using this framework, we show that CBCTRepD achieves superior report-generation performance and produces drafts with writing quality and standardization comparable to those of intermediate radiologists. More importantly, in radiologist-AI collaboration, CBCTRepD provides consistent and clinically meaningful benefits across experience levels: it helps novice radiologists improve toward intermediate-level reporting, enables intermediate radiologists to approach senior-level performance, and even assists senior radiologists by reducing omission-related errors, including clinically important missed lesions. By improving report structure, reducing omissions, and promoting attention to co-existing lesions across anatomical regions, CBCTRepD shows strong and reliable potential as a practical assistant for real-world CBCT reporting across multi-level care settings.", "date": "2026-03-11", "categories": []}
{"id": "2502.14916", "title": "MKE-Coder: Multi-Axial Knowledge with Evidence Verification in ICD Coding for Chinese EMRs", "abstract": "The task of automatically coding the International Classification of Diseases (ICD) in the medical field has been well-established and has received much attention. Automatic coding of the ICD in the medical field has been successful in English but faces challenges when dealing with Chinese electronic medical records (EMRs). The first issue lies in the difficulty of extracting disease code-related information from Chinese EMRs, primarily due to the concise writing style and specific internal structure of the EMRs. The second problem is that previous methods have failed to leverage the disease-based multi-axial knowledge and lack of association with the corresponding clinical evidence. This paper introduces a novel framework called MKE-Coder: Multi-axial Knowledge with Evidence verification in ICD coding for Chinese EMRs. Initially, we identify candidate codes for the diagnosis and categorize each of them into knowledge under four coding axes.Subsequently, we retrieve corresponding clinical evidence from the comprehensive content of EMRs and filter credible evidence through a scoring model. Finally, to ensure the validity of the candidate code, we propose an inference module based on the masked language modeling strategy. This module verifies that all the axis knowledge associated with the candidate code is supported by evidence and provides recommendations accordingly. To evaluate the performance of our framework, we conduct experiments using a large-scale Chinese EMR dataset collected from various hospitals. The experimental results demonstrate that MKE-Coder exhibits significant superiority in the task of automatic ICD coding based on Chinese EMRs. In the practical evaluation of our method within simulated real coding scenarios, it has been demonstrated that our approach significantly aids coders in enhancing both their coding accuracy and speed.", "date": "2025-02-19", "categories": []}
{"id": "2603.08989", "title": "Automated Thematic Analysis for Clinical Qualitative Data: Iterative Codebook Refinement with Full Provenance", "abstract": "Thematic analysis (TA) is widely used in health research to extract patterns from patient interviews, yet manual TA faces challenges in scalability and reproducibility. LLM-based automation can help, but existing approaches produce codebooks with limited generalizability and lack analytic auditability. We present an automated TA framework combining iterative codebook refinement with full provenance tracking. Evaluated on five corpora spanning clinical interviews, social media, and public transcripts, the framework achieves the highest composite quality score on four of five datasets compared to six baselines. Iterative refinement yields statistically significant improvements on four datasets with large effect sizes, driven by gains in code reusability and distributional consistency while preserving descriptive quality. On two clinical corpora (pediatric cardiology), generated themes align with expert-annotated themes.", "date": "2026-03-09", "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": "2603.06836", "title": "Validation of a Small Language Model for DSM-5 Substance Category Classification in Child Welfare Records", "abstract": "Background: Recent studies have demonstrated that large language models (LLMs) can perform binary classification tasks on child welfare narratives, detecting the presence or absence of constructs such as substance-related problems, domestic violence, and firearms involvement. Whether smaller, locally deployable models can move beyond binary detection to classify specific substance types from these narratives remains untested. Objective: To validate a locally hosted LLM classifier for identifying specific substance types aligned with DSM-5 categories in child welfare investigation narratives. Methods: A locally hosted 20-billion-parameter LLM classified child maltreatment investigation narratives from a Midwestern U.S. state. Records previously identified as containing substance-related problems were passed to a second classification stage targeting seven DSM-5 substance categories. Expert human review of 900 stratified cases assessed classification precision, recall, and inter-method reliability (Cohen's kappa). Test-retest stability was evaluated using approximately 15,000 independently classified records. Results: Five substance categories achieved almost perfect inter-method agreement (kappa = 0.94-1.00): alcohol, cannabis, opioid, stimulant, and sedative/hypnotic/anxiolytic. Classification precision ranged from 92% to 100% for these categories. Two low-prevalence categories (hallucinogen, inhalant) performed poorly. Test-retest agreement ranged from 92.1% to 99.1% across the seven categories. Conclusions: A small, locally hosted LLM can reliably classify substance types from child welfare administrative text, extending prior work on binary classification to multi-label substance identification.", "date": "2026-03-06", "categories": []}
{"id": "2603.07487", "title": "A Joint Neural Baseline for Concept, Assertion, and Relation Extraction from Clinical Text", "abstract": "Clinical information extraction (e.g., 2010 i2b2/VA challenge) usually presents tasks of concept recognition, assertion classification, and relation extraction. Jointly modeling the multi-stage tasks in the clinical domain is an underexplored topic. The existing independent task setting (reference inputs given in each stage) makes the joint models not directly comparable to the existing pipeline work. To address these issues, we define a joint task setting and propose a novel end-to-end system to jointly optimize three-stage tasks. We empirically investigate the joint evaluation of our proposal and the pipeline baseline with various embedding techniques: word, contextual, and in-domain contextual embeddings. The proposed joint system substantially outperforms the pipeline baseline by +0.3, +1.4, +3.1 for the concept, assertion, and relation F1. This work bridges joint approaches and clinical information extraction. The proposed approach could serve as a strong joint baseline for future research. The code is publicly available.", "date": "2026-03-08", "categories": []}
{"id": "2603.05776", "title": "PVminerLLM: Structured Extraction of Patient Voice from Patient-Generated Text using Large Language Models", "abstract": "Motivation: Patient-generated text contains critical information about patients' lived experiences, social circumstances, and engagement in care, including factors that strongly influence adherence, care coordination, and health equity. However, these patient voice signals are rarely available in structured form, limiting their use in patient-centered outcomes research and clinical quality improvement. Reliable extraction of such information is therefore essential for understanding and addressing non-clinical drivers of health outcomes at scale.\n  Results: We introduce PVminer, a benchmark for structured extraction of patient voice, and propose PVminerLLM, a supervised fine-tuned large language model tailored to this task. Across multiple datasets and model sizes, PVminerLLM substantially outperforms prompt-based baselines, achieving up to 83.82% F1 for Code prediction, 80.74% F1 for Sub-code prediction, and 87.03% F1 for evidence Span extraction. Notably, strong performance is achieved even with smaller models, demonstrating that reliable patient voice extraction is feasible without extreme model scale. These results enable scalable analysis of social and experiential signals embedded in patient-generated text.\n  Availability and Implementation: Code, evaluation scripts, and trained LLMs will be released publicly. Annotated datasets will be made available upon request for research use.\n  Keywords: Large Language Models, Supervised Fine-Tuning, Medical Annotation, Patient-Generated Text, Clinical NLP", "date": "2026-03-06", "categories": []}
{"id": "2503.15664", "title": "Enhancing Pancreatic Cancer Staging with Large Language Models: The Role of Retrieval-Augmented Generation", "abstract": "Purpose: Retrieval-augmented generation (RAG) is a technology to enhance the functionality and reliability of large language models (LLMs) by retrieving relevant information from reliable external knowledge (REK). RAG has gained interest in radiology, and we previously reported the utility of NotebookLM, an LLM with RAG (RAG-LLM), for lung cancer staging. However, since the comparator LLM differed from NotebookLM's internal model, it remained unclear whether its advantage stemmed from RAG or inherent model differences. To better isolate RAG's impact and assess its utility across different cancers, we compared NotebookLM with its internal LLM, Gemini 2.0 Flash, in a pancreatic cancer staging experiment.\n  Materials and Methods: A summary of Japan's pancreatic cancer staging guidelines was used as REK. We compared three groups - REK+/RAG+ (NotebookLM with REK), REK+/RAG- (Gemini 2.0 Flash with REK), and REK-/RAG- (Gemini 2.0 Flash without REK) - in staging 100 fictional pancreatic cancer cases based on CT findings. Staging criteria included TNM classification, local invasion factors, and resectability classification. In REK+/RAG+, retrieval accuracy was quantified based on the sufficiency of retrieved REK excerpts.\n  Results: REK+/RAG+ achieved a staging accuracy of 70%, outperforming REK+/RAG- (38%) and REK-/RAG- (35%). For TNM classification, REK+/RAG+ attained 80% accuracy, exceeding REK+/RAG- (55%) and REK-/RAG- (50%). Additionally, REK+/RAG+ explicitly presented retrieved REK excerpts, achieving a retrieval accuracy of 92%.\n  Conclusion: NotebookLM, a RAG-LLM, outperformed its internal LLM, Gemini 2.0 Flash, in a pancreatic cancer staging experiment, suggesting that RAG may improve LLM's staging accuracy. Furthermore, its ability to retrieve and present REK excerpts provides transparency for physicians, highlighting its applicability for clinical diagnosis and classification.", "date": "2025-03-19", "categories": []}
{"id": "2509.10035", "title": "Linguistic trajectories of bipolar disorder on social media", "abstract": "Language use offers valuable insight into affective disorders such as bipolar disorder (BD), yet past research has been cross-sectional and limited in scale. Here, we demonstrate that social media records can be leveraged to study longitudinal language change associated with BD on a large scale. Using a novel method to infer diagnosis timelines from user self-reports, we compared users self-identifying with BD, depression, or no mental health condition. The onset of BD diagnosis corresponded with widespread linguistic shifts reflecting mood disturbance, psychiatric comorbidity, substance abuse, hospitalization, medical comorbidities, interpersonal concerns, unusual thought content, and altered linguistic coherence. In the years following the diagnosis, discussions of mood symptoms were found to fluctuate periodically with a dominant 12-month cycle consistent with seasonal mood variation. These findings suggest that social media language captures linguistic and behavioral changes associated with BD and might serve as a valuable complement to traditional psychiatric cohort research.", "date": "2025-09-12", "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": "2511.06427", "title": "Dutch Metaphor Extraction from Cancer Patients' Interviews and Forum Data using LLMs and Human in the Loop", "abstract": "Metaphors and metaphorical language (MLs) play an important role in healthcare communication between clinicians, patients, and patients' family members. In this work, we focus on Dutch language data from cancer patients. We extract metaphors used by patients using two data sources: (1) cancer patient storytelling interview data and (2) online forum data, including patients' posts, comments, and questions to professionals. We investigate how current state-of-the-art large language models (LLMs) perform on this task by exploring different prompting strategies such as chain of thought reasoning, few-shot learning, and self-prompting. With a human-in-the-loop setup, we verify the extracted metaphors and compile the outputs into a corpus named HealthQuote.NL. We believe the extracted metaphors can support better patient care, for example shared decision making, improved communication between patients and clinicians, and enhanced patient health literacy. They can also inform the design of personalized care pathways. We share prompts and related resources at https://github.com/4dpicture/HealthQuote.NL", "date": "2025-11-09", "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": "2603.01690", "title": "QIME: Constructing Interpretable Medical Text Embeddings via Ontology-Grounded Questions", "abstract": "While dense biomedical embeddings achieve strong performance, their black-box nature limits their utility in clinical decision-making. Recent question-based interpretable embeddings represent text as binary answers to natural-language questions, but these approaches often rely on heuristic or surface-level contrastive signals and overlook specialized domain knowledge. We propose QIME, an ontology-grounded framework for constructing interpretable medical text embeddings in which each dimension corresponds to a clinically meaningful yes/no question. By conditioning on cluster-specific medical concept signatures, QIME generates semantically atomic questions that capture fine-grained distinctions in biomedical text. Furthermore, QIME supports a training-free embedding construction strategy that eliminates per-question classifier training while further improving performance. Experiments across biomedical semantic similarity, clustering, and retrieval benchmarks show that QIME consistently outperforms prior interpretable embedding methods and substantially narrows the gap to strong black-box biomedical encoders, while providing concise and clinically informative explanations.", "date": "2026-03-02", "categories": []}
{"id": "2506.13474", "title": "Language Agents for Hypothesis-driven Clinical Decision Making with Reinforcement Learning", "abstract": "Clinical decision-making is a dynamic, interactive, and cyclic process where doctors have to repeatedly decide on which clinical action to perform and consider newly uncovered information for diagnosis and treatment. Large Language Models (LLMs) have the potential to support clinicians in this process, however, most applications of LLMs in clinical decision support suffer from one of two limitations: Either they assume the unrealistic scenario of immediate availability of all patient information and do not model the interactive and iterative investigation process, or they restrict themselves to the limited \"out-of-the-box\" capabilities of large pre-trained models without performing task-specific training. In contrast to this, we propose to model clinical decision-making for diagnosis with a hypothesis-driven uncertainty-aware language agent, LA-CDM, that converges towards a diagnosis via repeatedly requesting and interpreting relevant tests. Using a hybrid training paradigm combining supervised and reinforcement learning, we train LA-CDM with three objectives targeting critical aspects of clinical decision-making: accurate hypothesis generation, hypothesis uncertainty estimation, and efficient decision-making. We evaluate our methodology on MIMIC-CDM, a real-world dataset covering four abdominal diseases containing various clinical tests and show the benefit of explicitly training clinical decision-making for increasing diagnostic performance and efficiency.", "date": "2025-06-16", "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": "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": "2603.00028", "title": "EPPCMinerBen: A Novel Benchmark for Evaluating Large Language Models on Electronic Patient-Provider Communication via the Patient Portal", "abstract": "Effective communication in health care is critical for treatment outcomes and adherence. With patient-provider exchanges shifting to secure messaging, analyzing electronic patient-communication (EPPC) data is both essential and challenging. We introduce EPPCMinerBen, a benchmark for evaluating LLMs in detecting communication patterns and extracting insights from electronic patient-provider messages. EPPCMinerBen includes three sub-tasks: Code Classification, Subcode Classification, and Evidence Extraction. Using 1,933 expert annotated sentences from 752 secure messages of the patient portal at Yale New Haven Hospital, it evaluates LLMs on identifying communicative intent and supportive text. Benchmarks span various LLMs under zero-shot and few-shot settings, with data to be released via the NCI Cancer Data Service. Model performance varied across tasks and settings. Llama-3.1-70B led in evidence extraction (F1: 82.84%) and performed well in classification. Llama-3.3-70b-Instruct outperformed all models in code classification (F1: 67.03%). DeepSeek-R1-Distill-Qwen-32B excelled in subcode classification (F1: 48.25%), while sdoh-llama-3-70B showed consistent performance. Smaller models underperformed, especially in subcode classification (>30% F1). Few-shot prompting improved most tasks. Our results show that large, instruction-tuned models generally perform better in EPPCMinerBen tasks, particularly evidence extraction while smaller models struggle with fine-grained reasoning. EPPCMinerBen provides a benchmark for discourse-level understanding, supporting future work on model generalization and patient-provider communication analysis. Keywords: Electronic Patient-Provider Communication, Large language models, Data collection, Prompt engineering", "date": "2026-02-04", "categories": []}
{"id": "2603.00460", "title": "MED-COPILOT: A Medical Assistant Powered by GraphRAG and Similar Patient Case Retrieval", "abstract": "Clinical decision-making requires synthesizing heterogeneous evidence, including patient histories, clinical guidelines, and trajectories of comparable cases. While large language models (LLMs) offer strong reasoning capabilities, they remain prone to hallucinations and struggle to integrate long, structured medical documents. We present MED-COPILOT, an interactive clinical decision-support system designed for clinicians and medical trainees, which combines guideline-grounded GraphRAG retrieval with hybrid semantic-keyword similar-patient retrieval to support transparent and evidence-aware clinical reasoning. The system builds a structured knowledge graph from WHO and NICE guidelines, applies community-level summarization for efficient retrieval, and maintains a 36,000-case similar-patient database derived from SOAP-normalized MIMIC-IV notes and Synthea-generated records.\n  We evaluate our framework on clinical note completion and medical question answering, and demonstrate that it consistently outperforms parametric LLM baselines and standard RAG, improving both generation fidelity and clinical reasoning accuracy. The full system is available at https://huggingface.co/spaces/Cryo3978/Med_GraphRAG , enabling users to inspect retrieved evidence, visualize token-level similarity contributions, and conduct guided follow-up analysis. Our results demonstrate a practical and interpretable approach to integrating structured guideline knowledge with patient-level analogical evidence for clinical LLMs.", "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": "2603.01625", "title": "Measuring What VLMs Don't Say: Validation Metrics Hide Clinical Terminology Erasure in Radiology Report Generation", "abstract": "Reliable deployment of Vision-Language Models (VLMs) in radiology requires validation metrics that go beyond surface-level text similarity to ensure clinical fidelity and demographic fairness. This paper investigates a critical blind spot in current model evaluation: the use of decoding strategies that lead to high aggregate token-overlap scores despite succumbing to template collapse, in which models generate only repetitive, safe generic text and omit clinical terminology. Unaddressed, this blind spot can lead to metric gaming, where models that perform well on benchmarks prove clinically uninformative. Instead, we advocate for lexical diversity measures to check model generations for clinical specificity. We introduce Clinical Association Displacement (CAD), a vocabulary-level framework that quantifies shifts in demographic-based word associations in generated reports. Weighted Association Erasure (WAE) aggregates these shifts to measure the clinical signal loss across demographic groups. We show that deterministic decoding produces high levels of semantic erasure, while stochastic sampling generates diverse outputs but risks introducing new bias, motivating a fundamental rethink of how \"optimal\" reporting is defined.", "date": "2026-03-02", "categories": []}
{"id": "2511.04506", "title": "Modeling Clinical Uncertainty in Radiology Reports: from Explicit Uncertainty Markers to Implicit Reasoning Pathways", "abstract": "Radiology reports are invaluable for clinical decision-making and hold great potential for automated analysis when structured into machine-readable formats. These reports often contain uncertainty, which we categorize into two distinct types: (i) Explicit uncertainty reflects doubt about the presence or absence of findings, conveyed through hedging phrases. These vary in meaning depending on the context, making rule-based systems insufficient to quantify the level of uncertainty for specific findings; (ii) Implicit uncertainty arises when radiologists omit parts of their reasoning, recording only key findings or diagnoses. Here, it is often unclear whether omitted findings are truly absent or simply unmentioned for brevity. We address these challenges with a two-part framework. We quantify explicit uncertainty by creating an expert-validated, LLM-based reference ranking of common hedging phrases, and mapping each finding to a probability value based on this reference. In addition, we model implicit uncertainty through an expansion framework that systematically adds characteristic sub-findings derived from expert-defined diagnostic pathways for 14 common diagnoses. Using these methods, we release Lunguage++, an expanded, uncertainty-aware version of the Lunguage benchmark of fine-grained structured radiology reports. This enriched resource enables uncertainty-aware image classification, faithful diagnostic reasoning, and new investigations into the clinical impact of diagnostic uncertainty.", "date": "2025-11-06", "categories": []}
{"id": "2602.23676", "title": "Suppressing Prior-Comparison Hallucinations in Radiology Report Generation via Semantically Decoupled Latent Steering", "abstract": "Automated radiology report generation using vision-language models (VLMs) is limited by the risk of prior-comparison hallucination, where the model generates historical findings unsupported by the current study. We address this challenge with a training-free, inference-time control framework termed Semantically Decoupled Latent Steering (SDLS). Unlike generic activation steering, which often suffers from semantic entanglement, our approach constructs a semantic-free intervention vector via large language model (LLM)-driven semantic decomposition followed by $QR$-based orthogonalization. This orthogonalization step is critical. It leverages geometric constraints to filter out the clinical semantics often entangled in standard principal component analysis (PCA) directions, ensuring that the steering vector targets only the ``historical comparison\" axis. We validate our method on the BiomedGPT foundation model, demonstrating that it overcomes the trade-off between hallucination suppression and clinical accuracy. Extensive experiments on MIMIC-CXR, and zero-shot transfer evaluation on CheXpert Plus and IU-Xray, demonstrate the robustness of our approach. Quantitative evaluations on MIMIC-CXR show that our approach significantly reduces the probability of historical hallucinations (FilBERT score decreases from 0.2373 to 0.1889) and improves clinical label fidelity (CheXpert macro-F1 increases from 0.2242 to 0.3208). Supplementary evaluations confirm that the structural integrity of the clinical narrative is maintained.", "date": "2026-02-27", "categories": []}
{"id": "2602.22483", "title": "Importance of Prompt Optimisation for Error Detection in Medical Notes Using Language Models", "abstract": "Errors in medical text can cause delays or even result in incorrect treatment for patients. Recently, language models have shown promise in their ability to automatically detect errors in medical text, an ability that has the opportunity to significantly benefit healthcare systems. In this paper, we explore the importance of prompt optimisation for small and large language models when applied to the task of error detection. We perform rigorous experiments and analysis across frontier language models and open-source language models. We show that automatic prompt optimisation with Genetic-Pareto (GEPA) improves error detection over the baseline accuracy performance from 0.669 to 0.785 with GPT-5 and 0.578 to 0.690 with Qwen3-32B, approaching the performance of medical doctors and achieving state-of-the-art performance on the MEDEC benchmark dataset. Code available on GitHub: https://github.com/CraigMyles/clinical-note-error-detection", "date": "2026-02-25", "categories": []}
{"id": "2602.23062", "title": "Toward Automatic Filling of Case Report Forms: A Case Study on Data from an Italian Emergency Department", "abstract": "Case Report Forms (CRFs) collect data about patients and are at the core of well-established practices to conduct research in clinical settings. With the recent progress of language technologies, there is an increasing interest in automatic CRF-filling from clinical notes, mostly based on the use of Large Language Models (LLMs). However, there is a general scarcity of annotated CRF data, both for training and testing LLMs, which limits the progress on this task. As a step in the direction of providing such data, we present a new dataset of clinical notes from an Italian Emergency Department annotated with respect to a pre-defined CRF containing 134 items to be filled. We provide an analysis of the data, define the CRF-filling task and metric for its evaluation, and report on pilot experiments where we use an open-source state-of-the-art LLM to automatically execute the task. Results of the case-study show that (i) CRF-filling from real clinical notes in Italian can be approached in a zero-shot setting; (ii) LLMs' results are affected by biases (e.g., a cautious behaviour favours \"unknown\" answers), which need to be corrected.", "date": "2026-02-26", "categories": []}
{"id": "2602.21374", "title": "Small Language Models for Privacy-Preserving Clinical Information Extraction in Low-Resource Languages", "abstract": "Extracting clinical information from medical transcripts in low-resource languages remains a significant challenge in healthcare natural language processing (NLP). This study evaluates a two-step pipeline combining Aya-expanse-8B as a Persian-to-English translation model with five open-source small language models (SLMs) -- Qwen2.5-7B-Instruct, Llama-3.1-8B-Instruct, Llama-3.2-3B-Instruct, Qwen2.5-1.5B-Instruct, and Gemma-3-1B-it -- for binary extraction of 13 clinical features from 1,221 anonymized Persian transcripts collected at a cancer palliative care call center. Using a few-shot prompting strategy without fine-tuning, models were assessed on macro-averaged F1-score, Matthews Correlation Coefficient (MCC), sensitivity, and specificity to account for class imbalance. Qwen2.5-7B-Instruct achieved the highest overall performance (median macro-F1: 0.899; MCC: 0.797), while Gemma-3-1B-it showed the weakest results. Larger models (7B--8B parameters) consistently outperformed smaller counterparts in sensitivity and MCC. A bilingual analysis of Aya-expanse-8B revealed that translating Persian transcripts to English improved sensitivity, reduced missing outputs, and boosted metrics robust to class imbalance, though at the cost of slightly lower specificity and precision. Feature-level results showed reliable extraction of physiological symptoms across most models, whereas psychological complaints, administrative requests, and complex somatic features remained challenging. These findings establish a practical, privacy-preserving blueprint for deploying open-source SLMs in multilingual clinical NLP settings with limited infrastructure and annotation resources, and highlight the importance of jointly optimizing model scale and input language strategy for sensitive healthcare applications.", "date": "2026-02-24", "categories": []}
{"id": "2602.21638", "title": "Multi-dimensional Assessment and Explainable Feedback for Counselor Responses to Client Resistance in Text-based Counseling with LLMs", "abstract": "Effectively addressing client resistance is a sophisticated clinical skill in psychological counseling, yet practitioners often lack timely and scalable supervisory feedback to refine their approaches. Although current NLP research has examined overall counseling quality and general therapeutic skills, it fails to provide granular evaluations of high-stakes moments where clients exhibit resistance. In this work, we present a comprehensive pipeline for the multi-dimensional evaluation of human counselors' interventions specifically targeting client resistance in text-based therapy. We introduce a theory-driven framework that decomposes counselor responses into four distinct communication mechanisms. Leveraging this framework, we curate and share an expert-annotated dataset of real-world counseling excerpts, pairing counselor-client interactions with professional ratings and explanatory rationales. Using this data, we perform full-parameter instruction tuning on a Llama-3.1-8B-Instruct backbone to model fine-grained evaluative judgments of response quality and generate explanations underlying. Experimental results show that our approach can effectively distinguish the quality of different communication mechanisms (77-81% F1), substantially outperforming GPT-4o and Claude-3.5-Sonnet (45-59% F1). Moreover, the model produces high-quality explanations that closely align with expert references and receive near-ceiling ratings from human experts (2.8-2.9/3.0). A controlled experiment with 43 counselors further confirms that receiving these AI-generated feedback significantly improves counselors' ability to respond effectively to client resistance.", "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": "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": "2602.21165", "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": "2602.18443", "title": "From \"Help\" to Helpful: A Hierarchical Assessment of LLMs in Mental e-Health Applications", "abstract": "Psychosocial online counselling frequently encounters generic subject lines that impede efficient case prioritisation. This study evaluates eleven large language models generating six-word subject lines for German counselling emails through hierarchical assessment - first categorising outputs, then ranking within categories to enable manageable evaluation. Nine assessors (counselling professionals and AI systems) enable analysis via Krippendorff's $\\alpha$, Spearman's $\\rho$, Pearson's $r$ and Kendall's $\\tau$. Results reveal performance trade-offs between proprietary services and privacy-preserving open-source alternatives, with German fine-tuning consistently improving performance. The study addresses critical ethical considerations for mental health AI deployment including privacy, bias and accountability.", "date": "2026-01-12", "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": "2602.20040", "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": "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": "2602.17475", "title": "Small LLMs for Medical NLP: a Systematic Analysis of Few-Shot, Constraint Decoding, Fine-Tuning and Continual Pre-Training in Italian", "abstract": "Large Language Models (LLMs) consistently excel in diverse medical Natural Language Processing (NLP) tasks, yet their substantial computational requirements often limit deployment in real-world healthcare settings. In this work, we investigate whether \"small\" LLMs (around one billion parameters) can effectively perform medical tasks while maintaining competitive accuracy. We evaluate models from three major families-Llama-3, Gemma-3, and Qwen3-across 20 clinical NLP tasks among Named Entity Recognition, Relation Extraction, Case Report Form Filling, Question Answering, and Argument Mining. We systematically compare a range of adaptation strategies, both at inference time (few-shot prompting, constraint decoding) and at training time (supervised fine-tuning, continual pretraining). Fine-tuning emerges as the most effective approach, while the combination of few-shot prompting and constraint decoding offers strong lower-resource alternatives. Our results show that small LLMs can match or even surpass larger baselines, with our best configuration based on Qwen3-1.7B achieving an average score +9.2 points higher than Qwen3-32B. We release a comprehensive collection of all the publicly available Italian medical datasets for NLP tasks, together with our top-performing models. Furthermore, we release an Italian dataset of 126M words from the Emergency Department of an Italian Hospital, and 175M words from various sources that we used for continual pre-training.", "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": "2602.16006", "title": "BTReport: A Framework for Brain Tumor Radiology Report Generation with Clinically Relevant Features", "abstract": "Recent advances in radiology report generation (RRG) have been driven by large paired image-text datasets; however, progress in neuro-oncology has been limited due to a lack of open paired image-report datasets. Here, we introduce BTReport, an open-source framework for brain tumor RRG that constructs natural language radiology reports using deterministically extracted imaging features. Unlike existing approaches that rely on large general-purpose or fine-tuned vision-language models for both image interpretation and report composition, BTReport performs deterministic feature extraction for image analysis and uses large language models only for syntactic structuring and narrative formatting. By separating RRG into a deterministic feature extraction step and a report generation step, the generated reports are completely interpretable and less prone to hallucinations. We show that the features used for report generation are predictive of key clinical outcomes, including survival and IDH mutation status, and reports generated by BTReport are more closely aligned with reference clinical reports than existing baselines for RRG. Finally, we introduce BTReport-BraTS, a companion dataset that augments BraTS imaging with synthetically generated radiology reports produced with BTReport. Code for this project can be found at  https://github.com/KurtLabUW/BTReport.", "date": "2026-02-17", "categories": []}
{"id": "2602.16037", "title": "Optimization Instability in Autonomous Agentic Workflows for Clinical Symptom Detection", "abstract": "Autonomous agentic workflows that iteratively refine their own behavior hold considerable promise, yet their failure modes remain poorly characterized. We investigate optimization instability, a phenomenon in which continued autonomous improvement paradoxically degrades classifier performance, using Pythia, an open-source framework for automated prompt optimization. Evaluating three clinical symptoms with varying prevalence (shortness of breath at 23%, chest pain at 12%, and Long COVID brain fog at 3%), we observed that validation sensitivity oscillated between 1.0 and 0.0 across iterations, with severity inversely proportional to class prevalence. At 3% prevalence, the system achieved 95% accuracy while detecting zero positive cases, a failure mode obscured by standard evaluation metrics. We evaluated two interventions: a guiding agent that actively redirected optimization, amplifying overfitting rather than correcting it, and a selector agent that retrospectively identified the best-performing iteration successfully prevented catastrophic failure. With selector agent oversight, the system outperformed expert-curated lexicons on brain fog detection by 331% (F1) and chest pain by 7%, despite requiring only a single natural language term as input. These findings characterize a critical failure mode of autonomous AI systems and demonstrate that retrospective selection outperforms active intervention for stabilization in low-prevalence classification tasks.", "date": "2026-02-17", "categories": []}
{"id": "2602.15504", "title": "Towards Expectation Detection in Language: A Case Study on Treatment Expectations in Reddit", "abstract": "Patients' expectations towards their treatment have a substantial effect on the treatments' success. While primarily studied in clinical settings, online patient platforms like medical subreddits may hold complementary insights: treatment expectations that patients feel unnecessary or uncomfortable to share elsewhere. Despite this, no studies examine what type of expectations users discuss online and how they express them. Presumably this is because expectations have not been studied in natural language processing (NLP) before. Therefore, we introduce the task of Expectation Detection, arguing that expectations are relevant for many applications, including opinion mining and product design. Subsequently, we present a case study for the medical domain, where expectations are particularly crucial to extract. We contribute RedHOTExpect, a corpus of Reddit posts (4.5K posts) to study expectations in this context. We use a large language model (LLM) to silver-label the data and validate its quality manually (label accuracy ~78%). Based on this, we analyze which linguistic patterns characterize expectations and explore what patients expect and why. We find that optimism and proactive framing are more pronounced in posts about physical or treatment-related illnesses compared to mental-health contexts, and that in our dataset, patients mostly discuss benefits rather than negative outcomes. The RedHOTExpect corpus can be obtained from https://www.ims.uni-stuttgart.de/data/RedHOTExpect", "date": "2026-02-17", "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": "2503.01159", "title": "Large Language Models for Healthcare Text Classification: A Systematic Review", "abstract": "Large Language Models (LLMs) have fundamentally transformed approaches to Natural Language Processing (NLP) tasks across diverse domains. In healthcare, accurate and cost-efficient text classification is crucial, whether for clinical notes analysis, diagnosis coding, or any other task, and LLMs present promising potential. Text classification has always faced multiple challenges, including manual annotation for training, handling imbalanced data, and developing scalable approaches. With healthcare, additional challenges are added, particularly the critical need to preserve patients' data privacy and the complexity of the medical terminology. Numerous studies have been conducted to leverage LLMs for automated healthcare text classification and contrast the results with existing machine learning-based methods where embedding, annotation, and training are traditionally required. Existing systematic reviews about LLMs either do not specialize in text classification or do not focus on the healthcare domain. This research synthesizes and critically evaluates the current evidence found in the literature regarding the use of LLMs for text classification in a healthcare setting. Major databases (e.g., Google Scholar, Scopus, PubMed, Science Direct) and other resources were queried, which focused on the papers published between 2018 and 2024 within the framework of PRISMA guidelines, which resulted in 65 eligible research articles. These were categorized by text classification type (e.g., binary classification, multi-label classification), application (e.g., clinical decision support, public health and opinion analysis), methodology, type of healthcare text, and metrics used for evaluation and validation. This review reveals the existing gaps in the literature and suggests future research lines that can be investigated and explored.", "date": "2025-03-03", "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": "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": "2508.16390", "title": "A Large-Scale Benchmark for Evaluating Large Language Models on Medical Question Answering in Romanian", "abstract": "We introduce MedQARo, the first large-scale medical QA benchmark in Romanian, alongside a comprehensive evaluation of state-of-the-art large language models (LLMs). We construct a high-quality and large-scale dataset comprising 105,880 QA pairs about cancer patients from two medical centers. The questions regard medical case summaries of 1,242 patients, requiring both keyword extraction and reasoning. Our benchmark contains both in-domain and cross-domain (cross-center and cross-cancer) test collections, enabling a precise assessment of generalization capabilities. We experiment with four open-source LLMs from distinct families of models on MedQARo. Each model is employed in two scenarios: zero-shot prompting and supervised fine-tuning. We also evaluate two state-of-the-art LLMs exposed only through APIs, namely GPT-5.2 and Gemini 3 Flash. Our results show that fine-tuned models significantly outperform zero-shot models, indicating that pretrained models fail to generalize on MedQARo. Our findings demonstrate the importance of both domain-specific and language-specific fine-tuning for reliable clinical QA in Romanian.", "date": "2025-08-22", "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": "2602.11028", "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": "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": "2409.17397", "title": "Building Multilingual Datasets for Predicting Mental Health Severity through LLMs: Prospects and Challenges", "abstract": "Large Language Models (LLMs) are increasingly being integrated into various medical fields, including mental health support systems. However, there is a gap in research regarding the effectiveness of LLMs in non-English mental health support applications. To address this problem, we present a novel multilingual adaptation of widely-used mental health datasets, translated from English into six languages (e.g., Greek, Turkish, French, Portuguese, German, and Finnish). This dataset enables a comprehensive evaluation of LLM performance in detecting mental health conditions and assessing their severity across multiple languages. By experimenting with GPT and Llama, we observe considerable variability in performance across languages, despite being evaluated on the same translated dataset. This inconsistency underscores the complexities inherent in multilingual mental health support, where language-specific nuances and mental health data coverage can affect the accuracy of the models. Through comprehensive error analysis, we emphasize the risks of relying exclusively on LLMs in medical settings (e.g., their potential to contribute to misdiagnoses). Moreover, our proposed approach offers significant cost savings for multilingual tasks, presenting a major advantage for broad-scale implementation.", "date": "2024-09-25", "categories": []}
{"id": "2602.05692", "title": "MedErrBench: A Fine-Grained Multilingual Benchmark for Medical Error Detection and Correction with Clinical Expert Annotations", "abstract": "Inaccuracies in existing or generated clinical text may lead to serious adverse consequences, especially if it is a misdiagnosis or incorrect treatment suggestion. With Large Language Models (LLMs) increasingly being used across diverse healthcare applications, comprehensive evaluation through dedicated benchmarks is crucial. However, such datasets remain scarce, especially across diverse languages and contexts. In this paper, we introduce MedErrBench, the first multilingual benchmark for error detection, localization, and correction, developed under the guidance of experienced clinicians. Based on an expanded taxonomy of ten common error types, MedErrBench covers English, Arabic and Chinese, with natural clinical cases annotated and reviewed by domain experts. We assessed the performance of a range of general-purpose, language-specific, and medical-domain language models across all three tasks. Our results reveal notable performance gaps, particularly in non-English settings, highlighting the need for clinically grounded, language-aware systems. By making MedErrBench and our evaluation protocols publicly-available, we aim to advance multilingual clinical NLP to promote safer and more equitable AI-based healthcare globally. The dataset is available in the supplementary material. An anonymized version of the dataset is available at: https://github.com/congboma/MedErrBench.", "date": "2026-02-05", "categories": []}
{"id": "2601.01171", "title": "Almost Clinical: Linguistic properties of synthetic electronic health records", "abstract": "This study evaluates the linguistic and clinical suitability of synthetic electronic health records in mental health. First, we describe the rationale and the methodology for creating the synthetic corpus. Second, we examine expressions of agency, modality, and information flow across four clinical genres (Assessments, Correspondence, Referrals and Care plans) with the aim to understand how LLMs grammatically construct medical authority and patient agency through linguistic choices. While LLMs produce coherent, terminology-appropriate texts that approximate clinical practice, systematic divergences remain, including registerial shifts, insufficient clinical specificity, and inaccuracies in medication use and diagnostic procedures. The results show both the potential and limitations of synthetic corpora for enabling large-scale linguistic research otherwise impossible with genuine patient records.", "date": "2026-01-03", "categories": []}
{"id": "2602.03942", "title": "Linguistic Blind Spots in Clinical Decision Extraction", "abstract": "Extracting medical decisions from clinical notes is a key step for clinical decision support and patient-facing care summaries. We study how the linguistic characteristics of clinical decisions vary across decision categories and whether these differences explain extraction failures. Using MedDec discharge summaries annotated with decision categories from the Decision Identification and Classification Taxonomy for Use in Medicine (DICTUM), we compute seven linguistic indices for each decision span and analyze span-level extraction recall of a standard transformer model. We find clear category-specific signatures: drug-related and problem-defining decisions are entity-dense and telegraphic, whereas advice and precaution decisions contain more narrative, with higher stopword and pronoun proportions and more frequent hedging and negation cues. On the validation split, exact-match recall is 48%, with large gaps across linguistic strata: recall drops from 58% to 24% from the lowest to highest stopword-proportion bins, and spans containing hedging or negation cues are less likely to be recovered. Under a relaxed overlap-based match criterion, recall increases to 71%, indicating that many errors are span boundary disagreements rather than complete misses. Overall, narrative-style spans--common in advice and precaution decisions--are a consistent blind spot under exact matching, suggesting that downstream systems should incorporate boundary-tolerant evaluation and extraction strategies for clinical decisions.", "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": "2504.08040", "title": "Can Reasoning LLMs Enhance Clinical Document Classification?", "abstract": "Clinical document classification is essential for converting unstructured medical texts into standardised ICD-10 diagnoses, yet it faces challenges due to complex medical language, privacy constraints, and limited annotated datasets. Large Language Models (LLMs) offer promising improvements in accuracy and efficiency for this task. This study evaluates the performance and consistency of eight LLMs; four reasoning (Qwen QWQ, Deepseek Reasoner, GPT o3 Mini, Gemini 2.0 Flash Thinking) and four non-reasoning (Llama 3.3, GPT 4o Mini, Gemini 2.0 Flash, Deepseek Chat); in classifying clinical discharge summaries using the MIMIC-IV dataset. Using cTAKES to structure clinical narratives, models were assessed across three experimental runs, with majority voting determining final predictions. Results showed that reasoning models outperformed non-reasoning models in accuracy (71% vs 68%) and F1 score (67% vs 60%), with Gemini 2.0 Flash Thinking achieving the highest accuracy (75%) and F1 score (76%). However, non-reasoning models demonstrated greater stability (91% vs 84% consistency). Performance varied across ICD-10 codes, with reasoning models excelling in complex cases but struggling with abstract categories. Findings indicate a trade-off between accuracy and consistency, suggesting that a hybrid approach could optimise clinical coding. Future research should explore multi-label classification, domain-specific fine-tuning, and ensemble methods to enhance model reliability in real-world applications.", "date": "2025-04-10", "categories": []}
{"id": "2602.00740", "title": "ExperienceWeaver: Optimizing Small-sample Experience Learning for LLM-based Clinical Text Improvement", "abstract": "Clinical text improvement is vital for healthcare efficiency but remains difficult due to limited high-quality data and the complex constraints of medical documentation. While Large Language Models (LLMs) show promise, current approaches struggle in small-sample settings: supervised fine-tuning is data-intensive and costly, while retrieval-augmented generation often provides superficial corrections without capturing the reasoning behind revisions. To address these limitations, we propose ExperienceWeaver, a hierarchical framework that shifts the focus from data retrieval to experience learning. Instead of simply recalling past examples, ExperienceWeaver distills noisy, multi-dimensional feedback into structured, actionable knowledge. Specifically, error-specific Tips and high-level Strategies. By injecting this distilled experience into an agentic pipeline, the model learns \"how to revise\" rather than just \"what to revise\". Extensive evaluations across four clinical datasets demonstrate that ExperienceWeaver consistently improves performance, surpassing state-of-the-art models such as Gemini-3 Pro in small-sample settings.", "date": "2026-01-31", "categories": []}
{"id": "2601.22373", "title": "Stability-Aware Prompt Optimization for Clinical Data Abstraction", "abstract": "Large language models used for clinical abstraction are sensitive to prompt wording, yet most work treats prompts as fixed and studies uncertainty in isolation. We argue these should be treated jointly. Across two clinical tasks (MedAlign applicability/correctness and MS subtype abstraction) and multiple open and proprietary models, we measure prompt sensitivity via flip rates and relate it to calibration and selective prediction. We find that higher accuracy does not guarantee prompt stability, and that models can appear well-calibrated yet remain fragile to paraphrases. We propose a dual-objective prompt optimization loop that jointly targets accuracy and stability, showing that explicitly including a stability term reduces flip rates across tasks and models, sometimes at modest accuracy cost. Our results suggest prompt sensitivity should be an explicit objective when validating clinical LLM systems.", "date": "2026-01-29", "categories": []}
{"id": "2601.21551", "title": "Note2Chat: Improving LLMs for Multi-Turn Clinical History Taking Using Medical Notes", "abstract": "Effective clinical history taking is a foundational yet underexplored component of clinical reasoning. While large language models (LLMs) have shown promise on static benchmarks, they often fall short in dynamic, multi-turn diagnostic settings that require iterative questioning and hypothesis refinement. To address this gap, we propose \\method{}, a note-driven framework that trains LLMs to conduct structured history taking and diagnosis by learning from widely available medical notes. Instead of relying on scarce and sensitive dialogue data, we convert real-world medical notes into high-quality doctor-patient dialogues using a decision tree-guided generation and refinement pipeline. We then propose a three-stage fine-tuning strategy combining supervised learning, simulated data augmentation, and preference learning. Furthermore, we propose a novel single-turn reasoning paradigm that reframes history taking as a sequence of single-turn reasoning problems. This design enhances interpretability and enables local supervision, dynamic adaptation, and greater sample efficiency. Experimental results show that our method substantially improves clinical reasoning, achieving gains of +16.9 F1 and +21.0 Top-1 diagnostic accuracy over GPT-4o. Our code and dataset can be found at https://github.com/zhentingsheng/Note2Chat.", "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": "2601.20674", "title": "Harnessing Large Language Models for Precision Querying and Retrieval-Augmented Knowledge Extraction in Clinical Data Science", "abstract": "This study applies Large Language Models (LLMs) to two foundational Electronic Health Record (EHR) data science tasks: structured data querying (using programmatic languages, Python/Pandas) and information extraction from unstructured clinical text via a Retrieval Augmented Generation (RAG) pipeline. We test the ability of LLMs to interact accurately with large structured datasets for analytics and the reliability of LLMs in extracting semantically correct information from free text health records when supported by RAG. To this end, we presented a flexible evaluation framework that automatically generates synthetic question and answer pairs tailored to the characteristics of each dataset or task. Experiments were conducted on a curated subset of MIMIC III, (four structured tables and one clinical note type), using a mix of locally hosted and API-based LLMs. Evaluation combined exact-match metrics, semantic similarity, and human judgment. Our findings demonstrate the potential of LLMs to support precise querying and accurate information extraction in clinical workflows.", "date": "2026-01-28", "categories": []}
{"id": "2601.19191", "title": "Transparency-First Medical Language Models: Datasheets, Model Cards, and End-to-End Data Provenance for Clinical NLP", "abstract": "We introduce TeMLM, a set of transparency-first release artifacts for clinical language models. TeMLM unifies provenance, data transparency, modeling transparency, and governance into a single, machine-checkable release bundle. We define an artifact suite (TeMLM-Card, TeMLM-Datasheet, TeMLM-Provenance) and a lightweight conformance checklist for repeatable auditing. We instantiate the artifacts on Technetium-I, a large-scale synthetic clinical NLP dataset with 498,000 notes, 7.74M PHI entity annotations across 10 types, and ICD-9-CM diagnosis labels, and report reference results for ProtactiniumBERT (about 100 million parameters) on PHI de-identification (token classification) and top-50 ICD-9 code extraction (multi-label classification). We emphasize that synthetic benchmarks are valuable for tooling and process validation, but models should be validated on real clinical data prior to deployment.", "date": "2026-01-27", "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": "2601.19899", "title": "Evaluation of Oncotimia: An LLM based system for supporting tumour boards", "abstract": "Multidisciplinary tumour boards (MDTBs) play a central role in oncology decision-making but require manual processes and structuring large volumes of heterogeneous clinical information, resulting in a substantial documentation burden. In this work, we present ONCOTIMIA, a modular and secure clinical tool designed to integrate generative artificial intelligence (GenAI) into oncology workflows and evaluate its application to the automatic completion of lung cancer tumour board forms using large language models (LLMs). The system combines a multi-layer data lake, hybrid relational and vector storage, retrieval-augmented generation (RAG) and a rule-driven adaptive form model to transform unstructured clinical documentation into structured and standardised tumour board records. We assess the performance of six LLMs deployed through AWS Bedrock on ten lung cancer cases, measuring both completion form accuracy and end-to-end latency. The results demonstrate high performance across models, with the best performing configuration achieving an 80% of correct field completion and clinically acceptable response time for most LLMs. Larger and more recent models exhibit best accuracies without incurring prohibitive latency. These findings provide empirical evidence that LLM- assisted autocompletion form is technically feasible and operationally viable in multidisciplinary lung cancer workflows and support its potential to significantly reduce documentation burden while preserving data quality.", "date": "2026-01-27", "categories": []}
{"id": "2508.19008", "title": "Computational Phenomenology of Borderline Personality Disorder: A Comparative Evaluation of LLM-Simulated Expert Personas and Human Clinical Experts", "abstract": "Building on a human-led thematic analysis of life-story interviews with inpatients with Borderline Personality Disorder, this study examines the capacity of large language models (OpenAI's GPT, Google's Gemini, and Anthropic's Claude) to support qualitative clinical analysis. The models were evaluated through a mixed procedure. Study A involved blinded and non-blinded expert judges in phenomenology and clinical psychology. Assessments included semantic congruence, Jaccard coefficients for overlap of outputs, multidimensional validity ratings of credibility, coherence, and the substantiveness of results, and their grounding in qualitative data. In Study B, neural methods were used to embed the theme descriptions created by humans and the models in a two-dimensional vector space to provide a computational measure of the difference between human and model semantics and linguistic style. In Study C, complementary non-expert evaluations were conducted to examine the influence of thematic verbosity on the perception of human authorship and content validity. Results of all three studies revealed variable overlap with the human analysis, with models being partly indistinguishable from, and also identifying themes originally omitted by, human researchers. The findings highlight both the variability and potential of AI-augmented thematic qualitative analysis to mitigate human interpretative bias and enhance sensitivity.", "date": "2025-08-26", "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": "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": "2601.18796", "title": "ctELM: Decoding and Manipulating Embeddings of Clinical Trials with Embedding Language Models", "abstract": "Text embeddings have become an essential part of a variety of language applications. However, methods for interpreting, exploring and reversing embedding spaces are limited, reducing transparency and precluding potentially valuable generative use cases. In this work, we align Large Language Models to embeddings of clinical trials using the recently reported Embedding Language Model (ELM) method. We develop an open-source, domain-agnostic ELM architecture and training framework, design training tasks for clinical trials, and introduce an expert-validated synthetic dataset. We then train a series of ELMs exploring the impact of tasks and training regimes. Our final model, ctELM, can accurately describe and compare unseen clinical trials from embeddings alone and produce plausible clinical trials from novel vectors. We further show that generated trial abstracts are responsive to moving embeddings along concept vectors for age and sex of study subjects. Our public ELM implementation and experimental results will aid the alignment of Large Language Models to embedding spaces in the biomedical domain and beyond.", "date": "2026-01-26", "categories": []}
{"id": "2505.22928", "title": "Enhancing Study-Level Inference from Clinical Trial Papers via Reinforcement Learning-Based Numeric Reasoning", "abstract": "Systematic reviews in medicine play a critical role in evidence-based decision-making by aggregating findings from multiple studies. A central bottleneck in automating this process is extracting numeric evidence and determining study-level conclusions for specific outcomes and comparisons. Prior work has framed this problem as a textual inference task by retrieving relevant content fragments and inferring conclusions from them. However, such approaches often rely on shallow textual cues and fail to capture the underlying numeric reasoning behind expert assessments.\n  In this work, we conceptualise the problem as one of quantitative reasoning. Rather than inferring conclusions from surface text, we extract structured numerical evidence (e.g., event counts or standard deviations) and apply domain knowledge informed logic to derive outcome-specific conclusions. We develop a numeric reasoning system composed of a numeric data extraction model and an effect estimate component, enabling more accurate and interpretable inference aligned with the domain expert principles. We train the numeric data extraction model using different strategies, including supervised fine-tuning (SFT) and reinforcement learning (RL) with a new value reward model.\n  When evaluated on the CochraneForest benchmark, our best-performing approach -- using RL to train a small-scale number extraction model -- yields up to a 21% absolute improvement in F1 score over retrieval-based systems and outperforms general-purpose LLMs of over 400B parameters by up to 9% on the RCTs benchmark. Our results demonstrate the promise of reasoning-driven approaches for automating systematic evidence synthesis.", "date": "2025-05-28", "categories": []}
{"id": "2601.16711", "title": "Better Generalizing to Unseen Concepts: An Evaluation Framework and An LLM-Based Auto-Labeled Pipeline for Biomedical Concept Recognition", "abstract": "Generalization to unseen concepts is a central challenge due to the scarcity of human annotations in Mention-agnostic Biomedical Concept Recognition (MA-BCR). This work makes two key contributions to systematically address this issue. First, we propose an evaluation framework built on hierarchical concept indices and novel metrics to measure generalization. Second, we explore LLM-based Auto-Labeled Data (ALD) as a scalable resource, creating a task-specific pipeline for its generation. Our research unequivocally shows that while LLM-generated ALD cannot fully substitute for manual annotations, it is a valuable resource for improving generalization, successfully providing models with the broader coverage and structural knowledge needed to approach recognizing unseen concepts. Code and datasets are available at https://github.com/bio-ie-tool/hi-ald.", "date": "2026-01-23", "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": "2601.15558", "title": "From Generation to Collaboration: Using LLMs to Edit for Empathy in Healthcare", "abstract": "Clinical empathy is essential for patient care, but physicians need continually balance emotional warmth with factual precision under the cognitive and emotional constraints of clinical practice. This study investigates how large language models (LLMs) can function as empathy editors, refining physicians' written responses to enhance empathetic tone while preserving underlying medical information. More importantly, we introduce novel quantitative metrics, an Empathy Ranking Score and a MedFactChecking Score to systematically assess both emotional and factual quality of the responses. Experimental results show that LLM edited responses significantly increase perceived empathy while preserving factual accuracy compared with fully LLM generated outputs. These findings suggest that using LLMs as editorial assistants, rather than autonomous generators, offers a safer, more effective pathway to empathetic and trustworthy AI-assisted healthcare communication.", "date": "2026-01-22", "categories": []}
{"id": "2601.15846", "title": "Determinants of Training Corpus Size for Clinical Text Classification", "abstract": "Introduction: Clinical text classification using natural language processing (NLP) models requires adequate training data to achieve optimal performance. For that, 200-500 documents are typically annotated. The number is constrained by time and costs and lacks justification of the sample size requirements and their relationship to text vocabulary properties.\n  Methods: Using the publicly available MIMIC-III dataset containing hospital discharge notes with ICD-9 diagnoses as labels, we employed pre-trained BERT embeddings followed by Random Forest classifiers to identify 10 randomly selected diagnoses, varying training corpus sizes from 100 to 10,000 documents, and analyzed vocabulary properties by identifying strong and noisy predictive words through Lasso logistic regression on bag-of-words embeddings.\n  Results: Learning curves varied significantly across the 10 classification tasks despite identical preprocessing and algorithms, with 600 documents sufficient to achieve 95% of the performance attainable with 10,000 documents for all tasks. Vocabulary analysis revealed that more strong predictors and fewer noisy predictors were associated with steeper learning curves, where every 100 additional noisy words decreased accuracy by approximately 0.02 while 100 additional strong predictors increased maximum accuracy by approximately 0.04.", "date": "2026-01-22", "categories": []}
{"id": "2503.18085", "title": "Temporal Relation Extraction in Clinical Texts: A Span-based Graph Transformer Approach", "abstract": "Temporal information extraction from unstructured text is essential for contextualizing events and deriving actionable insights, particularly in the medical domain. We address the task of extracting clinical events and their temporal relations using the well-studied I2B2 2012 Temporal Relations Challenge corpus. This task is inherently challenging due to complex clinical language, long documents, and sparse annotations. We introduce GRAPHTREX, a novel method integrating span-based entity-relation extraction, clinical large pre-trained language models (LPLMs), and Heterogeneous Graph Transformers (HGT) to capture local and global dependencies. Our HGT component facilitates information propagation across the document through innovative global landmarks that bridge distant entities. Our method improves the state-of-the-art with 5.5% improvement in the tempeval $F_1$ score over the previous best and up to 8.9% improvement on long-range relations, which presents a formidable challenge. We further demonstrate generalizability by establishing a strong baseline on the E3C corpus. This work not only advances temporal information extraction but also lays the groundwork for improved diagnostic and prognostic models through enhanced temporal reasoning.", "date": "2025-03-23", "categories": []}
{"id": "2601.14478", "title": "Large Language Models for Large-Scale, Rigorous Qualitative Analysis in Applied Health Services Research", "abstract": "Large language models (LLMs) show promise for improving the efficiency of qualitative analysis in large, multi-site health-services research. Yet methodological guidance for LLM integration into qualitative analysis and evidence of their impact on real-world research methods and outcomes remain limited. We developed a model- and task-agnostic framework for designing human-LLM qualitative analysis methods to support diverse analytic aims. Within a multi-site study of diabetes care at Federally Qualified Health Centers (FQHCs), we leveraged the framework to implement human-LLM methods for (1) qualitative synthesis of researcher-generated summaries to produce comparative feedback reports and (2) deductive coding of 167 interview transcripts to refine a practice-transformation intervention. LLM assistance enabled timely feedback to practitioners and the incorporation of large-scale qualitative data to inform theory and practice changes. This work demonstrates how LLMs can be integrated into applied health-services research to enhance efficiency while preserving rigor, offering guidance for continued innovation with LLMs in qualitative research.", "date": "2026-01-20", "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": "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": "2601.12648", "title": "Intelligent Documentation in Medical Education: Can AI Replace Manual Case Logging?", "abstract": "Procedural case logs are a core requirement in radiology training, yet they are time-consuming to complete and prone to inconsistency when authored manually. This study investigates whether large language models (LLMs) can automate procedural case log documentation directly from free-text radiology reports. We evaluate multiple local and commercial LLMs under instruction-based and chain-of-thought prompting to extract structured procedural information from 414 curated interventional radiology reports authored by nine residents between 2018 and 2024. Model performance is assessed using sensitivity, specificity, and F1-score, alongside inference latency and token efficiency to estimate operational cost. Results show that both local and commercial models achieve strong extraction performance, with best F1-scores approaching 0.87, while exhibiting different trade-offs between speed and cost. Automation using LLMs has the potential to substantially reduce clerical burden for trainees and improve consistency in case logging. These findings demonstrate the feasibility of AI-assisted documentation in medical education and highlight the need for further validation across institutions and clinical workflows.", "date": "2026-01-19", "categories": []}
{"id": "2601.12812", "title": "Do Clinical Question Answering Systems Really Need Specialised Medical Fine Tuning?", "abstract": "Clinical Question-Answering (CQA) industry systems are increasingly rely on Large Language Models (LLMs), yet their deployment is often guided by the assumption that domain-specific fine-tuning is essential. Although specialised medical LLMs such as BioBERT, BioGPT, and PubMedBERT remain popular, they face practical limitations including narrow coverage, high retraining costs, and limited adaptability. Efforts based on Supervised Fine-Tuning (SFT) have attempted to address these assumptions but continue to reinforce what we term the SPECIALISATION FALLACY-the belief that specialised medical LLMs are inherently superior for CQA. To address this assumption, we introduce MEDASSESS-X, a deployment-industry-oriented CQA framework that applies alignment at inference time rather than through SFT. MEDASSESS-X uses lightweight steering vectors to guide model activations toward medically consistent reasoning without updating model weights or requiring domain-specific retraining. This inference-time alignment layer stabilises CQA performance across both general-purpose and specialised medical LLMs, thereby resolving the SPECIALISATION FALLACY. Empirically, MEDASSESS-X delivers consistent gains across all LLM families, improving Accuracy by up to +6%, Factual Consistency by +7%, and reducing Safety Error Rate by as much as 50%.", "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": "2601.13918", "title": "AgentEHR: Advancing Autonomous Clinical Decision-Making via Retrospective Summarization", "abstract": "Large Language Models have demonstrated profound utility in the medical domain. However, their application to autonomous Electronic Health Records~(EHRs) navigation remains constrained by a reliance on curated inputs and simplified retrieval tasks. To bridge the gap between idealized experimental settings and realistic clinical environments, we present AgentEHR. This benchmark challenges agents to execute complex decision-making tasks, such as diagnosis and treatment planning, requiring long-range interactive reasoning directly within raw and high-noise databases. In tackling these tasks, we identify that existing summarization methods inevitably suffer from critical information loss and fractured reasoning continuity. To address this, we propose RetroSum, a novel framework that unifies a retrospective summarization mechanism with an evolving experience strategy. By dynamically re-evaluating interaction history, the retrospective mechanism prevents long-context information loss and ensures unbroken logical coherence. Additionally, the evolving strategy bridges the domain gap by retrieving accumulated experience from a memory bank. Extensive empirical evaluations demonstrate that RetroSum achieves performance gains of up to 29.16% over competitive baselines, while significantly decreasing total interaction errors by up to 92.3%.", "date": "2026-01-20", "categories": []}
{"id": "2601.11344", "title": "How Much Would a Clinician Edit This Draft? Evaluating LLM Alignment for Patient Message Response Drafting", "abstract": "Large language models (LLMs) show promise in drafting responses to patient portal messages, yet their integration into clinical workflows raises various concerns, including whether they would actually save clinicians time and effort in their portal workload. We investigate LLM alignment with individual clinicians through a comprehensive evaluation of the patient message response drafting task. We develop a novel taxonomy of thematic elements in clinician responses and propose a novel evaluation framework for assessing clinician editing load of LLM-drafted responses at both content and theme levels. We release an expert-annotated dataset and conduct large-scale evaluations of local and commercial LLMs using various adaptation techniques including thematic prompting, retrieval-augmented generation, supervised fine-tuning, and direct preference optimization. Our results reveal substantial epistemic uncertainty in aligning LLM drafts with clinician responses. While LLMs demonstrate capability in drafting certain thematic elements, they struggle with clinician-aligned generation in other themes, particularly question asking to elicit further information from patients. Theme-driven adaptation strategies yield improvements across most themes. Our findings underscore the necessity of adapting LLMs to individual clinician preferences to enable reliable and responsible use in patient-clinician communication workflows.", "date": "2026-01-16", "categories": []}
{"id": "2601.11488", "title": "CTest-Metric: A Unified Framework to Assess Clinical Validity of Metrics for CT Report Generation", "abstract": "In the generative AI era, where even critical medical tasks are increasingly automated, radiology report generation (RRG) continues to rely on suboptimal metrics for quality assessment. Developing domain-specific metrics has therefore been an active area of research, yet it remains challenging due to the lack of a unified, well-defined framework to assess their robustness and applicability in clinical contexts. To address this, we present CTest-Metric, a first unified metric assessment framework with three modules determining the clinical feasibility of metrics for CT RRG. The modules test: (i) Writing Style Generalizability (WSG) via LLM-based rephrasing; (ii) Synthetic Error Injection (SEI) at graded severities; and (iii) Metrics-vs-Expert correlation (MvE) using clinician ratings on 175 \"disagreement\" cases. Eight widely used metrics (BLEU, ROUGE, METEOR, BERTScore-F1, F1-RadGraph, RaTEScore, GREEN Score, CRG) are studied across seven LLMs built on a CT-CLIP encoder. Using our novel framework, we found that lexical NLG metrics are highly sensitive to stylistic variations; GREEN Score aligns best with expert judgments (Spearman~0.70), while CRG shows negative correlation; and BERTScore-F1 is least sensitive to factual error injection. We will release the framework, code, and allowable portion of the anonymized evaluation data (rephrased/error-injected CT reports), to facilitate reproducible benchmarking and future metric development.", "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": "2505.20323", "title": "PMOA-TTS: Introducing the PubMed Open Access Textual Times Series Corpus", "abstract": "Clinical narratives encode temporal dynamics essential for modeling patient trajectories, yet large-scale temporally annotated resources are scarce. We introduce PMOA-TTS, a corpus of 124,699 single-patient PubMed Open Access case reports converted into structured textual timelines of (event, time) pairs using a scalable large-language-model pipeline (Llama 3.3 70B and DeepSeek-R1). The corpus comprises over 5.6 million timestamped events, alongside extracted demographics and diagnoses. Technical validation uses a clinician-curated gold set and three measures: semantic event matching, temporal concordance (c-index), and alignment error summarized with Area Under the Log-Time CDF (AULTC). We benchmark alternative prompting and model choices and provide documentation to support reproduction. PMOA-TTS enables research on timeline extraction, temporal reasoning, survival modeling and event forecasting from narrative text, and offers broad diagnostic and demographic coverage. Data and code are openly available in public repositories.", "date": "2025-05-23", "categories": []}
{"id": "2509.10222", "title": "Compartmentalised Agentic Reasoning for Clinical NLI", "abstract": "Large language models can produce fluent judgments for clinical natural language inference, yet they frequently fail when the decision requires the correct inferential schema rather than surface matching. We introduce CARENLI, a compartmentalised agentic framework that routes each premise-statement pair to a reasoning family and then applies a specialised solver with explicit verification and targeted refinement. We evaluate on an expanded CTNLI benchmark of 200 instances spanning four reasoning families: Causal Attribution, Compositional Grounding, Epistemic Verification, and Risk State Abstraction. Across four contemporary backbone models, CARENLI improves mean accuracy from about 23% with direct prompting to about 57%, a gain of roughly 34 points, with the largest benefits on structurally demanding reasoning types. These results support compartmentalisation plus verification as a practical route to more reliable and auditable clinical inference.", "date": "2025-09-12", "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": "2601.09730", "title": "Clinical Document Metadata Extraction: A Scoping Review", "abstract": "Clinical document metadata, such as document type, structure, author role, medical specialty, and encounter setting, is essential for accurate interpretation of information captured in clinical documents. However, vast documentation heterogeneity and drift over time challenge harmonization of document metadata. Automated extraction methods have emerged to coalesce metadata from disparate practices into target schema. This scoping review aims to catalog research on clinical document metadata extraction, identify methodological trends and applications, and highlight gaps. We followed the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines to identify articles that perform clinical document metadata extraction. We initially found and screened 266 articles published between January 2011 and August 2025, then comprehensively reviewed 67 we deemed relevant to our study. Among the articles included, 45 were methodological, 17 used document metadata as features in a downstream application, and 5 analyzed document metadata composition. We observe myriad purposes for methodological study and application types. Available labelled public data remains sparse except for structural section datasets. Methods for extracting document metadata have progressed from largely rule-based and traditional machine learning with ample feature engineering to transformer-based architectures with minimal feature engineering. The emergence of large language models has enabled broader exploration of generalizability across tasks and datasets, allowing the possibility of advanced clinical text processing systems. We anticipate that research will continue to expand into richer document metadata representations and integrate further into clinical applications and workflows.", "date": "2025-12-28", "categories": []}
{"id": "2601.09876", "title": "Patient-Similarity Cohort Reasoning in Clinical Text-to-SQL", "abstract": "Real-world clinical text-to-SQL requires reasoning over heterogeneous EHR tables, temporal windows, and patient-similarity cohorts to produce executable queries. We introduce CLINSQL, a benchmark of 633 expert-annotated tasks on MIMIC-IV v3.1 that demands multi-table joins, clinically meaningful filters, and executable SQL. Solving CLINSQL entails navigating schema metadata and clinical coding systems, handling long contexts, and composing multi-step queries beyond traditional text-to-SQL. We evaluate 22 proprietary and open-source models under Chain-of-Thought self-refinement and use rubric-based SQL analysis with execution checks that prioritize critical clinical requirements. Despite recent advances, performance remains far from clinical reliability: on the test set, GPT-5-mini attains 74.7% execution score, DeepSeek-R1 leads open-source at 69.2% and Gemini-2.5-Pro drops from 85.5% on Easy to 67.2% on Hard. Progress on CLINSQL marks tangible advances toward clinically reliable text-to-SQL for real-world EHR analytics.", "date": "2026-01-14", "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": "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": "2601.09367", "title": "Relation Extraction Capabilities of LLMs on Clinical Text: A Bilingual Evaluation for English and Turkish", "abstract": "The scarcity of annotated datasets for clinical information extraction in non-English languages hinders the evaluation of large language model (LLM)-based methods developed primarily in English. In this study, we present the first comprehensive bilingual evaluation of LLMs for the clinical Relation Extraction (RE) task in both English and Turkish. To facilitate this evaluation, we introduce the first English-Turkish parallel clinical RE dataset, derived and carefully curated from the 2010 i2b2/VA relation classification corpus. We systematically assess a diverse set of prompting strategies, including multiple in-context learning (ICL) and Chain-of-Thought (CoT) approaches, and compare their performance to fine-tuned baselines such as PURE. Furthermore, we propose Relation-Aware Retrieval (RAR), a novel in-context example selection method based on contrastive learning, that is specifically designed to capture both sentence-level and relation-level semantics. Our results show that prompting-based LLM approaches consistently outperform traditional fine-tuned models. Moreover, evaluations for English performed better than their Turkish counterparts across all evaluated LLMs and prompting techniques. Among ICL methods, RAR achieves the highest performance, with Gemini 1.5 Flash reaching a micro-F1 score of 0.906 in English and 0.888 in Turkish. Performance further improves to 0.918 F1 in English when RAR is combined with a structured reasoning prompt using the DeepSeek-V3 model. These findings highlight the importance of high-quality demonstration retrieval and underscore the potential of advanced retrieval and prompting techniques to bridge resource gaps in clinical natural language processing.", "date": "2026-01-14", "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": "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": "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": "2601.05376", "title": "The Persona Paradox: Medical Personas as Behavioral Priors in Clinical Language Models", "abstract": "Persona conditioning can be viewed as a behavioral prior for large language models (LLMs) and is often assumed to confer expertise and improve safety in a monotonic manner. However, its effects on high-stakes clinical decision-making remain poorly characterized. We systematically evaluate persona-based control in clinical LLMs, examining how professional roles (e.g., Emergency Department physician, nurse) and interaction styles (bold vs.\\ cautious) influence behavior across models and medical tasks. We assess performance on clinical triage and patient-safety tasks using multidimensional evaluations that capture task accuracy, calibration, and safety-relevant risk behavior. We find systematic, context-dependent, and non-monotonic effects: Medical personas improve performance in critical care tasks, yielding gains of up to $\\sim+20\\%$ in accuracy and calibration, but degrade performance in primary-care settings by comparable margins. Interaction style modulates risk propensity and sensitivity, but it's highly model-dependent. While aggregated LLM-judge rankings favor medical over non-medical personas in safety-critical cases, we found that human clinicians show moderate agreement on safety compliance (average Cohen's $\\kappa = 0.43$) but indicate a low confidence in 95.9\\% of their responses on reasoning quality. Our work shows that personas function as behavioral priors that introduce context-dependent trade-offs rather than guarantees of safety or expertise. The code is available at https://github.com/rsinghlab/Persona\\_Paradox.", "date": "2026-01-08", "categories": []}
{"id": "2506.03259", "title": "Evaluating Large Language Models for Zero-Shot Disease Labeling in CT Radiology Reports Across Organ Systems", "abstract": "Purpose: This study aims to evaluate the effectiveness of large language models (LLMs) in automating disease annotation of CT radiology reports. We compare a rule-based algorithm (RBA), RadBERT, and three lightweight open-weight LLMs for multi-disease labeling of chest, abdomen, and pelvis (CAP) CT reports.\n  Materials and Methods: This retrospective study analyzed 40,833 chest-abdomen-pelvis (CAP) CT reports from 29,540 patients, with 1,789 reports manually annotated across three organ systems. External validation was conducted using the CT RATE dataset. Three open-weight LLMs were tested with zero-shot prompting. Performance was evaluated using Cohen's Kappa ($\\kappa$) and micro/macro-averaged F1 scores.\n  Results: In the internal test set of 12,197 CAP reports from 8,854 patients, Llama-3.1 8B and Gemma-3 27B showed the highest agreement ($\\kappa$ median: 0.87). On the manually annotated set, Gemma-3 27B achieved the top macro-F1 (0.82), followed by Llama-3.1 8B (0.79), while the RBA scored lowest (0.64). On the CT RATE dataset (lungs/pleura labels only), Llama-3.1 8B performed best (0.91), with Gemma-3 27B close behind (0.89). Performance differences were mainly due to differing labeling practices, especially for labels with high subjectivity such as atelectasis.\n  Conclusion: Lightweight LLMs outperform rule-based methods for CT report annotation and generalize across organ systems with zero-shot prompting. However, binary labels alone cannot capture the full nuance of report language. LLMs can provide a flexible, efficient solution aligned with clinical judgment and user needs.", "date": "2025-06-03", "categories": []}
{"id": "2504.01216", "title": "Detecting PTSD in Clinical Interviews: A Comparative Analysis of NLP Methods and Large Language Models", "abstract": "Post-Traumatic Stress Disorder (PTSD) remains underdiagnosed in clinical settings, presenting opportunities for automated detection to identify patients. This study evaluates natural language processing approaches for detecting PTSD from clinical interview transcripts. We compared general and mental health-specific transformer models (BERT/RoBERTa), embedding-based methods (SentenceBERT/LLaMA), and large language model prompting strategies (zero-shot/few-shot/chain-of-thought) using the DAIC-WOZ dataset. Domain-specific end-to-end models significantly outperformed general models (Mental-RoBERTa AUPRC=0.675+/-0.084 vs. RoBERTa-base 0.599+/-0.145). SentenceBERT embeddings with neural networks achieved the highest overall performance (AUPRC=0.758+/-0.128). Few-shot prompting using DSM-5 criteria yielded competitive results with two examples (AUPRC=0.737). Performance varied significantly across symptom severity and comorbidity status with depression, with higher accuracy for severe PTSD cases and patients with comorbid depression. Our findings highlight the potential of domain-adapted embeddings and LLMs for scalable screening while underscoring the need for improved detection of nuanced presentations and offering insights for developing clinically viable AI tools for PTSD assessment.", "date": "2025-04-01", "categories": []}
{"id": "2508.13798", "title": "TracSum: A New Benchmark for Aspect-Based Summarization with Sentence-Level Traceability in Medical Domain", "abstract": "While document summarization with LLMs has enhanced access to textual information, concerns about the factual accuracy of these summaries persist, especially in the medical domain. Tracing evidence from which summaries are derived enables users to assess their accuracy, thereby alleviating this concern. In this paper, we introduce TracSum, a novel benchmark for traceable, aspect-based summarization, in which generated summaries are paired with sentence-level citations, enabling users to trace back to the original context. First, we annotate 500 medical abstracts for seven key medical aspects, yielding 3.5K summary-citation pairs. We then propose a fine-grained evaluation framework for this new task, designed to assess the completeness and consistency of generated content using four metrics. Finally, we introduce a summarization pipeline, Track-Then-Sum, which serves as a baseline method for comparison. In experiments, we evaluate both this baseline and a set of LLMs on TracSum, and conduct a human evaluation to assess the evaluation results. The findings demonstrate that TracSum can serve as an effective benchmark for traceable, aspect-based summarization tasks. We also observe that explicitly performing sentence-level tracking prior to summarization enhances generation accuracy, while incorporating the full context further improves completeness.", "date": "2025-08-19", "categories": []}
{"id": "2601.03418", "title": "PCoA: A New Benchmark for Medical Aspect-Based Summarization With Phrase-Level Context Attribution", "abstract": "Verifying system-generated summaries remains challenging, as effective verification requires precise attribution to the source context, which is especially crucial in high-stakes medical domains. To address this challenge, we introduce PCoA, an expert-annotated benchmark for medical aspect-based summarization with phrase-level context attribution. PCoA aligns each aspect-based summary with its supporting contextual sentences and contributory phrases within them. We further propose a fine-grained, decoupled evaluation framework that independently assesses the quality of generated summaries, citations, and contributory phrases. Through extensive experiments, we validate the quality and consistency of the PCoA dataset and benchmark several large language models on the proposed task. Experimental results demonstrate that PCoA provides a reliable benchmark for evaluating system-generated summaries with phrase-level context attribution. Furthermore, comparative experiments show that explicitly identifying relevant sentences and contributory phrases before summarization can improve overall quality. The data and code are available at https://github.com/chubohao/PCoA.", "date": "2026-01-06", "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": "2601.03232", "title": "Multi-RADS Synthetic Radiology Report Dataset and Head-to-Head Benchmarking of 41 Open-Weight and Proprietary Language Models", "abstract": "Background: Reporting and Data Systems (RADS) standardize radiology risk communication but automated RADS assignment from narrative reports is challenging because of guideline complexity, output-format constraints, and limited benchmarking across RADS frameworks and model sizes. Purpose: To create RXL-RADSet, a radiologist-verified synthetic multi-RADS benchmark, and compare validity and accuracy of open-weight small language models (SLMs) with a proprietary model for RADS assignment. Materials and Methods: RXL-RADSet contains 1,600 synthetic radiology reports across 10 RADS (BI-RADS, CAD-RADS, GB-RADS, LI-RADS, Lung-RADS, NI-RADS, O-RADS, PI-RADS, TI-RADS, VI-RADS) and multiple modalities. Reports were generated by LLMs using scenario plans and simulated radiologist styles and underwent two-stage radiologist verification. We evaluated 41 quantized SLMs (12 families, 0.135-32B parameters) and GPT-5.2 under a fixed guided prompt. Primary endpoints were validity and accuracy; a secondary analysis compared guided versus zero-shot prompting. Results: Under guided prompting GPT-5.2 achieved 99.8% validity and 81.1% accuracy (1,600 predictions). Pooled SLMs (65,600 predictions) achieved 96.8% validity and 61.1% accuracy; top SLMs in the 20-32B range reached ~99% validity and mid-to-high 70% accuracy. Performance scaled with model size (inflection between <1B and >=10B) and declined with RADS complexity primarily due to classification difficulty rather than invalid outputs. Guided prompting improved validity (99.2% vs 96.7%) and accuracy (78.5% vs 69.6%) compared with zero-shot. Conclusion: RXL-RADSet provides a radiologist-verified multi-RADS benchmark; large SLMs (20-32B) can approach proprietary-model performance under guided prompting, but gaps remain for higher-complexity schemes.", "date": "2026-01-06", "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": "2601.01844", "title": "Clinical Knowledge Graph Construction and Evaluation with Multi-LLMs via Retrieval-Augmented Generation", "abstract": "Large language models (LLMs) offer new opportunities for constructing knowledge graphs (KGs) from unstructured clinical narratives. However, existing approaches often rely on structured inputs and lack robust validation of factual accuracy and semantic consistency, limitations that are especially problematic in oncology. We introduce an end-to-end framework for clinical KG construction and evaluation directly from free text using multi-agent prompting and a schema-constrained Retrieval-Augmented Generation (KG-RAG) strategy. Our pipeline integrates (1) prompt-driven entity, attribute, and relation extraction; (2) entropy-based uncertainty scoring; (3) ontology-aligned RDF/OWL schema generation; and (4) multi-LLM consensus validation for hallucination detection and semantic refinement. Beyond static graph construction, the framework supports continuous refinement and self-supervised evaluation, enabling iterative improvement of graph quality. Applied to two oncology cohorts (PDAC and BRCA), our method produces interpretable, SPARQL-compatible, and clinically grounded knowledge graphs without relying on gold-standard annotations. Experimental results demonstrate consistent gains in precision, relevance, and ontology compliance over baseline methods.", "date": "2026-01-05", "categories": []}
{"id": "2601.00787", "title": "Adapting Natural Language Processing Models Across Jurisdictions: A pilot Study in Canadian Cancer Registries", "abstract": "Population-based cancer registries depend on pathology reports as their primary diagnostic source, yet manual abstraction is resource-intensive and contributes to delays in cancer data. While transformer-based NLP systems have improved registry workflows, their ability to generalize across jurisdictions with differing reporting conventions remains poorly understood. We present the first cross-provincial evaluation of adapting BCCRTron, a domain-adapted transformer model developed at the British Columbia Cancer Registry, alongside GatorTron, a biomedical transformer model, for cancer surveillance in Canada. Our training dataset consisted of approximately 104,000 and 22,000 de-identified pathology reports from the Newfoundland & Labrador Cancer Registry (NLCR) for Tier 1 (cancer vs. non-cancer) and Tier 2 (reportable vs. non-reportable) tasks, respectively. Both models were fine-tuned using complementary synoptic and diagnosis focused report section input pipelines. Across NLCR test sets, the adapted models maintained high performance, demonstrating transformers pretrained in one jurisdiction can be localized to another with modest fine-tuning. To improve sensitivity, we combined the two models using a conservative OR-ensemble achieving a Tier 1 recall of 0.99 and reduced missed cancers to 24, compared with 48 and 54 for the standalone models. For Tier 2, the ensemble achieved 0.99 recall and reduced missed reportable cancers to 33, compared with 54 and 46 for the individual models. These findings demonstrate that an ensemble combining complementary text representations substantially reduce missed cancers and improve error coverage in cancer-registry NLP. We implement a privacy-preserving workflow in which only model weights are shared between provinces, supporting interoperable NLP infrastructure and a future pan-Canadian foundation model for cancer pathology and registry workflows.", "date": "2026-01-02", "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": "2512.22795", "title": "CNSight: Evaluation of Clinical Note Segmentation Tools", "abstract": "Clinical notes are often stored in unstructured or semi-structured formats after extraction from electronic medical record (EMR) systems, which complicates their use for secondary analysis and downstream clinical applications. Reliable identification of section boundaries is a key step toward structuring these notes, as sections such as history of present illness, medications, and discharge instructions each provide distinct clinical contexts. In this work, we evaluate rule-based baselines, domain-specific transformer models, and large language models for clinical note segmentation using a curated dataset of 1,000 notes from MIMIC-IV. Our experiments show that large API-based models achieve the best overall performance, with GPT-5-mini reaching a best average F1 of 72.4 across sentence-level and freetext segmentation. Lightweight baselines remain competitive on structured sentence-level tasks but falter on unstructured freetext. Our results provide guidance for method selection and lay the groundwork for downstream tasks such as information extraction, cohort identification, and automated summarization.", "date": "2025-12-28", "categories": []}
{"id": "2512.19864", "title": "HARMON-E: Hierarchical Agentic Reasoning for Multimodal Oncology Notes to Extract Structured Data", "abstract": "Unstructured notes within the electronic health record (EHR) contain rich clinical information vital for cancer treatment decision making and research, yet reliably extracting structured oncology data remains challenging due to extensive variability, specialized terminology, and inconsistent document formats. Manual abstraction, although accurate, is prohibitively costly and unscalable. Existing automated approaches typically address narrow scenarios - either using synthetic datasets, restricting focus to document-level extraction, or isolating specific clinical variables (e.g., staging, biomarkers, histology) - and do not adequately handle patient-level synthesis across the large number of clinical documents containing contradictory information. In this study, we propose an agentic framework that systematically decomposes complex oncology data extraction into modular, adaptive tasks. Specifically, we use large language models (LLMs) as reasoning agents, equipped with context-sensitive retrieval and iterative synthesis capabilities, to exhaustively and comprehensively extract structured clinical variables from real-world oncology notes. Evaluated on a large-scale dataset of over 400,000 unstructured clinical notes and scanned PDF reports spanning 2,250 cancer patients, our method achieves an average F1-score of 0.93, with 100 out of 103 oncology-specific clinical variables exceeding 0.85, and critical variables (e.g., biomarkers and medications) surpassing 0.95. Moreover, integration of the agentic system into a data curation workflow resulted in 0.94 direct manual approval rate, significantly reducing annotation costs. To our knowledge, this constitutes the first exhaustive, end-to-end application of LLM-based agents for structured oncology data extraction at scale", "date": "2025-12-22", "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": "2512.20520", "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": "2412.12422", "title": "FactEHR: A Dataset for Evaluating Factuality in Clinical Notes Using LLMs", "abstract": "Verifying and attributing factual claims is essential for the safe and effective use of large language models (LLMs) in healthcare. A core component of factuality evaluation is fact decomposition, the process of breaking down complex clinical statements into fine-grained atomic facts for verification. Recent work has proposed fact decomposition, which uses LLMs to rewrite source text into concise sentences conveying a single piece of information, to facilitate fine-grained fact verification. However, clinical documentation poses unique challenges for fact decomposition due to dense terminology and diverse note types and remains understudied. To address this gap and explore these challenges, we present FactEHR, an NLI dataset consisting of document fact decompositions for 2,168 clinical notes spanning four types from three hospital systems, resulting in 987,266 entailment pairs. We assess the generated facts on different axes, from entailment evaluation of LLMs to a qualitative analysis. Our evaluation, including review by the clinicians, reveals substantial variability in LLM performance for fact decomposition. For example, Gemini-1.5-Flash consistently generates relevant and accurate facts, while Llama-3 8B produces fewer and less consistent outputs. The results underscore the need for better LLM capabilities to support factual verification in clinical text.", "date": "2024-12-17", "categories": []}
{"id": "2412.17669", "title": "Generating Completions for Broca's Aphasic Sentences Using Large Language Models", "abstract": "Broca's aphasia is a type of aphasia characterized by non-fluent, effortful and agrammatic speech production with relatively good comprehension. Since traditional aphasia treatment methods are often time-consuming, labour-intensive, and do not reflect real-world conversations, applying natural language processing based approaches such as Large Language Models (LLMs) could potentially contribute to improving existing treatment approaches. To address this issue, we explore the use of sequence-to-sequence LLMs for completing Broca's aphasic sentences. We first generate synthetic Broca's aphasic data using a rule-based system designed to mirror the linguistic characteristics of Broca's aphasic speech. Using this synthetic data (without authentic aphasic samples), we then fine-tune four pre-trained LLMs on the task of completing agrammatic sentences. We evaluate our fine-tuned models on both synthetic and authentic Broca's aphasic data. We demonstrate LLMs' capability for reconstructing agrammatic sentences, with the models showing improved performance with longer input utterances. Our result highlights the LLMs' potential in advancing communication aids for individuals with Broca's aphasia and possibly other clinical populations.", "date": "2024-12-23", "categories": []}
{"id": "2511.06000", "title": "LLMs Do Not See Age: Assessing Demographic Bias in Automated Systematic Review Synthesis", "abstract": "Clinical interventions often hinge on age: medications and procedures safe for adults may be harmful to children or ineffective for older adults. However, as language models are increasingly integrated into biomedical evidence synthesis workflows, it remains uncertain whether these systems preserve such crucial demographic distinctions. To address this gap, we evaluate how well state-of-the-art language models retain age-related information when generating abstractive summaries of biomedical studies. We construct DemogSummary, a novel age-stratified dataset of systematic review primary studies, covering child, adult, and older adult populations. We evaluate three prominent summarisation-capable LLMs, Qwen (open-source), Longformer (open-source) and GPT-4.1 Nano (proprietary), using both standard metrics and a newly proposed Demographic Salience Score (DSS), which quantifies age-related entity retention and hallucination. Our results reveal systematic disparities across models and age groups: demographic fidelity is lowest for adult-focused summaries, and under-represented populations are more prone to hallucinations. These findings highlight the limitations of current LLMs in faithful and bias-free summarisation and point to the need for fairness-aware evaluation frameworks and summarisation pipelines in biomedical NLP.", "date": "2025-11-08", "categories": []}
{"id": "2512.17769", "title": "Bangla MedER: Multi-BERT Ensemble Approach for the Recognition of Bangla Medical Entity", "abstract": "Medical Entity Recognition (MedER) is an essential NLP task for extracting meaningful entities from the medical corpus. Nowadays, MedER-based research outcomes can remarkably contribute to the development of automated systems in the medical sector, ultimately enhancing patient care and outcomes. While extensive research has been conducted on MedER in English, low-resource languages like Bangla remain underexplored. Our work aims to bridge this gap. For Bangla medical entity recognition, this study first examined a number of transformer models, including BERT, DistilBERT, ELECTRA, and RoBERTa. We also propose a novel Multi-BERT Ensemble approach that outperformed all baseline models with the highest accuracy of 89.58%. Notably, it provides an 11.80% accuracy improvement over the single-layer BERT model, demonstrating its effectiveness for this task. A major challenge in MedER for low-resource languages is the lack of annotated datasets. To address this issue, we developed a high-quality dataset tailored for the Bangla MedER task. The dataset was used to evaluate the effectiveness of our model through multiple performance metrics, demonstrating its robustness and applicability. Our findings highlight the potential of Multi-BERT Ensemble models in improving MedER for Bangla and set the foundation for further advancements in low-resource medical NLP.", "date": "2025-12-19", "categories": []}
{"id": "2512.15259", "title": "SynGP500: A Clinically-Grounded Synthetic Dataset of Australian General Practice Medical Notes", "abstract": "We introduce SynGP500, a clinician-curated collection of 500 synthetic Australian general practice medical notes. The dataset integrates curriculum-based clinical breadth (RACGP 2022 Curriculum), epidemiologically-calibrated prevalence (BEACH study), and diverse consultation contexts. This approach systematically includes both common presentations and less-common curriculum-specified conditions that GPs must recognize but appear infrequently in single practice populations, potentially supporting more generalizable model training than datasets constrained by naturally occurring case distributions. SynGP500 is messy by design, reflecting the authentic complexity of healthcare delivery: telegraphic documentation, typos, patient non-adherence, socioeconomic barriers, and clinician-patient disagreements, unlike sanitized synthetic datasets that obscure clinical realities. Multi-faceted validation demonstrates dataset quality through epidemiological alignment with real Australian GP consultation patterns (BEACH study), stylometric analysis confirming high linguistic variation, semantic diversity analysis demonstrating broad coverage, and exploratory downstream evaluation using self-supervised medical concept extraction, showing F1 improvements. SynGP500 addresses a critical national gap, providing researchers and educators with a resource for developing and evaluating clinical NLP methods for Australian general practice while inherently protecting patient privacy.", "date": "2025-12-17", "categories": []}
{"id": "2512.15601", "title": "You Never Know a Person, You Only Know Their Defenses: Detecting Levels of Psychological Defense Mechanisms in Supportive Conversations", "abstract": "Psychological defenses are strategies, often automatic, that people use to manage distress. Rigid or overuse of defenses is negatively linked to mental health and shapes what speakers disclose and how they accept or resist help. However, defenses are complex and difficult to reliably measure, particularly in clinical dialogues. We introduce PsyDefConv, a dialogue corpus with help seeker utterances labeled for defense level, and DMRS Co-Pilot, a four-stage pipeline that provides evidence-based pre-annotations. The corpus contains 200 dialogues and 4709 utterances, including 2336 help seeker turns, with labeling and Cohen's kappa 0.639. In a counterbalanced study, the co-pilot reduced average annotation time by 22.4%. In expert review, it averaged 4.62 for evidence, 4.44 for clinical plausibility, and 4.40 for insight on a seven-point scale. Benchmarks with strong language models in zero-shot and fine-tuning settings demonstrate clear headroom, with the best macro F1-score around 30% and a tendency to overpredict mature defenses. Corpus analyses confirm that mature defenses are most common and reveal emotion-specific deviations. We will release the corpus, annotations, code, and prompts to support research on defensive functioning in language.", "date": "2025-12-17", "categories": []}
{"id": "2505.16097", "title": "Developing Large Language Models for Clinical Research Using One Million Clinical Trials", "abstract": "Developing artificial intelligence (AI) for clinical research requires a comprehensive data foundation that supports model training and rigorous evaluation. Here, we introduce TrialPanorama, a large-scale structured resource that aggregates 1.6M clinical trial records from fifteen global registries and links them with biomedical ontologies and associated literature. To demonstrate its utility, we build a pipeline that constructs 152K training and testing samples for eight key clinical research tasks. Three tasks support systematic review workflows, including study search, study screening, and evidence summarization. Five tasks focus on trial design and optimization, including arm design, eligibility criteria design, endpoint selection, sample size estimation, and trial completion assessment and rationalization. Benchmarking cutting-edge large language models (LLMs) reveals that generic LLMs have limited capability in clinical reasoning. In contrast, an 8B LLM we developed on TrialPanorama using supervised finetuning and reinforcement learning wins over the 70B generic counterparts in all eight tasks, with a relative improvement of 73.7%, 67.6%, 38.4%, 37.8%, 26.5%, 20.7%, 20.0%, 18.1%, and 5.2%, respectively. We envision that TrialPanorama provides a solid foundation for future scaling of AI for clinical research.", "date": "2025-05-22", "categories": []}
{"id": "2512.13700", "title": "Leveraging LLMs for Structured Data Extraction from Unstructured Patient Records", "abstract": "Manual chart review remains an extremely time-consuming and resource-intensive component of clinical research, requiring experts to extract often complex information from unstructured electronic health record (EHR) narratives. We present a secure, modular framework for automated structured feature extraction from clinical notes leveraging locally deployed large language models (LLMs) on institutionally approved, Health Insurance Portability and Accountability Act (HIPPA)-compliant compute infrastructure. This system integrates retrieval augmented generation (RAG) and structured response methods of LLMs into a widely deployable and scalable container to provide feature extraction for diverse clinical domains. In evaluation, the framework achieved high accuracy across multiple medical characteristics present in large bodies of patient notes when compared against an expert-annotated dataset and identified several annotation errors missed in manual review. This framework demonstrates the potential of LLM systems to reduce the burden of manual chart review through automated extraction and increase consistency in data capture, accelerating clinical research.", "date": "2025-12-03", "categories": []}
{"id": "2509.25540", "title": "RadOnc-GPT: An Autonomous LLM Agent for Real-Time Patient Outcomes Labeling at Scale", "abstract": "Manual labeling limits the scale, accuracy, and timeliness of patient outcomes research in radiation oncology. We present RadOnc-GPT, an autonomous large language model (LLM)-based agent capable of independently retrieving patient-specific information, iteratively assessing evidence, and returning structured outcomes. Our evaluation explicitly validates RadOnc-GPT across two clearly defined tiers of increasing complexity: (1) a structured quality assurance (QA) tier, assessing the accurate retrieval of demographic and radiotherapy treatment plan details, followed by (2) a complex clinical outcomes labeling tier involving determination of mandibular osteoradionecrosis (ORN) in head-and-neck cancer patients and detection of cancer recurrence in independent prostate and head-and-neck cancer cohorts requiring combined interpretation of structured and unstructured patient data. The QA tier establishes foundational trust in structured-data retrieval, a critical prerequisite for successful complex clinical outcome labeling.", "date": "2025-09-29", "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": "2512.10967", "title": "ASR Under the Stethoscope: Evaluating Biases in Clinical Speech Recognition across Indian Languages", "abstract": "Automatic Speech Recognition (ASR) is increasingly used to document clinical encounters, yet its reliability in multilingual and demographically diverse Indian healthcare contexts remains largely unknown. In this study, we conduct the first systematic audit of ASR performance on real world clinical interview data spanning Kannada, Hindi, and Indian English, comparing leading models including Indic Whisper, Whisper, Sarvam, Google speech to text, Gemma3n, Omnilingual, Vaani, and Gemini. We evaluate transcription accuracy across languages, speakers, and demographic subgroups, with a particular focus on error patterns affecting patients vs. clinicians and gender based or intersectional disparities. Our results reveal substantial variability across models and languages, with some systems performing competitively on Indian English but failing on code mixed or vernacular speech. We also uncover systematic performance gaps tied to speaker role and gender, raising concerns about equitable deployment in clinical settings. By providing a comprehensive multilingual benchmark and fairness analysis, our work highlights the need for culturally and demographically inclusive ASR development for healthcare ecosystem in India.", "date": "2025-11-30", "categories": []}
{"id": "2512.11437", "title": "CLINIC: Evaluating Multilingual Trustworthiness in Language Models for Healthcare", "abstract": "Integrating language models (LMs) in healthcare systems holds great promise for improving medical workflows and decision-making. However, a critical barrier to their real-world adoption is the lack of reliable evaluation of their trustworthiness, especially in multilingual healthcare settings. Existing LMs are predominantly trained in high-resource languages, making them ill-equipped to handle the complexity and diversity of healthcare queries in mid- and low-resource languages, posing significant challenges for deploying them in global healthcare contexts where linguistic diversity is key. In this work, we present CLINIC, a Comprehensive Multilingual Benchmark to evaluate the trustworthiness of language models in healthcare. CLINIC systematically benchmarks LMs across five key dimensions of trustworthiness: truthfulness, fairness, safety, robustness, and privacy, operationalized through 18 diverse tasks, spanning 15 languages (covering all the major continents), and encompassing a wide array of critical healthcare topics like disease conditions, preventive actions, diagnostic tests, treatments, surgeries, and medications. Our extensive evaluation reveals that LMs struggle with factual correctness, demonstrate bias across demographic and linguistic groups, and are susceptible to privacy breaches and adversarial attacks. By highlighting these shortcomings, CLINIC lays the foundation for enhancing the global reach and safety of LMs in healthcare across diverse languages.", "date": "2025-12-12", "categories": []}
{"id": "2512.11502", "title": "Building Patient Journeys in Hebrew: A Language Model for Clinical Timeline Extraction", "abstract": "We present a new Hebrew medical language model designed to extract structured clinical timelines from electronic health records, enabling the construction of patient journeys. Our model is based on DictaBERT 2.0 and continually pre-trained on over five million de-identified hospital records. To evaluate its effectiveness, we introduce two new datasets -- one from internal medicine and emergency departments, and another from oncology -- annotated for event temporal relations. Our results show that our model achieves strong performance on both datasets. We also find that vocabulary adaptation improves token efficiency and that de-identification does not compromise downstream performance, supporting privacy-conscious model development. The model is made available for research use under ethical restrictions.", "date": "2025-12-12", "categories": []}
{"id": "2512.11544", "title": "AI-MASLD Metabolic Dysfunction and Information Steatosis of Large Language Models in Unstructured Clinical Narratives", "abstract": "This study aims to simulate real-world clinical scenarios to systematically evaluate the ability of Large Language Models (LLMs) to extract core medical information from patient chief complaints laden with noise and redundancy, and to verify whether they exhibit a functional decline analogous to Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD). We employed a cross-sectional analysis design based on standardized medical probes, selecting four mainstream LLMs as research subjects: GPT-4o, Gemini 2.5, DeepSeek 3.1, and Qwen3-Max. An evaluation system comprising twenty medical probes across five core dimensions was used to simulate a genuine clinical communication environment. All probes had gold-standard answers defined by clinical experts and were assessed via a double-blind, inverse rating scale by two independent clinicians. The results show that all tested models exhibited functional defects to varying degrees, with Qwen3-Max demonstrating the best overall performance and Gemini 2.5 the worst. Under conditions of extreme noise, most models experienced a functional collapse. Notably, GPT-4o made a severe misjudgment in the risk assessment for pulmonary embolism (PE) secondary to deep vein thrombosis (DVT). This research is the first to empirically confirm that LLMs exhibit features resembling metabolic dysfunction when processing clinical information, proposing the innovative concept of \"AI-Metabolic Dysfunction-Associated Steatotic Liver Disease (AI-MASLD)\". These findings offer a crucial safety warning for the application of Artificial Intelligence (AI) in healthcare, emphasizing that current LLMs must be used as auxiliary tools under human expert supervision, as there remains a significant gap between their theoretical knowledge and practical clinical application.", "date": "2025-12-12", "categories": []}
{"id": "2403.20145", "title": "Leveraging language models for summarizing mental state examinations: A comprehensive evaluation and dataset release", "abstract": "Mental health disorders affect a significant portion of the global population, with diagnoses primarily conducted through Mental State Examinations (MSEs). MSEs serve as structured assessments to evaluate behavioral and cognitive functioning across various domains, aiding mental health professionals in diagnosis and treatment monitoring. However, in developing countries, access to mental health support is limited, leading to an overwhelming demand for mental health professionals. Resident doctors often conduct initial patient assessments and create summaries for senior doctors, but their availability is constrained, resulting in extended patient wait times.\n  This study addresses the challenge of generating concise summaries from MSEs through the evaluation of various language models. Given the scarcity of relevant mental health conversation datasets, we developed a 12-item descriptive MSE questionnaire and collected responses from 405 participants, resulting in 9720 utterances covering diverse mental health aspects. Subsequently, we assessed the performance of five well-known pre-trained summarization models, both with and without fine-tuning, for summarizing MSEs. Our comprehensive evaluation, leveraging metrics such as ROUGE, SummaC, and human evaluation, demonstrates that language models can generate automated coherent MSE summaries for doctors. With this paper, we release our collected conversational dataset and trained models publicly for the mental health research community.", "date": "2024-03-29", "categories": []}
{"id": "2512.09127", "title": "Knowledge-Guided Large Language Model for Automatic Pediatric Dental Record Understanding and Safe Antibiotic Recommendation", "abstract": "Accurate interpretation of pediatric dental clinical records and safe antibiotic prescribing remain persistent challenges in dental informatics. Traditional rule-based clinical decision support systems struggle with unstructured dental narratives, incomplete radiographic descriptions, and complex safety constraints. To address these limitations, this study proposes a Knowledge-Guided Large Language Model (KG-LLM) that integrates a pediatric dental knowledge graph, retrieval-augmented generation (RAG), and a multi-stage safety validation pipeline for evidence-grounded antibiotic recommendation. The framework first employs a clinical NER/RE module to extract structured entities and relations from dental notes and radiology reports. Relevant guidelines, drug-safety rules, and analogous historical cases are subsequently retrieved from the knowledge graph and supplied to the LLM for diagnostic summarization and dose-drug-duration prediction. Safety assurance is achieved through a dual-layer validation mechanism combining deterministic rule checking with a learned classifier for detecting allergies, contraindications, and dosing errors. Experiments on 32,000 de-identified pediatric dental visit records demonstrate the effectiveness of the proposed approach. Compared with a domain-adapted Llama-2 clinical baseline, KG-LLM improves record-understanding performance (F1: 0.914 vs. 0.867), drug-dose-duration accuracy (Top-1: 0.782 vs. 0.716), and reduces unsafe antibiotic suggestions by 50%. Additional evaluation across summary quality, recommendation accuracy, and global safety scores further confirms the robustness of the system. Ablation analyses indicate that the knowledge graph, RAG, and safety modules each contribute substantially to clinical reliability and interpretability.", "date": "2025-12-09", "categories": []}
{"id": "2409.08936", "title": "SimSUM: Simulated Benchmark with Structured and Unstructured Medical Records", "abstract": "Clinical information extraction, which involves structuring clinical concepts from unstructured medical text, remains a challenging problem that could benefit from the inclusion of tabular background information available in electronic health records. Existing open-source datasets lack explicit links between structured features and clinical concepts in the text, motivating the need for a new research dataset. We introduce SimSUM, a benchmark dataset of 10,000 simulated patient records that link unstructured clinical notes with structured background variables. Each record simulates a patient encounter in the domain of respiratory diseases and includes tabular data (e.g., symptoms, diagnoses, underlying conditions) generated from a Bayesian network whose structure and parameters are defined by domain experts. A large language model (GPT-4o) is prompted to generate a clinical note describing the encounter, including symptoms and relevant context. These notes are annotated with span-level symptom mentions. We conduct an expert evaluation to assess note quality and run baseline predictive models on both the tabular and textual data. The SimSUM dataset is primarily designed to support research on clinical information extraction in the presence of tabular background variables, which can be linked through domain knowledge to concepts of interest to be extracted from the text -- namely, symptoms in the case of SimSUM. Secondary uses include research on the automation of clinical reasoning over both tabular data and text, causal effect estimation in the presence of tabular and/or textual confounders, and multi-modal synthetic data generation. SimSUM is not intended for training clinical decision support systems or production-grade models, but rather to facilitate reproducible research in a simplified and controlled setting.", "date": "2024-09-13", "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": "2512.08026", "title": "Toward an AI Reasoning-Enabled System for Patient-Clinical Trial Matching", "abstract": "Screening patients for clinical trial eligibility remains a manual, time-consuming, and resource-intensive process. We present a secure, scalable proof-of-concept system for Artificial Intelligence (AI)-augmented patient-trial matching that addresses key implementation challenges: integrating heterogeneous electronic health record (EHR) data, facilitating expert review, and maintaining rigorous security standards. Leveraging open-source, reasoning-enabled large language models (LLMs), the system moves beyond binary classification to generate structured eligibility assessments with interpretable reasoning chains that support human-in-the-loop review. This decision support tool represents eligibility as a dynamic state rather than a fixed determination, identifying matches when available and offering actionable recommendations that could render a patient eligible in the future. The system aims to reduce coordinator burden, intelligently broaden the set of trials considered for each patient and guarantee comprehensive auditability of all AI-generated outputs.", "date": "2025-12-08", "categories": []}
{"id": "2512.08617", "title": "HealthcareNLP: where are we and what is next?", "abstract": "This proposed tutorial focuses on Healthcare Domain Applications of NLP, what we have achieved around HealthcareNLP, and the challenges that lie ahead for the future. Existing reviews in this domain either overlook some important tasks, such as synthetic data generation for addressing privacy concerns, or explainable clinical NLP for improved integration and implementation, or fail to mention important methodologies, including retrieval augmented generation and the neural symbolic integration of LLMs and KGs. In light of this, the goal of this tutorial is to provide an introductory overview of the most important sub-areas of a patient- and resource-oriented HealthcareNLP, with three layers of hierarchy: data/resource layer: annotation guidelines, ethical approvals, governance, synthetic data; NLP-Eval layer: NLP tasks such as NER, RE, sentiment analysis, and linking/coding with categorised methods, leading to explainable HealthAI; patients layer: Patient Public Involvement and Engagement (PPIE), health literacy, translation, simplification, and summarisation (also NLP tasks), and shared decision-making support. A hands-on session will be included in the tutorial for the audience to use HealthcareNLP applications. The target audience includes NLP practitioners in the healthcare application domain, NLP researchers who are interested in domain applications, healthcare researchers, and students from NLP fields. The type of tutorial is \"Introductory to CL/NLP topics (HealthcareNLP)\" and the audience does not need prior knowledge to attend this. Tutorial materials: https://github.com/4dpicture/HealthNLP", "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": "2509.21933", "title": "Why Chain of Thought Fails in Clinical Text Understanding", "abstract": "Large language models (LLMs) are increasingly being applied to clinical care, a domain where both accuracy and transparent reasoning are critical for safe and trustworthy deployment. Chain-of-thought (CoT) prompting, which elicits step-by-step reasoning, has demonstrated improvements in performance and interpretability across a wide range of tasks. However, its effectiveness in clinical contexts remains largely unexplored, particularly in the context of electronic health records (EHRs), the primary source of clinical documentation, which are often lengthy, fragmented, and noisy. In this work, we present the first large-scale systematic study of CoT for clinical text understanding. We assess 95 advanced LLMs on 87 real-world clinical text tasks, covering 9 languages and 8 task types. Contrary to prior findings in other domains, we observe that 86.3\\% of models suffer consistent performance degradation in the CoT setting. More capable models remain relatively robust, while weaker ones suffer substantial declines. To better characterize these effects, we perform fine-grained analyses of reasoning length, medical concept alignment, and error profiles, leveraging both LLM-as-a-judge evaluation and clinical expert evaluation. Our results uncover systematic patterns in when and why CoT fails in clinical contexts, which highlight a critical paradox: CoT enhances interpretability but may undermine reliability in clinical text tasks. This work provides an empirical basis for clinical reasoning strategies of LLMs, highlighting the need for transparent and trustworthy approaches.", "date": "2025-09-26", "categories": []}
{"id": "2512.06161", "title": "Deep learning for autism detection using clinical notes: A comparison of transfer learning for a transparent and black-box approach", "abstract": "Autism spectrum disorder (ASD) is a complex neurodevelopmental condition whose rising prevalence places increasing demands on a lengthy diagnostic process. Machine learning (ML) has shown promise in automating ASD diagnosis, but most existing models operate as black boxes and are typically trained on a single dataset, limiting their generalizability. In this study, we introduce a transparent and interpretable ML approach that leverages BioBERT, a state-of-the-art language model, to analyze unstructured clinical text. The model is trained to label descriptions of behaviors and map them to diagnostic criteria, which are then used to assign a final label (ASD or not). We evaluate transfer learning, the ability to transfer knowledge to new data, using two distinct real-world datasets. We trained on datasets sequentially and mixed together and compared the performance of the best models and their ability to transfer to new data. We also created a black-box approach and repeated this transfer process for comparison. Our transparent model demonstrated robust performance, with the mixed-data training strategy yielding the best results (97 % sensitivity, 98 % specificity). Sequential training across datasets led to a slight drop in performance, highlighting the importance of training data order. The black-box model performed worse (90 % sensitivity, 96 % specificity) when trained sequentially or with mixed data. Overall, our transparent approach outperformed the black-box approach. Mixing datasets during training resulted in slightly better performance and should be the preferred approach when practically possible. This work paves the way for more trustworthy, generalizable, and clinically actionable AI tools in neurodevelopmental diagnostics.", "date": "2025-12-05", "categories": []}
{"id": "2510.21084", "title": "Chinese Discharge Drug Recommendation in Metabolic Diseases with Large Language Models", "abstract": "Intelligent drug recommendation based on Electronic Health Records (EHRs) is critical for improving the quality and efficiency of clinical decision-making. By leveraging large-scale patient data, drug recommendation systems can assist physicians in selecting the most appropriate medications according to a patient's medical history, diagnoses, laboratory results, and comorbidities. Recent advances in large language models (LLMs) have shown remarkable capabilities in complex reasoning and medical text understanding, making them promising tools for drug recommendation tasks. However, the application of LLMs for Chinese clinical medication recommendation remains largely unexplored. In this work, we conduct a systematic investigation of LLM-based methodologies for Chinese discharge medication recommendation. We evaluate several representative LLM families (GLM, Llama, Qwen) under a unified methodological framework including zero-shot prompting, in-context learning, chain-of-thought prompting, and supervised fine-tuning using LoRA. We analyze model behavior across reasoning styles, error patterns, domain adaptation mechanisms, and robustness. Experimental results show that while supervised fine-tuning improves model performance, there remains substantial room for improvement, with the best model achieving the F1 score of 0.5648 and Jaccard score of 0.4477. Our findings highlight both the potential and limitations of LLMs for Chinese drug recommendation.", "date": "2025-10-24", "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": "2512.05836", "title": "Using Large Language Models to Create Personalized Networks From Therapy Sessions", "abstract": "Recent advances in psychotherapy have focused on treatment personalization, such as by selecting treatment modules based on personalized networks. However, estimating personalized networks typically requires intensive longitudinal data, which is not always feasible. A solution to facilitate scalability of network-driven treatment personalization is leveraging LLMs. In this study, we present an end-to-end pipeline for automatically generating client networks from 77 therapy transcripts to support case conceptualization and treatment planning. We annotated 3364 psychological processes and their corresponding dimensions in therapy transcripts. Using these data, we applied in-context learning to jointly identify psychological processes and their dimensions. The method achieved high performance even with a few training examples. To organize the processes into networks, we introduced a two-step method that grouped them into clinically meaningful clusters. We then generated explanation-augmented relationships between clusters. Experts found that networks produced by our multi-step approach outperformed those built with direct prompting for clinical utility and interpretability, with up to 90% preferring our approach. In addition, the networks were rated favorably by experts, with scores for clinical relevance, novelty, and usefulness ranging from 72-75%. Our findings provide a proof of concept for using LLMs to create clinically relevant networks from therapy transcripts. Advantages of our approach include bottom-up case conceptualization from client utterances in therapy sessions and identification of latent themes. Networks generated from our pipeline may be used in clinical settings and supervision and training. Future research should examine whether these networks improve treatment outcomes relative to other methods of treatment personalization, including statistically estimated networks.", "date": "2025-12-05", "categories": []}
{"id": "2512.04518", "title": "UW-BioNLP at ChemoTimelines 2025: Thinking, Fine-Tuning, and Dictionary-Enhanced LLM Systems for Chemotherapy Timeline Extraction", "abstract": "The ChemoTimelines shared task benchmarks methods for constructing timelines of systemic anticancer treatment from electronic health records of cancer patients. This paper describes our methods, results, and findings for subtask 2 -- generating patient chemotherapy timelines from raw clinical notes. We evaluated strategies involving chain-of-thought thinking, supervised fine-tuning, direct preference optimization, and dictionary-based lookup to improve timeline extraction. All of our approaches followed a two-step workflow, wherein an LLM first extracted chemotherapy events from individual clinical notes, and then an algorithm normalized and aggregated events into patient-level timelines. Each specific method differed in how the associated LLM was utilized and trained. Multiple approaches yielded competitive performances on the test set leaderboard, with fine-tuned Qwen3-14B achieving the best official score of 0.678. Our results and analyses could provide useful insights for future attempts on this task as well as the design of similar tasks.", "date": "2025-12-04", "categories": []}
{"id": "2512.00601", "title": "Clinical-R1: Empowering Large Language Models for Faithful and Comprehensive Reasoning with Clinical Objective Relative Policy Optimization", "abstract": "Recent advances in large language models (LLMs) have shown strong reasoning capabilities through large-scale pretraining and post-training reinforcement learning, demonstrated by DeepSeek-R1. However, current post-training methods, such as Grouped Relative Policy Optimization (GRPO), mainly reward correctness, which is not aligned with the multi-dimensional objectives required in high-stakes fields such as medicine, where reasoning must also be faithful and comprehensive. We introduce Clinical-Objective Relative Policy Optimization (CRPO), a scalable, multi-objective, verifiable reinforcement learning method designed to align LLM post-training with clinical reasoning principles. CRPO integrates rule-based and verifiable reward signals that jointly optimize accuracy, faithfulness, and comprehensiveness without relying on human annotation. To demonstrate its effectiveness, we train Clinical-R1-3B, a 3B-parameter model for clinical reasoning. The experiments on three benchmarks demonstrate that our CRPO substantially improves reasoning on truthfulness and completeness over standard GRPO while maintaining comfortable accuracy enhancements. This framework provides a scalable pathway to align LLM reasoning with clinical objectives, enabling safer and more collaborative AI systems for healthcare while also highlighting the potential of multi-objective, verifiable RL methods in post-training scaling of LLMs for medical domains.", "date": "2025-11-29", "categories": []}
{"id": "2512.02024", "title": "Human-Level and Beyond: Benchmarking Large Language Models Against Clinical Pharmacists in Prescription Review", "abstract": "The rapid advancement of large language models (LLMs) has accelerated their integration into clinical decision support, particularly in prescription review. To enable systematic and fine-grained evaluation, we developed RxBench, a comprehensive benchmark that covers common prescription review categories and consolidates 14 frequent types of prescription errors drawn from authoritative pharmacy references. RxBench consists of 1,150 single-choice, 230 multiple-choice, and 879 short-answer items, all reviewed by experienced clinical pharmacists. We benchmarked 18 state-of-the-art LLMs and identified clear stratification of performance across tasks. Notably, Gemini-2.5-pro-preview-05-06, Grok-4-0709, and DeepSeek-R1-0528 consistently formed the first tier, outperforming other models in both accuracy and robustness. Comparisons with licensed pharmacists indicated that leading LLMs can match or exceed human performance in certain tasks. Furthermore, building on insights from our benchmark evaluation, we performed targeted fine-tuning on a mid-tier model, resulting in a specialized model that rivals leading general-purpose LLMs in performance on short-answer question tasks. The main contribution of RxBench lies in establishing a standardized, error-type-oriented framework that not only reveals the capabilities and limitations of frontier LLMs in prescription review but also provides a foundational resource for building more reliable and specialized clinical tools.", "date": "2025-11-17", "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": "2303.11032", "title": "DeID-GPT: Zero-shot Medical Text De-Identification by GPT-4", "abstract": "The digitization of healthcare has facilitated the sharing and re-using of medical data but has also raised concerns about confidentiality and privacy. HIPAA (Health Insurance Portability and Accountability Act) mandates removing re-identifying information before the dissemination of medical records. Thus, effective and efficient solutions for de-identifying medical data, especially those in free-text forms, are highly needed. While various computer-assisted de-identification methods, including both rule-based and learning-based, have been developed and used in prior practice, such solutions still lack generalizability or need to be fine-tuned according to different scenarios, significantly imposing restrictions in wider use. The advancement of large language models (LLM), such as ChatGPT and GPT-4, have shown great potential in processing text data in the medical domain with zero-shot in-context learning, especially in the task of privacy protection, as these models can identify confidential information by their powerful named entity recognition (NER) capability. In this work, we developed a novel GPT4-enabled de-identification framework (``DeID-GPT\") to automatically identify and remove the identifying information. Compared to existing commonly used medical text data de-identification methods, our developed DeID-GPT showed the highest accuracy and remarkable reliability in masking private information from the unstructured medical text while preserving the original structure and meaning of the text. This study is one of the earliest to utilize ChatGPT and GPT-4 for medical text data processing and de-identification, which provides insights for further research and solution development on the use of LLMs such as ChatGPT/GPT-4 in healthcare. Codes and benchmarking data information are available at https://github.com/yhydhx/ChatGPT-API.", "date": "2023-03-20", "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": "2512.00586", "title": "Statistical NLP for Optimization of Clinical Trial Success Prediction in Pharmaceutical R&D", "abstract": "This work presents the development and evaluation of an NLP-enabled probabilistic classifier designed to estimate the probability of technical and regulatory success (pTRS) for clinical trials in the field of neuroscience. While pharmaceutical R&D is plagued by high attrition rates and enormous costs, particularly within neuroscience, where success rates are below 10%, timely identification of promising programs can streamline resource allocation and reduce financial risk. Leveraging data from the ClinicalTrials.gov database and success labels from the recently developed Clinical Trial Outcome dataset, the classifier extracts text-based clinical trial features using statistical NLP techniques. These features were integrated into several non-LLM frameworks (logistic regression, gradient boosting, and random forest) to generate calibrated probability scores. Model performance was assessed on a retrospective dataset of 101,145 completed clinical trials spanning 1976-2024, achieving an overall ROC-AUC of 0.64. An LLM-based predictive model was then built using BioBERT, a domain-specific language representation encoder. The BioBERT-based model achieved an overall ROC-AUC of 0.74 and a Brier Score of 0.185, indicating its predictions had, on average, 40% less squared error than would be observed using industry benchmarks. The BioBERT-based model also made trial outcome predictions that were superior to benchmark values 70% of the time overall. By integrating NLP-driven insights into drug development decision-making, this work aims to enhance strategic planning and optimize investment allocation in neuroscience programs.", "date": "2025-11-29", "categories": []}
{"id": "2512.00768", "title": "Text Mining Analysis of Symptom Patterns in Medical Chatbot Conversations", "abstract": "The fast growth of digital health systems has led to a need to better comprehend how they interpret and represent patient-reported symptoms. Chatbots have been used in healthcare to provide clinical support and enhance the user experience, making it possible to provide meaningful clinical patterns from text-based data through chatbots. The proposed research utilises several different natural language processing methods to study the occurrences of symptom descriptions in medicine as well as analyse the patterns that emerge through these conversations within medical bots. Through the use of the Medical Conversations to Disease Dataset which contains 960 multi-turn dialogues divided into 24 Clinical Conditions, a standardised representation of conversations between patient and bot is created for further analysis by computational means. The multi-method approach uses a variety of tools, including Latent Dirichlet Allocation (LDA) to identify latent symptom themes, K-Means to group symptom descriptions by similarity, Transformer-based Named Entity Recognition (NER) to extract medical concepts, and the Apriori algorithm to discover frequent symptom pairs. Findings from the analysis indicate a coherent structure of clinically relevant topics, moderate levels of clustering cohesiveness and several high confidence rates on the relationships between symptoms like fever headache and rash itchiness. The results support the notion that conversational medical data can be a valuable diagnostic signal for early symptom interpretation, assist in strengthening decision support and improve how users interact with tele-health technology. By demonstrating a method for converting unstructured free-flowing dialogue into actionable knowledge regarding symptoms this work provides an extensible framework to further enhance future performance, dependability and clinical utility of selecting medical chatbots.", "date": "2025-11-30", "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": "2511.22818", "title": "Mitigating Semantic Drift: Evaluating LLMs' Efficacy in Psychotherapy through MI Dialogue Summarization", "abstract": "Recent advancements in large language models (LLMs) have shown their potential across both general and domain-specific tasks. However, there is a growing concern regarding their lack of sensitivity, factual incorrectness in responses, inconsistent expressions of empathy, bias, hallucinations, and overall inability to capture the depth and complexity of human understanding, especially in low-resource and sensitive domains such as psychology. To address these challenges, our study employs a mixed-methods approach to evaluate the efficacy of LLMs in psychotherapy. We use LLMs to generate precise summaries of motivational interviewing (MI) dialogues and design a two-stage annotation scheme based on key components of the Motivational Interviewing Treatment Integrity (MITI) framework, namely evocation, collaboration, autonomy, direction, empathy, and a non-judgmental attitude. Using expert-annotated MI dialogues as ground truth, we formulate multi-class classification tasks to assess model performance under progressive prompting techniques, incorporating one-shot and few-shot prompting. Our results offer insights into LLMs' capacity for understanding complex psychological constructs and highlight best practices to mitigate ``semantic drift\" in therapeutic settings. Our work contributes not only to the MI community by providing a high-quality annotated dataset to address data scarcity in low-resource domains but also critical insights for using LLMs for precise contextual interpretation in complex behavioral therapy.", "date": "2025-11-28", "categories": []}
{"id": "2307.13693", "title": "Evaluating Large Language Models for Radiology Natural Language Processing", "abstract": "The rise of large language models (LLMs) has marked a pivotal shift in the field of natural language processing (NLP). LLMs have revolutionized a multitude of domains, and they have made a significant impact in the medical field. Large language models are now more abundant than ever, and many of these models exhibit bilingual capabilities, proficient in both English and Chinese. However, a comprehensive evaluation of these models remains to be conducted. This lack of assessment is especially apparent within the context of radiology NLP. This study seeks to bridge this gap by critically evaluating thirty two LLMs in interpreting radiology reports, a crucial component of radiology NLP. Specifically, the ability to derive impressions from radiologic findings is assessed. The outcomes of this evaluation provide key insights into the performance, strengths, and weaknesses of these LLMs, informing their practical applications within the medical domain.", "date": "2023-07-25", "categories": []}
{"id": "2511.19858", "title": "A Systematic Analysis of Large Language Models with RAG-enabled Dynamic Prompting for Medical Error Detection and Correction", "abstract": "Objective: Clinical documentation contains factual, diagnostic, and management errors that can compromise patient safety. Large language models (LLMs) may help detect and correct such errors, but their behavior under different prompting strategies remains unclear. We evaluate zero-shot prompting, static prompting with random exemplars (SPR), and retrieval-augmented dynamic prompting (RDP) for three subtasks of medical error processing: error flag detection, error sentence detection, and error correction.\n  Methods: Using the MEDEC dataset, we evaluated nine instruction-tuned LLMs (GPT, Claude, Gemini, and OpenAI o-series models). We measured performance using accuracy, recall, false-positive rate (FPR), and an aggregate score of ROUGE-1, BLEURT, and BERTScore for error correction. We also analyzed example outputs to identify failure modes and differences between LLM and clinician reasoning.\n  Results: Zero-shot prompting showed low recall in both detection tasks, often missing abbreviation-heavy or atypical errors. SPR improved recall but increased FPR. Across all nine LLMs, RDP reduced FPR by about 15 percent, improved recall by 5 to 10 percent in error sentence detection, and generated more contextually accurate corrections.\n  Conclusion: Across diverse LLMs, RDP outperforms zero-shot and SPR prompting. Using retrieved exemplars improves detection accuracy, reduces false positives, and enhances the reliability of medical error correction.", "date": "2025-11-25", "categories": []}
{"id": "2511.20680", "title": "Cognitive bias in LLM reasoning compromises interpretation of clinical oncology notes", "abstract": "Despite high performance on clinical benchmarks, large language models may reach correct conclusions through faulty reasoning, a failure mode with safety implications for oncology decision support that is not captured by accuracy-based evaluation. In this two-cohort retrospective study, we developed a hierarchical taxonomy of reasoning errors from GPT-4 chain-of-thought responses to real oncology notes and tested its clinical relevance. Using breast and pancreatic cancer notes from the CORAL dataset, we annotated 600 reasoning traces to define a three-tier taxonomy mapping computational failures to cognitive bias frameworks. We validated the taxonomy on 822 responses from prostate cancer consult notes spanning localized through metastatic disease, simulating extraction, analysis, and clinical recommendation tasks. Reasoning errors occurred in 23 percent of interpretations and dominated overall errors, with confirmation bias and anchoring bias most common. Reasoning failures were associated with guideline-discordant and potentially harmful recommendations, particularly in advanced disease management. Automated evaluators using state-of-the-art language models detected error presence but could not reliably classify subtypes. These findings show that large language models may provide fluent but clinically unsafe recommendations when reasoning is flawed. The taxonomy provides a generalizable framework for evaluating and improving reasoning fidelity before clinical deployment.", "date": "2025-11-16", "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": "2511.19739", "title": "Comparative Analysis of LoRA-Adapted Embedding Models for Clinical Cardiology Text Representation", "abstract": "Domain-specific text embeddings are critical for clinical natural language processing, yet systematic comparisons across model architectures remain limited. This study evaluates ten transformer-based embedding models adapted for cardiology through Low-Rank Adaptation (LoRA) fine-tuning on 106,535 cardiology text pairs derived from authoritative medical textbooks. Results demonstrate that encoder-only architectures, particularly BioLinkBERT, achieve superior domain-specific performance (separation score: 0.510) compared to larger decoder-based models, while requiring significantly fewer computational resources. The findings challenge the assumption that larger language models necessarily produce better domain-specific embeddings and provide practical guidance for clinical NLP system development. All models, training code, and evaluation datasets are publicly available to support reproducible research in medical informatics.", "date": "2025-11-24", "categories": []}
{"id": "2503.05060", "title": "ModernBERT is More Efficient than Conventional BERT for Chest CT Findings Classification in Japanese Radiology Reports", "abstract": "Japanese language models for medical text classification face challenges with complex vocabulary and linguistic structures in radiology reports. This study compared three Japanese models--BERT Base, JMedRoBERTa, and ModernBERT--for multi-label classification of 18 chest CT findings. Using the CT-RATE-JPN dataset, all models were fine-tuned under identical conditions. ModernBERT showed clear efficiency advantages, producing substantially fewer tokens and achieving faster training and inference than the other models while maintaining comparable performance on the internal test dataset (exact match accuracy: 74.7% vs. 72.7% for BERT Base). To assess generalizability, we additionally constructed RR-Findings, an external dataset of 243 naturally written Japanese radiology reports annotated using the same schema. Under this domain-shifted setting, performance differences became pronounced: BERT Base outperformed both JMedRoBERTa and ModernBERT, whereas ModernBERT showed the largest decline in exact match accuracy. Average precision differences were smaller, indicating that ModernBERT retained reasonable ranking ability despite reduced calibration. Overall, ModernBERT offers substantial computational efficiency and strong in-domain performance but remains sensitive to real-world linguistic variability. These results highlight the need for more diverse natural-language training data and domain-specific calibration strategies to improve robustness when deploying modern transformer models in heterogeneous clinical environments.", "date": "2025-03-07", "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": "2511.17947", "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": "2511.18393", "title": "Towards Robust and Fair Next Visit Diagnosis Prediction under Noisy Clinical Notes with Large Language Models", "abstract": "A decade of rapid advances in artificial intelligence (AI) has opened new opportunities for clinical decision support systems (CDSS), with large language models (LLMs) demonstrating strong reasoning abilities on timely medical tasks. However, clinical texts are often degraded by human errors or failures in automated pipelines, raising concerns about the reliability and fairness of AI-assisted decision-making. Yet the impact of such degradations remains under-investigated, particularly regarding how noise-induced shifts can heighten predictive uncertainty and unevenly affect demographic subgroups. We present a systematic study of state-of-the-art LLMs under diverse text corruption scenarios, focusing on robustness and equity in next-visit diagnosis prediction. To address the challenge posed by the large diagnostic label space, we introduce a clinically grounded label-reduction scheme and a hierarchical chain-of-thought (CoT) strategy that emulates clinicians' reasoning. Our approach improves robustness and reduces subgroup instability under degraded inputs, advancing the reliable use of LLMs in CDSS. We release code at https://github.com/heejkoo9/NECHOv3.", "date": "2025-11-23", "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": "2511.15778", "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": "2511.16445", "title": "PersonaDrift: A Benchmark for Temporal Anomaly Detection in Language-Based Dementia Monitoring", "abstract": "People living with dementia (PLwD) often show gradual shifts in how they communicate, becoming less expressive, more repetitive, or drifting off-topic in subtle ways. While caregivers may notice these changes informally, most computational tools are not designed to track such behavioral drift over time. This paper introduces PersonaDrift, a synthetic benchmark designed to evaluate machine learning and statistical methods for detecting progressive changes in daily communication, focusing on user responses to a digital reminder system. PersonaDrift simulates 60-day interaction logs for synthetic users modeled after real PLwD, based on interviews with caregivers. These caregiver-informed personas vary in tone, modality, and communication habits, enabling realistic diversity in behavior. The benchmark focuses on two forms of longitudinal change that caregivers highlighted as particularly salient: flattened sentiment (reduced emotional tone and verbosity) and off-topic replies (semantic drift). These changes are injected progressively at different rates to emulate naturalistic cognitive trajectories, and the framework is designed to be extensible to additional behaviors in future use cases. To explore this novel application space, we evaluate several anomaly detection approaches, unsupervised statistical methods (CUSUM, EWMA, One-Class SVM), sequence models using contextual embeddings (GRU + BERT), and supervised classifiers in both generalized and personalized settings. Preliminary results show that flattened sentiment can often be detected with simple statistical models in users with low baseline variability, while detecting semantic drift requires temporal modeling and personalized baselines. Across both tasks, personalized classifiers consistently outperform generalized ones, highlighting the importance of individual behavioral context.", "date": "2025-11-20", "categories": []}
{"id": "2505.00827", "title": "MIMIC-\\RNum{4}-Ext-22MCTS: A 22 Millions-Event Temporal Clinical Time-Series Dataset with Relative Timestamp for Risk Prediction", "abstract": "A crucial component for clinical risk prediction is developing a reliable prediction model is collecting high-quality time series clinical events. In this work, we release such a dataset that consists of 22,588,586 Clinical Time Series events, which we term MIMIC-\\RNum{4}-Ext-22MCTS. Our source data are discharge summaries selected from the well-known yet unstructured MIMIC-IV-Note \\cite{Johnson2023-pg}. The general-purpose MIMIC-IV-Note pose specific challenges for our work: it turns out that the discharge summaries are too lengthy for typical natural language models to process, and the clinical events of interest often are not accompanied with explicit timestamps. Therefore, we propose a new framework that works as follows: 1) we break each discharge summary into manageably small text chunks; 2) we apply contextual BM25 and contextual semantic search to retrieve chunks that have a high potential of containing clinical events; and 3) we carefully design prompts to teach the recently released Llama-3.1-8B \\cite{touvron2023llama} model to identify or infer temporal information of the chunks. The obtained dataset is informative and transparent that standard models fine-tuned on the dataset achieves significant improvements in healthcare applications. In particular, the BERT model fine-tuned based on our dataset achieves 10\\% improvement in accuracy on medical question answering task, and 3\\% improvement in clinical trial matching task compared with the classic BERT. The dataset is available at https://physionet.org/content/mimic-iv-ext-22mcts/1.0.0. The codebase is released at https://github.com/JingWang-RU/MIMIC-IV-Ext-22MCTS-Temporal-Clinical-Time-Series-Dataset.", "date": "2025-05-01", "categories": []}
{"id": "2510.16194", "title": "Towards Automatic Evaluation and Selection of PHI De-identification Models via Multi-Agent Collaboration", "abstract": "Protected health information (PHI) de-identification is critical for enabling the safe reuse of clinical notes, yet evaluating and comparing PHI de-identification models typically depends on costly, small-scale expert annotations. We present TEAM-PHI, a multi-agent evaluation and selection framework that uses large language models (LLMs) to automatically measure de-identification quality and select the best-performing model without heavy reliance on gold labels. TEAM-PHI deploys multiple Evaluation Agents, each independently judging the correctness of PHI extractions and outputting structured metrics. Their results are then consolidated through an LLM-based majority voting mechanism that integrates diverse evaluator perspectives into a single, stable, and reproducible ranking. Experiments on a real-world clinical note corpus demonstrate that TEAM-PHI produces consistent and accurate rankings: despite variation across individual evaluators, LLM-based voting reliably converges on the same top-performing systems. Further comparison with ground-truth annotations and human evaluation confirms that the framework's automated rankings closely match supervised evaluation. By combining independent evaluation agents with LLM majority voting, TEAM-PHI offers a practical, secure, and cost-effective solution for automatic evaluation and best-model selection in PHI de-identification, even when ground-truth labels are limited.", "date": "2025-10-17", "categories": []}
{"id": "2511.14112", "title": "Synthetic Clinical Notes for Rare ICD Codes: A Data-Centric Framework for Long-Tail Medical Coding", "abstract": "Automatic ICD coding from clinical text is a critical task in medical NLP but remains hindered by the extreme long-tail distribution of diagnostic codes. Thousands of rare and zero-shot ICD codes are severely underrepresented in datasets like MIMIC-III, leading to low macro-F1 scores. In this work, we propose a data-centric framework that generates high-quality synthetic discharge summaries to mitigate this imbalance. Our method constructs realistic multi-label code sets anchored on rare codes by leveraging real-world co-occurrence patterns, ICD descriptions, synonyms, taxonomy, and similar clinical notes. Using these structured prompts, we generate 90,000 synthetic notes covering 7,902 ICD codes, significantly expanding the training distribution. We fine-tune two state-of-the-art transformer-based models, PLM-ICD and GKI-ICD, on both the original and extended datasets. Experiments show that our approach modestly improves macro-F1 while maintaining strong micro-F1, outperforming prior SOTA. While the gain may seem marginal relative to the computational cost, our results demonstrate that carefully crafted synthetic data can enhance equity in long-tail ICD code prediction.", "date": "2025-11-18", "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": "2511.12851", "title": "NeuroLex: A Lightweight Domain Language Model for EEG Report Understanding and Generation", "abstract": "Clinical electroencephalogram (EEG) reports encode domain-specific linguistic conventions that general-purpose language models (LMs) fail to capture. We introduce NeuroLex, a lightweight domain-adaptive language model trained purely on EEG report text from the Harvard Electroencephalography Database. Unlike existing biomedical LMs, NeuroLex is tailored to the linguistic and diagnostic characteristics of EEG reporting, enabling it to serve as both an independent textual model and a decoder backbone for multimodal EEG-language systems. Using span-corruption pretraining and instruction-style fine-tuning on report polishing, paragraph summarization, and terminology question answering, NeuroLex learns the syntax and reasoning patterns characteristic of EEG interpretation. Comprehensive evaluations show that it achieves lower perplexity, higher extraction and summarization accuracy, better label efficiency, and improved robustness to negation and factual hallucination compared with general models of the same scale. With an EEG-aware linguistic backbone, NeuroLex bridges biomedical text modeling and brain-computer interface applications, offering a foundation for interpretable and language-driven neural decoding.", "date": "2025-11-17", "categories": []}
{"id": "2511.13361", "title": "MedDCR: Learning to Design Agentic Workflows for Medical Coding", "abstract": "Medical coding converts free-text clinical notes into standardized diagnostic and procedural codes, which are essential for billing, hospital operations, and medical research. Unlike ordinary text classification, it requires multi-step reasoning: extracting diagnostic concepts, applying guideline constraints, mapping to hierarchical codebooks, and ensuring cross-document consistency. Recent advances leverage agentic LLMs, but most rely on rigid, manually crafted workflows that fail to capture the nuance and variability of real-world documentation, leaving open the question of how to systematically learn effective workflows. We present MedDCR, a closed-loop framework that treats workflow design as a learning problem. A Designer proposes workflows, a Coder executes them, and a Reflector evaluates predictions and provides constructive feedback, while a memory archive preserves prior designs for reuse and iterative refinement. On benchmark datasets, MedDCR outperforms state-of-the-art baselines and produces interpretable, adaptable workflows that better reflect real coding practice, improving both the reliability and trustworthiness of automated systems.", "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": "2405.08151", "title": "Benchmarking Retrieval-Augmented Large Language Models in Biomedical NLP: Application, Robustness, and Self-Awareness", "abstract": "Large language models (LLM) have demonstrated remarkable capabilities in various biomedical natural language processing (NLP) tasks, leveraging the demonstration within the input context to adapt to new tasks. However, LLM is sensitive to the selection of demonstrations. To address the hallucination issue inherent in LLM, retrieval-augmented LLM (RAL) offers a solution by retrieving pertinent information from an established database. Nonetheless, existing research work lacks rigorous evaluation of the impact of retrieval-augmented large language models on different biomedical NLP tasks. This deficiency makes it challenging to ascertain the capabilities of RAL within the biomedical domain. Moreover, the outputs from RAL are affected by retrieving the unlabeled, counterfactual, or diverse knowledge that is not well studied in the biomedical domain. However, such knowledge is common in the real world. Finally, exploring the self-awareness ability is also crucial for the RAL system. So, in this paper, we systematically investigate the impact of RALs on 5 different biomedical tasks (triple extraction, link prediction, classification, question answering, and natural language inference). We analyze the performance of RALs in four fundamental abilities, including unlabeled robustness, counterfactual robustness, diverse robustness, and negative awareness. To this end, we proposed an evaluation framework to assess the RALs' performance on different biomedical NLP tasks and establish four different testbeds based on the aforementioned fundamental abilities. Then, we evaluate 3 representative LLMs with 3 different retrievers on 5 tasks over 9 datasets.", "date": "2024-05-13", "categories": []}
{"id": "2511.10658", "title": "Evaluating Open-Weight Large Language Models for Structured Data Extraction from Narrative Medical Reports Across Multiple Use Cases and Languages", "abstract": "Large language models (LLMs) are increasingly used to extract structured information from free-text clinical records, but prior work often focuses on single tasks, limited models, and English-language reports. We evaluated 15 open-weight LLMs on pathology and radiology reports across six use cases, colorectal liver metastases, liver tumours, neurodegenerative diseases, soft-tissue tumours, melanomas, and sarcomas, at three institutes in the Netherlands, UK, and Czech Republic. Models included general-purpose and medical-specialised LLMs of various sizes, and six prompting strategies were compared: zero-shot, one-shot, few-shot, chain-of-thought, self-consistency, and prompt graph. Performance was assessed using task-appropriate metrics, with consensus rank aggregation and linear mixed-effects models quantifying variance. Top-ranked models achieved macro-average scores close to inter-rater agreement across tasks. Small-to-medium general-purpose models performed comparably to large models, while tiny and specialised models performed worse. Prompt graph and few-shot prompting improved performance by ~13%. Task-specific factors, including variable complexity and annotation variability, influenced results more than model size or prompting strategy. These findings show that open-weight LLMs can extract structured data from clinical reports across diseases, languages, and institutions, offering a scalable approach for clinical data curation.", "date": "2025-11-03", "categories": []}
{"id": "2511.10912", "title": "Evaluating Large Language Models on Rare Disease Diagnosis: A Case Study using House M.D", "abstract": "Large language models (LLMs) have demonstrated capabilities across diverse domains, yet their performance on rare disease diagnosis from narrative medical cases remains underexplored. We introduce a novel dataset of 176 symptom-diagnosis pairs extracted from House M.D., a medical television series validated for teaching rare disease recognition in medical education. We evaluate four state-of-the-art LLMs such as GPT 4o mini, GPT 5 mini, Gemini 2.5 Flash, and Gemini 2.5 Pro on narrative-based diagnostic reasoning tasks. Results show significant variation in performance, ranging from 16.48% to 38.64% accuracy, with newer model generations demonstrating a 2.3 times improvement. While all models face substantial challenges with rare disease diagnosis, the observed improvement across architectures suggests promising directions for future development. Our educationally validated benchmark establishes baseline performance metrics for narrative medical reasoning and provides a publicly accessible evaluation framework for advancing AI-assisted diagnosis research.", "date": "2025-11-14", "categories": []}
{"id": "2511.10930", "title": "CardioEmbed: Domain-Specialized Text Embeddings for Clinical Cardiology", "abstract": "Biomedical text embeddings have primarily been developed using research literature from PubMed, yet clinical cardiology practice relies heavily on procedural knowledge and specialized terminology found in comprehensive textbooks rather than research abstracts. This research practice gap limits the effectiveness of existing embedding models for clinical applications incardiology. This study trained CardioEmbed, a domain-specialized embedding model based on Qwen3-Embedding-8B, using contrastive learning on a curated corpus of seven comprehensive cardiology textbooks totaling approximately 150,000 sentences after deduplication. The model employs InfoNCE loss with in-batch negatives and achieves 99.60% retrieval accuracy on cardiac-specific semantic retrieval tasks, a +15.94 percentage point improvement over MedTE, the current state-of-the-art medical embedding model. On MTEB medical benchmarks, the model obtained BIOSSES 0.77 Spearman and SciFact 0.61 NDCG@10, indicating competitive performance on related biomedical domains. Domain-specialized training on comprehensive clinical textbooks yields near-perfect cardiology retrieval (99.60% Acc@1), improving over MedTE by +15.94 percentage points.", "date": "2025-11-14", "categories": []}
{"id": "2406.15045", "title": "Error Correction in Radiology Reports: A Knowledge Distillation-Based Multi-Stage Framework", "abstract": "The increasing complexity and workload of clinical radiology leads to inevitable oversights and mistakes in their use as diagnostic tools, causing delayed treatments and sometimes life-threatening harm to patients. While large language models (LLMs) have shown remarkable progress in many tasks, their utilities in detecting and correcting errors in radiology reporting are limited. This paper proposes a novel dual-knowledge infusion framework that enhances LLMs' capability for radiology report proofreading through systematic integration of medical expertise. Specifically, the knowledge infusion combines medical knowledge graph distillation (MKGD) with external knowledge retrieval (EXKR), enabling an effective automated approach in tackling mistakes in radiology reporting. By decomposing the complex proofreading task into three specialized stages of detection, localization, and correction, our method mirrors the systematic review process employed by expert radiologists, ensuring both precision and clinical interpretability. To perform a robust, clinically relevant evaluation, a comprehensive benchmark is also proposed using real-world radiology reports with real-world error patterns, including speech recognition confusions, terminology ambiguities, and template-related inconsistencies. Extensive evaluations across multiple LLM architectures demonstrate substantial improvements of our approach: up to 31.56% increase in error detection accuracy and 37.4% reduction in processing time. Human evaluation by radiologists confirms superior clinical relevance and factual consistency compared to existing approaches.", "date": "2024-06-21", "categories": []}
{"id": "2508.10027", "title": "LLMCARE: early detection of cognitive impairment via transformer models enhanced by LLM-generated synthetic data", "abstract": "Alzheimer's disease and related dementias(ADRD) affect nearly five million older adults in the United States, yet more than half remain undiagnosed. Speech-based natural language processing(NLP) offers a scalable approach for detecting early cognitive decline through subtle linguistic markers that may precede clinical diagnosis. This study develops and evaluates a speech-based screening pipeline integrating transformer embeddings with handcrafted linguistic features, synthetic augmentation using large language models(LLMs), and benchmarking of unimodal and multimodal classifiers. External validation assessed generalizability to a MCI-only cohort.\n  Transcripts were drawn from the ADReSSo 2021 benchmark dataset(n=237, Pitt Corpus) and the DementiaBank Delaware corpus(n=205, MCI vs. controls). Ten transformer models were tested under three fine-tuning strategies. A late-fusion model combined embeddings from the top transformer with 110 linguistic features. Five LLMs(LLaMA8B/70B, MedAlpaca7B, Ministral8B,GPT-4o) generated label-conditioned synthetic speech for augmentation, and three multimodal LLMs(GPT-4o,Qwen-Omni,Phi-4) were evaluated in zero-shot and fine-tuned modes. On ADReSSo, the fusion model achieved F1=83.3(AUC=89.5), outperforming transformer-only and linguistic baselines. MedAlpaca7B augmentation(2x) improved F1=85.7, though larger scales reduced gains. Fine-tuning boosted unimodal LLMs(MedAlpaca7B F1=47.7=>78.7), while multimodal models performed lower (Phi-4=71.6;GPT-4o=67.6). On Delaware, the fusion plus 1x MedAlpaca7B model achieved F1=72.8(AUC=69.6). Integrating transformer and linguistic features enhances ADRD detection. LLM-based augmentation improves data efficiency but yields diminishing returns, while current multimodal models remain limited. Validation on an independent MCI cohort supports the pipeline's potential for scalable, clinically relevant early screening.", "date": "2025-08-08", "categories": []}
{"id": "2509.19319", "title": "FHIR-AgentBench: Benchmarking LLM Agents for Realistic Interoperable EHR Question Answering", "abstract": "The recent shift toward the Health Level Seven Fast Healthcare Interoperability Resources (HL7 FHIR) standard opens a new frontier for clinical AI, demanding LLM agents to navigate complex, resource-based data models instead of conventional structured health data. However, existing benchmarks have lagged behind this transition, lacking the realism needed to evaluate recent LLMs on interoperable clinical data. To bridge this gap, we introduce FHIR-AgentBench, a benchmark that grounds 2,931 real-world clinical questions in the HL7 FHIR standard. Using this benchmark, we systematically evaluate agentic frameworks, comparing different data retrieval strategies (direct FHIR API calls vs. specialized tools), interaction patterns (single-turn vs. multi-turn), and reasoning strategies (natural language vs. code generation). Our experiments highlight the practical challenges of retrieving data from intricate FHIR resources and the difficulty of reasoning over them, both of which critically affect question answering performance. We publicly release the FHIR-AgentBench dataset and evaluation suite (https://github.com/glee4810/FHIR-AgentBench) to promote reproducible research and the development of robust, reliable LLM agents for clinical applications.", "date": "2025-09-12", "categories": []}
{"id": "2511.05901", "title": "Retrieval-Augmented Generation in Medicine: A Scoping Review of Technical Implementations, Clinical Applications, and Ethical Considerations", "abstract": "The rapid growth of medical knowledge and increasing complexity of clinical practice pose challenges. In this context, large language models (LLMs) have demonstrated value; however, inherent limitations remain. Retrieval-augmented generation (RAG) technologies show potential to enhance their clinical applicability. This study reviewed RAG applications in medicine. We found that research primarily relied on publicly available data, with limited application in private data. For retrieval, approaches commonly relied on English-centric embedding models, while LLMs were mostly generic, with limited use of medical-specific LLMs. For evaluation, automated metrics evaluated generation quality and task performance, whereas human evaluation focused on accuracy, completeness, relevance, and fluency, with insufficient attention to bias and safety. RAG applications were concentrated on question answering, report generation, text summarization, and information extraction. Overall, medical RAG remains at an early stage, requiring advances in clinical validation, cross-linguistic adaptation, and support for low-resource settings to enable trustworthy and responsible global use.", "date": "2025-11-08", "categories": []}
{"id": "2511.10583", "title": "Evaluating Prompting Strategies with MedGemma for Medical Order Extraction", "abstract": "The accurate extraction of medical orders from doctor-patient conversations is a critical task for reducing clinical documentation burdens and ensuring patient safety. This paper details our team submission to the MEDIQA-OE-2025 Shared Task. We investigate the performance of MedGemma, a new domain-specific open-source language model, for structured order extraction. We systematically evaluate three distinct prompting paradigms: a straightforward one-Shot approach, a reasoning-focused ReAct framework, and a multi-step agentic workflow. Our experiments reveal that while more complex frameworks like ReAct and agentic flows are powerful, the simpler one-shot prompting method achieved the highest performance on the official validation set. We posit that on manually annotated transcripts, complex reasoning chains can lead to \"overthinking\" and introduce noise, making a direct approach more robust and efficient. Our work provides valuable insights into selecting appropriate prompting strategies for clinical information extraction in varied data conditions.", "date": "2025-11-13", "categories": []}
{"id": "2410.21348", "title": "Large Language Model Benchmarks in Medical Tasks", "abstract": "With the increasing application of large language models (LLMs) in the medical domain, evaluating these models' performance using benchmark datasets has become crucial. This paper presents a comprehensive survey of various benchmark datasets employed in medical LLM tasks. These datasets span multiple modalities including text, image, and multimodal benchmarks, focusing on different aspects of medical knowledge such as electronic health records (EHRs), doctor-patient dialogues, medical question-answering, and medical image captioning. The survey categorizes the datasets by modality, discussing their significance, data structure, and impact on the development of LLMs for clinical tasks such as diagnosis, report generation, and predictive decision support. Key benchmarks include MIMIC-III, MIMIC-IV, BioASQ, PubMedQA, and CheXpert, which have facilitated advancements in tasks like medical report generation, clinical summarization, and synthetic data generation. The paper summarizes the challenges and opportunities in leveraging these benchmarks for advancing multimodal medical intelligence, emphasizing the need for datasets with a greater degree of language diversity, structured omics data, and innovative approaches to synthesis. This work also provides a foundation for future research in the application of LLMs in medicine, contributing to the evolving field of medical artificial intelligence.", "date": "2024-10-28", "categories": []}
{"id": "2508.01159", "title": "Asking the Right Questions: Benchmarking Large Language Models in the Development of Clinical Consultation Templates", "abstract": "This study evaluates the capacity of large language models (LLMs) to generate structured clinical consultation templates for electronic consultation. Using 145 expert-crafted templates developed and routinely used by Stanford's eConsult team, we assess frontier models -- including o3, GPT-4o, Kimi K2, Claude 4 Sonnet, Llama 3 70B, and Gemini 2.5 Pro -- for their ability to produce clinically coherent, concise, and prioritized clinical question schemas. Through a multi-agent pipeline combining prompt optimization, semantic autograding, and prioritization analysis, we show that while models like o3 achieve high comprehensiveness (up to 92.2\\%), they consistently generate excessively long templates and fail to correctly prioritize the most clinically important questions under length constraints. Performance varies across specialties, with significant degradation in narrative-driven fields such as psychiatry and pain medicine. Our findings demonstrate that LLMs can enhance structured clinical information exchange between physicians, while highlighting the need for more robust evaluation methods that capture a model's ability to prioritize clinically salient information within the time constraints of real-world physician communication.", "date": "2025-08-02", "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": "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": "2510.05387", "title": "Cross-Lingual Mental Health Ontologies for Indian Languages: Bridging Patient Expression and Clinical Understanding through Explainable AI and Human-in-the-Loop Validation", "abstract": "Mental health communication in India is linguistically fragmented, culturally diverse, and often underrepresented in clinical NLP. Current health ontologies and mental health resources are dominated by diagnostic frameworks centered on English or Western culture, leaving a gap in representing patient distress expressions in Indian languages. We propose cross-linguistic graphs of patient stress expressions (CL-PDE), a framework for building cross-lingual mental health ontologies through graph-based methods that capture culturally embedded expressions of distress, align them across languages, and link them with clinical terminology. Our approach addresses critical gaps in healthcare communication by grounding AI systems in culturally valid representations, allowing more inclusive and patient-centric NLP tools for mental health care in multilingual contexts.", "date": "2025-10-06", "categories": []}
{"id": "2510.15267", "title": "TraceCoder: Towards Traceable ICD Coding via Multi-Source Knowledge Integration", "abstract": "Automated International Classification of Diseases (ICD) coding assigns standardized diagnosis and procedure codes to clinical records, playing a critical role in healthcare systems. However, existing methods face challenges such as semantic gaps between clinical text and ICD codes, poor performance on rare and long-tail codes, and limited interpretability. To address these issues, we propose TraceCoder, a novel framework integrating multi-source external knowledge to enhance traceability and explainability in ICD coding. TraceCoder dynamically incorporates diverse knowledge sources, including UMLS, Wikipedia, and large language models (LLMs), to enrich code representations, bridge semantic gaps, and handle rare and ambiguous codes. It also introduces a hybrid attention mechanism to model interactions among labels, clinical context, and knowledge, improving long-tail code recognition and making predictions interpretable by grounding them in external evidence. Experiments on MIMIC-III-ICD9, MIMIC-IV-ICD9, and MIMIC-IV-ICD10 datasets demonstrate that TraceCoder achieves state-of-the-art performance, with ablation studies validating the effectiveness of its components. TraceCoder offers a scalable and robust solution for automated ICD coding, aligning with clinical needs for accuracy, interpretability, and reliability.", "date": "2025-10-17", "categories": []}
{"id": "2510.15269", "title": "TACL: Threshold-Adaptive Curriculum Learning Strategy for Enhancing Medical Text Understanding", "abstract": "Medical texts, particularly electronic medical records (EMRs), are a cornerstone of modern healthcare, capturing critical information about patient care, diagnoses, and treatments. These texts hold immense potential for advancing clinical decision-making and healthcare analytics. However, their unstructured nature, domain-specific language, and variability across contexts make automated understanding an intricate challenge. Despite the advancements in natural language processing, existing methods often treat all data as equally challenging, ignoring the inherent differences in complexity across clinical records. This oversight limits the ability of models to effectively generalize and perform well on rare or complex cases. In this paper, we present TACL (Threshold-Adaptive Curriculum Learning), a novel framework designed to address these challenges by rethinking how models interact with medical texts during training. Inspired by the principle of progressive learning, TACL dynamically adjusts the training process based on the complexity of individual samples. By categorizing data into difficulty levels and prioritizing simpler cases early in training, the model builds a strong foundation before tackling more complex records. By applying TACL to multilingual medical data, including English and Chinese clinical records, we observe significant improvements across diverse clinical tasks, including automatic ICD coding, readmission prediction and TCM syndrome differentiation. TACL not only enhances the performance of automated systems but also demonstrates the potential to unify approaches across disparate medical domains, paving the way for more accurate, scalable, and globally applicable medical text understanding solutions.", "date": "2025-10-17", "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": "2511.06215", "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": "2511.07011", "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": "2506.10751", "title": "Neural at ArchEHR-QA 2025: Agentic Prompt Optimization for Evidence-Grounded Clinical Question Answering", "abstract": "Automated question answering (QA) over electronic health records (EHRs) can bridge critical information gaps for clinicians and patients, yet it demands both precise evidence retrieval and faithful answer generation under limited supervision. In this work, we present Neural, the runner-up in the BioNLP 2025 ArchEHR-QA shared task on evidence-grounded clinical QA. Our proposed method decouples the task into (1) sentence-level evidence identification and (2) answer synthesis with explicit citations. For each stage, we automatically explore the prompt space with DSPy's MIPROv2 optimizer, jointly tuning instructions and few-shot demonstrations on the development set. A self-consistency voting scheme further improves evidence recall without sacrificing precision. On the hidden test set, our method attains an overall score of 51.5, placing second stage while outperforming standard zero-shot and few-shot prompting by over 20 and 10 points, respectively. These results indicate that data-driven prompt optimization is a cost-effective alternative to model fine-tuning for high-stakes clinical QA, advancing the reliability of AI assistants in healthcare.", "date": "2025-06-12", "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": "2511.05485", "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": "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": "2511.03048", "title": "ROBoto2: An Interactive System and Dataset for LLM-assisted Clinical Trial Risk of Bias Assessment", "abstract": "We present ROBOTO2, an open-source, web-based platform for large language model (LLM)-assisted risk of bias (ROB) assessment of clinical trials. ROBOTO2 streamlines the traditionally labor-intensive ROB v2 (ROB2) annotation process via an interactive interface that combines PDF parsing, retrieval-augmented LLM prompting, and human-in-the-loop review. Users can upload clinical trial reports, receive preliminary answers and supporting evidence for ROB2 signaling questions, and provide real-time feedback or corrections to system suggestions. ROBOTO2 is publicly available at https://roboto2.vercel.app/, with code and data released to foster reproducibility and adoption. We construct and release a dataset of 521 pediatric clinical trial reports (8954 signaling questions with 1202 evidence passages), annotated using both manually and LLM-assisted methods, serving as a benchmark and enabling future research. Using this dataset, we benchmark ROB2 performance for 4 LLMs and provide an analysis into current model capabilities and ongoing challenges in automating this critical aspect of systematic review.", "date": "2025-11-04", "categories": []}
{"id": "2510.23104", "title": "Leveraging Hierarchical Organization for Medical Multi-document Summarization", "abstract": "Medical multi-document summarization (MDS) is a complex task that requires effectively managing cross-document relationships. This paper investigates whether incorporating hierarchical structures in the inputs of MDS can improve a model's ability to organize and contextualize information across documents compared to traditional flat summarization methods. We investigate two ways of incorporating hierarchical organization across three large language models (LLMs), and conduct comprehensive evaluations of the resulting summaries using automated metrics, model-based metrics, and domain expert evaluation of preference, understandability, clarity, complexity, relevance, coverage, factuality, and coherence. Our results show that human experts prefer model-generated summaries over human-written summaries. Hierarchical approaches generally preserve factuality, coverage, and coherence of information, while also increasing human preference for summaries. Additionally, we examine whether simulated judgments from GPT-4 align with human judgments, finding higher agreement along more objective evaluation facets. Our findings demonstrate that hierarchical structures can improve the clarity of medical summaries generated by models while maintaining content coverage, providing a practical way to improve human preference for generated summaries.", "date": "2025-10-27", "categories": []}
{"id": "2511.01907", "title": "Between Myths and Metaphors: Rethinking LLMs for SRH in Conservative Contexts", "abstract": "Low-resource countries represent over 90% of maternal deaths, with Pakistan among the top four countries contributing nearly half in 2023. Since these deaths are mostly preventable, large language models (LLMs) can help address this crisis by automating health communication and risk assessment. However, sexual and reproductive health (SRH) communication in conservative contexts often relies on indirect language that obscures meaning, complicating LLM-based interventions. We conduct a two-stage study in Pakistan: (1) analyzing data from clinical observations, interviews, and focus groups with clinicians and patients, and (2) evaluating the interpretive capabilities of five popular LLMs on this data. Our analysis identifies two axes of communication (referential domain and expression approach) and shows LLMs struggle with semantic drift, myths, and polysemy in clinical interactions. We contribute: (1) empirical themes in SRH communication, (2) a categorization framework for indirect communication, (3) evaluation of LLM performance, and (4) design recommendations for culturally-situated SRH communication.", "date": "2025-10-31", "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": "2507.14681", "title": "Large Language Models as Medical Codes Selectors: a benchmark using the International Classification of Primary Care", "abstract": "Background: Medical coding structures healthcare data for research, quality monitoring, and policy. This study assesses the potential of large language models (LLMs) to assign ICPC-2 codes using the output of a domain-specific search engine.\n  Methods: A dataset of 437 Brazilian Portuguese clinical expressions, each annotated with ICPC-2 codes, was used. A semantic search engine (OpenAI's text-embedding-3-large) retrieved candidates from 73,563 labeled concepts. Thirty-three LLMs were prompted with each query and retrieved results to select the best-matching ICPC-2 code. Performance was evaluated using F1-score, along with token usage, cost, response time, and format adherence.\n  Results: Twenty-eight models achieved F1-score > 0.8; ten exceeded 0.85. Top performers included gpt-4.5-preview, o3, and gemini-2.5-pro. Retriever optimization can improve performance by up to 4 points. Most models returned valid codes in the expected format, with reduced hallucinations. Smaller models (<3B) struggled with formatting and input length.\n  Conclusions: LLMs show strong potential for automating ICPC-2 coding, even without fine-tuning. This work offers a benchmark and highlights challenges, but findings are limited by dataset scope and setup. Broader, multilingual, end-to-end evaluations are needed for clinical validation.", "date": "2025-07-19", "categories": []}
{"id": "2511.00421", "title": "MedRECT: A Medical Reasoning Benchmark for Error Correction in Clinical Texts", "abstract": "Large language models (LLMs) show increasing promise in medical applications, but their ability to detect and correct errors in clinical texts -- a prerequisite for safe deployment -- remains under-evaluated, particularly beyond English. We introduce MedRECT, a cross-lingual benchmark (Japanese/English) that formulates medical error handling as three subtasks: error detection, error localization (sentence extraction), and error correction. MedRECT is built with a scalable, automated pipeline from the Japanese Medical Licensing Examinations (JMLE) and a curated English counterpart, yielding MedRECT-ja (663 texts) and MedRECT-en (458 texts) with comparable error/no-error balance. We evaluate 9 contemporary LLMs spanning proprietary, open-weight, and reasoning families. Key findings: (i) reasoning models substantially outperform standard architectures, with up to 13.5% relative improvement in error detection and 51.0% in sentence extraction; (ii) cross-lingual evaluation reveals 5-10% performance gaps from English to Japanese, with smaller disparities for reasoning models; (iii) targeted LoRA fine-tuning yields asymmetric improvements in error correction performance (Japanese: +0.078, English: +0.168) while preserving reasoning capabilities; and (iv) our fine-tuned model exceeds human expert performance on structured medical error correction tasks. To our knowledge, MedRECT is the first comprehensive cross-lingual benchmark for medical error correction, providing a reproducible framework and resources for developing safer medical LLMs across languages.", "date": "2025-11-01", "categories": []}
{"id": "2511.01052", "title": "Knowledge Elicitation with Large Language Models for Interpretable Cancer Stage Identification from Pathology Reports", "abstract": "Cancer staging is critical for patient prognosis and treatment planning, yet extracting pathologic TNM staging from unstructured pathology reports poses a persistent challenge. Existing natural language processing (NLP) and machine learning (ML) strategies often depend on large annotated datasets, limiting their scalability and adaptability. In this study, we introduce two Knowledge Elicitation methods designed to overcome these limitations by enabling large language models (LLMs) to induce and apply domain-specific rules for cancer staging. The first, Knowledge Elicitation with Long-Term Memory (KEwLTM), uses an iterative prompting strategy to derive staging rules directly from unannotated pathology reports, without requiring ground-truth labels. The second, Knowledge Elicitation with Retrieval-Augmented Generation (KEwRAG), employs a variation of RAG where rules are pre-extracted from relevant guidelines in a single step and then applied, enhancing interpretability and avoiding repeated retrieval overhead. We leverage the ability of LLMs to apply broad knowledge learned during pre-training to new tasks. Using breast cancer pathology reports from the TCGA dataset, we evaluate their performance in identifying T and N stages, comparing them against various baseline approaches on two open-source LLMs. Our results indicate that KEwLTM outperforms KEwRAG when Zero-Shot Chain-of-Thought (ZSCOT) inference is effective, whereas KEwRAG achieves better performance when ZSCOT inference is less effective. Both methods offer transparent, interpretable interfaces by making the induced rules explicit. These findings highlight the promise of our Knowledge Elicitation methods as scalable, high-performing solutions for automated cancer staging with enhanced interpretability, particularly in clinical settings with limited annotated data.", "date": "2025-11-02", "categories": []}
{"id": "2504.11373", "title": "Cancer-Myth: Evaluating Large Language Models on Patient Questions with False Presuppositions", "abstract": "Cancer patients are increasingly turning to large language models (LLMs) for medical information, making it critical to assess how well these models handle complex, personalized questions. However, current medical benchmarks focus on medical exams or consumer-searched questions and do not evaluate LLMs on real patient questions with patient details. In this paper, we first have three hematology-oncology physicians evaluate cancer-related questions drawn from real patients. While LLM responses are generally accurate, the models frequently fail to recognize or address false presuppositions in the questions, posing risks to safe medical decision-making. To study this limitation systematically, we introduce Cancer-Myth, an expert-verified adversarial dataset of 585 cancer-related questions with false presuppositions. On this benchmark, no frontier LLM -- including GPT-5, Gemini-2.5-Pro, and Claude-4-Sonnet -- corrects these false presuppositions more than $43\\%$ of the time. To study mitigation strategies, we further construct a 150-question Cancer-Myth-NFP set, in which physicians confirm the absence of false presuppositions. We find typical mitigation strategies, such as adding precautionary prompts with GEPA optimization, can raise accuracy on Cancer-Myth to $80\\%$, but at the cost of misidentifying presuppositions in $41\\%$ of Cancer-Myth-NFP questions and causing a $10\\%$ relative performance drop on other medical benchmarks. These findings highlight a critical gap in the reliability of LLMs, show that prompting alone is not a reliable remedy for false presuppositions, and underscore the need for more robust safeguards in medical AI systems.", "date": "2025-04-15", "categories": []}
{"id": "2510.26974", "title": "Overview of the MEDIQA-OE 2025 Shared Task on Medical Order Extraction from Doctor-Patient Consultations", "abstract": "Clinical documentation increasingly uses automatic speech recognition and summarization, yet converting conversations into actionable medical orders for Electronic Health Records remains unexplored. A solution to this problem can significantly reduce the documentation burden of clinicians and directly impact downstream patient care. We introduce the MEDIQA-OE 2025 shared task, the first challenge on extracting medical orders from doctor-patient conversations. Six teams participated in the shared task and experimented with a broad range of approaches, and both closed- and open-weight large language models (LLMs). In this paper, we describe the MEDIQA-OE task, dataset, final leaderboard ranking, and participants' solutions.", "date": "2025-10-30", "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": "2510.27552", "title": "Multilingual BERT language model for medical tasks: Evaluation on domain-specific adaptation and cross-linguality", "abstract": "In multilingual healthcare applications, the availability of domain-specific natural language processing(NLP) tools is limited, especially for low-resource languages. Although multilingual bidirectional encoder representations from transformers (BERT) offers a promising motivation to mitigate the language gap, the medical NLP tasks in low-resource languages are still underexplored. Therefore, this study investigates how further pre-training on domain-specific corpora affects model performance on medical tasks, focusing on three languages: Dutch, Romanian and Spanish. In terms of further pre-training, we conducted four experiments to create medical domain models. Then, these models were fine-tuned on three downstream tasks: Automated patient screening in Dutch clinical notes, named entity recognition in Romanian and Spanish clinical notes. Results show that domain adaptation significantly enhanced task performance. Furthermore, further differentiation of domains, e.g. clinical and general biomedical domains, resulted in diverse performances. The clinical domain-adapted model outperformed the more general biomedical domain-adapted model. Moreover, we observed evidence of cross-lingual transferability. Moreover, we also conducted further investigations to explore potential reasons contributing to these performance differences. These findings highlight the feasibility of domain adaptation and cross-lingual ability in medical NLP. Within the low-resource language settings, these findings can provide meaningful guidance for developing multilingual medical NLP systems to mitigate the lack of training data and thereby improve the model performance.", "date": "2025-10-31", "categories": []}
{"id": "2510.25816", "title": "Beyond Long Context: When Semantics Matter More than Tokens", "abstract": "Electronic Health Records (EHR) store clinical documentation as base64 encoded attachments in FHIR DocumentReference resources, which makes semantic question answering difficult. Traditional vector database methods often miss nuanced clinical relationships. The Clinical Entity Augmented Retrieval (CLEAR) method, introduced by Lopez et al. 2025, uses entity aware retrieval and achieved improved performance with an F1 score of 0.90 versus 0.86 for embedding based retrieval, while using over 70 percent fewer tokens. We developed a Clinical Notes QA Evaluation Platform to validate CLEAR against zero shot large context inference and traditional chunk based retrieval augmented generation. The platform was tested on 12 clinical notes ranging from 10,000 to 65,000 tokens representing realistic EHR content. CLEAR achieved a 58.3 percent win rate, an average semantic similarity of 0.878, and used 78 percent fewer tokens than wide context processing. The largest performance gains occurred on long notes, with a 75 percent win rate for documents exceeding 65,000 tokens. These findings confirm that entity aware retrieval improves both efficiency and accuracy in clinical natural language processing. The evaluation framework provides a reusable and transparent benchmark for assessing clinical question answering systems where semantic precision and computational efficiency are critical.", "date": "2025-10-29", "categories": []}
{"id": "2510.26032", "title": "Artificial Intelligence-Enabled Analysis of Radiology Reports: Epidemiology and Consequences of Incidental Thyroid Findings", "abstract": "Importance Incidental thyroid findings (ITFs) are increasingly detected on imaging performed for non-thyroid indications. Their prevalence, features, and clinical consequences remain undefined. Objective To develop, validate, and deploy a natural language processing (NLP) pipeline to identify ITFs in radiology reports and assess their prevalence, features, and clinical outcomes. Design, Setting, and Participants Retrospective cohort of adults without prior thyroid disease undergoing thyroid-capturing imaging at Mayo Clinic sites from July 1, 2017, to September 30, 2023. A transformer-based NLP pipeline identified ITFs and extracted nodule characteristics from image reports from multiple modalities and body regions. Main Outcomes and Measures Prevalence of ITFs, downstream thyroid ultrasound, biopsy, thyroidectomy, and thyroid cancer diagnosis. Logistic regression identified demographic and imaging-related factors. Results Among 115,683 patients (mean age, 56.8 [SD 17.2] years; 52.9% women), 9,077 (7.8%) had an ITF, of which 92.9% were nodules. ITFs were more likely in women, older adults, those with higher BMI, and when imaging was ordered by oncology or internal medicine. Compared with chest CT, ITFs were more likely via neck CT, PET, and nuclear medicine scans. Nodule characteristics were poorly documented, with size reported in 44% and other features in fewer than 15% (e.g. calcifications). Compared with patients without ITFs, those with ITFs had higher odds of thyroid nodule diagnosis, biopsy, thyroidectomy and thyroid cancer diagnosis. Most cancers were papillary, and larger when detected after ITFs vs no ITF. Conclusions ITFs were common and strongly associated with cascades leading to the detection of small, low-risk cancers. These findings underscore the role of ITFs in thyroid cancer overdiagnosis and the need for standardized reporting and more selective follow-up.", "date": "2025-10-30", "categories": []}
{"id": "2510.04655", "title": "FT-MDT: Extracting Decision Trees from Medical Texts via a Novel Low-rank Adaptation Method", "abstract": "Knowledge of the medical decision process, which can be modeled as medical decision trees (MDTs), is critical to building clinical decision support systems. However, current MDT construction methods rely heavily on time-consuming and laborious manual annotation. To address this challenge, we propose PI-LoRA (Path-Integrated LoRA), a novel low-rank adaptation method for automatically extracting MDTs from clinical guidelines and textbooks. We integrate gradient path information to capture synergistic effects between different modules, enabling more effective and reliable rank allocation. This framework ensures that the most critical modules receive appropriate rank allocations while less important ones are pruned, resulting in a more efficient and accurate model for extracting medical decision trees from clinical texts. Extensive experiments on medical guideline datasets demonstrate that our PI-LoRA method significantly outperforms existing parameter-efficient fine-tuning approaches for the Text2MDT task, achieving better accuracy with substantially reduced model complexity. The proposed method achieves state-of-the-art results while maintaining a lightweight architecture, making it particularly suitable for clinical decision support systems where computational resources may be limited.", "date": "2025-10-06", "categories": []}
{"id": "2510.25007", "title": "Taming the Real-world Complexities in CPT E/M Coding with Large Language Models", "abstract": "Evaluation and Management (E/M) coding, under the Current Procedural Terminology (CPT) taxonomy, documents medical services provided to patients by physicians. Used primarily for billing purposes, it is in physicians' best interest to provide accurate CPT E/M codes. %While important, it is an auxiliary task that adds to physicians' documentation burden. Automating this coding task will help alleviate physicians' documentation burden, improve billing efficiency, and ultimately enable better patient care. However, a number of real-world complexities have made E/M encoding automation a challenging task. In this paper, we elaborate some of the key complexities and present ProFees, our LLM-based framework that tackles them, followed by a systematic evaluation. On an expert-curated real-world dataset, ProFees achieves an increase in coding accuracy of more than 36\\% over a commercial CPT E/M coding system and almost 5\\% over our strongest single-prompt baseline, demonstrating its effectiveness in addressing the real-world complexities.", "date": "2025-10-28", "categories": []}
{"id": "2510.25588", "title": "Standardization of Psychiatric Diagnoses -- Role of Fine-tuned LLM Consortium and OpenAI-gpt-oss Reasoning LLM Enabled Decision Support System", "abstract": "The diagnosis of most mental disorders, including psychiatric evaluations, primarily depends on dialogues between psychiatrists and patients. This subjective process can lead to variability in diagnoses across clinicians and patients, resulting in inconsistencies and challenges in achieving reliable outcomes. To address these issues and standardize psychiatric diagnoses, we propose a Fine-Tuned Large Language Model (LLM) Consortium and OpenAI-gpt-oss Reasoning LLM-enabled Decision Support System for the clinical diagnosis of mental disorders. Our approach leverages fine-tuned LLMs trained on conversational datasets involving psychiatrist-patient interactions focused on mental health conditions (e.g., depression). The diagnostic predictions from individual models are aggregated through a consensus-based decision-making process, refined by the OpenAI-gpt-oss reasoning LLM. We propose a novel method for deploying LLM agents that orchestrate communication between the LLM consortium and the reasoning LLM, ensuring transparency, reliability, and responsible AI across the entire diagnostic workflow. Experimental results demonstrate the transformative potential of combining fine-tuned LLMs with a reasoning model to create a robust and highly accurate diagnostic system for mental health assessment. A prototype of the proposed platform, integrating three fine-tuned LLMs with the OpenAI-gpt-oss reasoning LLM, was developed in collaboration with the U.S. Army Medical Research Team in Norfolk, Virginia, USA. To the best of our knowledge, this work represents the first application of a fine-tuned LLM consortium integrated with a reasoning LLM for clinical mental health diagnosis paving the way for next-generation AI-powered eHealth systems aimed at standardizing psychiatric diagnoses.", "date": "2025-10-29", "categories": []}
{"id": "2501.03727", "title": "Detecting Neurocognitive Disorders through Analyses of Topic Evolution and Cross-modal Consistency in Visual-Stimulated Narratives", "abstract": "Early detection of neurocognitive disorders (NCDs) is crucial for timely intervention and disease management. Given that language impairments manifest early in NCD progression, visual-stimulated narrative (VSN)-based analysis offers a promising avenue for NCD detection. Current VSN-based NCD detection methods primarily focus on linguistic microstructures (e.g., lexical diversity) that are closely tied to bottom-up, stimulus-driven cognitive processes. While these features illuminate basic language abilities, the higher-order linguistic macrostructures (e.g., topic development) that may reflect top-down, concept-driven cognitive abilities remain underexplored. These macrostructural patterns are crucial for NCD detection, yet challenging to quantify due to their abstract and complex nature. To bridge this gap, we propose two novel macrostructural approaches: (1) a Dynamic Topic Model (DTM) to track topic evolution over time, and (2) a Text-Image Temporal Alignment Network (TITAN) to measure cross-modal consistency between narrative and visual stimuli. Experimental results show the effectiveness of the proposed approaches in NCD detection, with TITAN achieving superior performance across three corpora: ADReSS (F1=0.8889), ADReSSo (F1=0.8504), and CU-MARVEL-RABBIT (F1=0.7238). Feature contribution analysis reveals that macrostructural features (e.g., topic variability, topic change rate, and topic consistency) constitute the most significant contributors to the model's decision pathways, outperforming the investigated microstructural features. These findings underscore the value of macrostructural analysis for understanding linguistic-cognitive interactions associated with NCDs.", "date": "2025-01-07", "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": "2505.20014", "title": "Does Rationale Quality Matter? Enhancing Mental Disorder Detection via Selective Reasoning Distillation", "abstract": "The detection of mental health problems from social media and the interpretation of these results have been extensively explored. Research has shown that incorporating clinical symptom information into a model enhances domain expertise, improving its detection and interpretation performance. While large language models (LLMs) are shown to be effective for generating explanatory rationales in mental health detection, their substantially large parameter size and high computational cost limit their practicality. Reasoning distillation transfers this ability to smaller language models (SLMs), but inconsistencies in the relevance and domain alignment of LLM-generated rationales pose a challenge. This paper investigates how rationale quality impacts SLM performance in mental health detection and explanation generation. We hypothesize that ensuring high-quality and domain-relevant rationales enhances the distillation. To this end, we propose a framework that selects rationales based on their alignment with expert clinical reasoning. Experiments show that our quality-focused approach significantly enhances SLM performance in both mental disorder detection and rationale generation. This work highlights the importance of rationale quality and offers an insightful framework for knowledge transfer in mental health applications.", "date": "2025-05-26", "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": "2508.08730", "title": "Magical: Medical Lay Language Generation via Semantic Invariance and Layperson-tailored Adaptation", "abstract": "Medical Lay Language Generation (MLLG) plays a vital role in improving the accessibility of complex scientific content for broader audiences. Recent literature to MLLG commonly employ parameter-efficient fine-tuning methods such as Low-Rank Adaptation (LoRA) to fine-tuning large language models (LLMs) using paired expert-lay language datasets. However, LoRA struggles with the challenges posed by multi-source heterogeneous MLLG datasets. Specifically, through a series of exploratory experiments, we reveal that standard LoRA fail to meet the requirement for semantic fidelity and diverse lay-style generation in MLLG task. To address these limitations, we propose Magical, an asymmetric LoRA architecture tailored for MLLG under heterogeneous data scenarios. Magical employs a shared matrix $A$ for abstractive summarization, along with multiple isolated matrices $B$ for diverse lay-style generation. To preserve semantic fidelity during the lay language generation process, Magical introduces a Semantic Invariance Constraint to mitigate semantic subspace shifts on matrix $A$. Furthermore, to better adapt to diverse lay-style generation, Magical incorporates the Recommendation-guided Switch, an externally interface to prompt the LLM to switch between different matrices $B$. Experimental results on three real-world lay language generation datasets demonstrate that Magical consistently outperforms prompt-based methods, vanilla LoRA, and its recent variants, while also reducing trainable parameters by 31.66%. Our code is publicly available at https://github.com/tianlwang/Magical.git.", "date": "2025-08-12", "categories": []}
{"id": "2510.21027", "title": "Customizing Open Source LLMs for Quantitative Medication Attribute Extraction across Heterogeneous EHR Systems", "abstract": "Harmonizing medication data across Electronic Health Record (EHR) systems is a persistent barrier to monitoring medications for opioid use disorder (MOUD). In heterogeneous EHR systems, key prescription attributes are scattered across differently formatted fields and freetext notes. We present a practical framework that customizes open source large language models (LLMs), including Llama, Qwen, Gemma, and MedGemma, to extract a unified set of MOUD prescription attributes (prescription date, drug name, duration, total quantity, daily quantity, and refills) from heterogeneous, site specific data and compute a standardized metric of medication coverage, \\emph{MOUD days}, per patient. Our pipeline processes records directly in a fixed JSON schema, followed by lightweight normalization and cross-field consistency checks. We evaluate the system on prescription level EHR data from five clinics in a national OUD study (25{,}605 records from 1{,}257 patients), using a previously annotated benchmark of 10{,}369 records (776 patients) as the ground truth. Performance is reported as coverage (share of records with a valid, matchable output) and record-level exact-match accuracy. Larger models perform best overall: Qwen2.5-32B achieves \\textbf{93.4\\%} coverage with \\textbf{93.0\\%} exact-match accuracy across clinics, and MedGemma-27B attains \\textbf{93.1\\%}/\\textbf{92.2\\%}. A brief error review highlights three common issues and fixes: imputing missing dosage fields using within-drug norms, handling monthly/weekly injectables (e.g., Vivitrol) by setting duration from the documented schedule, and adding unit checks to prevent mass units (e.g., ``250 g'') from being misread as daily counts. By removing brittle, site-specific ETL and supporting local, privacy-preserving deployment, this approach enables consistent cross-site analyses of MOUD exposure, adherence, and retention in real-world settings.", "date": "2025-10-23", "categories": []}
{"id": "2509.04462", "title": "Benchmarking GPT-5 for biomedical natural language processing", "abstract": "Biomedical literature and clinical narratives pose multifaceted challenges for natural language understanding, from precise entity extraction and document synthesis to multi-step diagnostic reasoning. This study extends a unified benchmark to evaluate GPT-5 and GPT-4o under zero-, one-, and five-shot prompting across five core biomedical NLP tasks: named entity recognition, relation extraction, multi-label document classification, summarization, and simplification, and nine expanded biomedical QA datasets covering factual knowledge, clinical reasoning, and multimodal visual understanding. Using standardized prompts, fixed decoding parameters, and consistent inference pipelines, we assessed model performance, latency, and token-normalized cost under official pricing. GPT-5 consistently outperformed GPT-4o, with the largest gains on reasoning-intensive datasets such as MedXpertQA and DiagnosisArena and stable improvements in multimodal QA. In core tasks, GPT-5 achieved better chemical NER and ChemProt scores but remained below domain-tuned baselines for disease NER and summarization. Despite producing longer outputs, GPT-5 showed comparable latency and 30 to 50 percent lower effective cost per correct prediction. Fine-grained analyses revealed improvements in diagnosis, treatment, and reasoning subtypes, whereas boundary-sensitive extraction and evidence-dense summarization remain challenging. Overall, GPT-5 approaches deployment-ready performance for biomedical QA while offering a favorable balance of accuracy, interpretability, and economic efficiency. The results support a tiered prompting strategy: direct prompting for large-scale or cost-sensitive applications, and chain-of-thought scaffolds for analytically complex or high-stakes scenarios, highlighting the continued need for hybrid solutions where precision and factual fidelity are critical.", "date": "2025-08-28", "categories": []}
{"id": "2510.20727", "title": "Automated Extraction of Fluoropyrimidine Treatment and Treatment-Related Toxicities from Clinical Notes Using Natural Language Processing", "abstract": "Objective: Fluoropyrimidines are widely prescribed for colorectal and breast cancers, but are associated with toxicities such as hand-foot syndrome and cardiotoxicity. Since toxicity documentation is often embedded in clinical notes, we aimed to develop and evaluate natural language processing (NLP) methods to extract treatment and toxicity information.\n  Materials and Methods: We constructed a gold-standard dataset of 236 clinical notes from 204,165 adult oncology patients. Domain experts annotated categories related to treatment regimens and toxicities. We developed rule-based, machine learning-based (Random Forest, Support Vector Machine [SVM], Logistic Regression [LR]), deep learning-based (BERT, ClinicalBERT), and large language models (LLM)-based NLP approaches (zero-shot and error-analysis prompting). Models used an 80:20 train-test split.\n  Results: Sufficient data existed to train and evaluate 5 annotated categories. Error-analysis prompting achieved optimal precision, recall, and F1 scores (F1=1.000) for treatment and toxicities extraction, whereas zero-shot prompting reached F1=1.000 for treatment and F1=0.876 for toxicities extraction.LR and SVM ranked second for toxicities (F1=0.937). Deep learning underperformed, with BERT (F1=0.873 treatment; F1= 0.839 toxicities) and ClinicalBERT (F1=0.873 treatment; F1 = 0.886 toxicities). Rule-based methods served as our baseline with F1 scores of 0.857 in treatment and 0.858 in toxicities.\n  Discussion: LMM-based approaches outperformed all others, followed by machine learning methods. Machine and deep learning approaches were limited by small training data and showed limited generalizability, particularly for rare categories.\n  Conclusion: LLM-based NLP most effectively extracted fluoropyrimidine treatment and toxicity information from clinical notes, and has strong potential to support oncology research and pharmacovigilance.", "date": "2025-10-23", "categories": []}
{"id": "2510.16708", "title": "Natural Language Processing for Cardiology: A Narrative Review", "abstract": "Cardiovascular diseases are becoming increasingly prevalent in modern society, with a profound impact on global health and well-being. These Cardiovascular disorders are complex and multifactorial, influenced by genetic predispositions, lifestyle choices, and diverse socioeconomic and clinical factors. Information about these interrelated factors is dispersed across multiple types of textual data, including patient narratives, medical records, and scientific literature. Natural language processing (NLP) has emerged as a powerful approach for analysing such unstructured data, enabling healthcare professionals and researchers to gain deeper insights that may transform the diagnosis, treatment, and prevention of cardiac disorders. This review provides a comprehensive overview of NLP research in cardiology from 2014 to 2025. We systematically searched six literature databases for studies describing NLP applications across a range of cardiovascular diseases. After a rigorous screening process, we identified 265 relevant articles. Each study was analysed across multiple dimensions, including NLP paradigms, cardiology-related tasks, disease types, and data sources. Our findings reveal substantial diversity within these dimensions, reflecting the breadth and evolution of NLP research in cardiology. A temporal analysis further highlights methodological trends, showing a progression from rule-based systems to large language models. Finally, we discuss key challenges and future directions, such as developing interpretable LLMs and integrating multimodal data. To the best of our knowledge, this review represents the most comprehensive synthesis of NLP research in cardiology to date.", "date": "2025-10-19", "categories": []}
{"id": "2510.19346", "title": "Local Obfuscation by GLINER for Impartial Context Aware Lineage: Development and evaluation of PII Removal system", "abstract": "Removing Personally Identifiable Information (PII) from clinical notes in Electronic Health Records (EHRs) is essential for research and AI development. While Large Language Models (LLMs) are powerful, their high computational costs and the data privacy risks of API-based services limit their use, especially in low-resource settings. To address this, we developed LOGICAL (Local Obfuscation by GLINER for Impartial Context-Aware Lineage), an efficient, locally deployable PII removal system built on a fine-tuned Generalist and Lightweight Named Entity Recognition (GLiNER) model. We used 1515 clinical documents from a psychiatric hospital's EHR system. We defined nine PII categories for removal. A modern-gliner-bi-large-v1.0 model was fine-tuned on 2849 text instances and evaluated on a test set of 376 instances using character-level precision, recall, and F1-score. We compared its performance against Microsoft Azure NER, Microsoft Presidio, and zero-shot prompting with Gemini-Pro-2.5 and Llama-3.3-70B-Instruct. The fine-tuned GLiNER model achieved superior performance, with an overall micro-average F1-score of 0.980, significantly outperforming Gemini-Pro-2.5 (F1-score: 0.845). LOGICAL correctly sanitised 95% of documents completely, compared to 64% for the next-best solution. The model operated efficiently on a standard laptop without a dedicated GPU. However, a 2% entity-level false negative rate underscores the need for human-in-the-loop validation across all tested systems. Fine-tuned, specialised transformer models like GLiNER offer an accurate, computationally efficient, and secure solution for PII removal from clinical notes. This \"sanitisation at the source\" approach is a practical alternative to resource-intensive LLMs, enabling the creation of de-identified datasets for research and AI development while preserving data privacy, particularly in resource-constrained environments.", "date": "2025-10-22", "categories": []}
{"id": "2510.10025", "title": "Lightweight Baselines for Medical Abstract Classification: DistilBERT with Cross-Entropy as a Strong Default", "abstract": "The research evaluates lightweight medical abstract classification methods to establish their maximum performance capabilities under financial budget restrictions. On the public medical abstracts corpus, we finetune BERT base and Distil BERT with three objectives cross entropy (CE), class weighted CE, and focal loss under identical tokenization, sequence length, optimizer, and schedule. DistilBERT with plain CE gives the strongest raw argmax trade off, while a post hoc operating point selection (validation calibrated, classwise thresholds) sub stantially improves deployed performance; under this tuned regime, focal benefits most. We report Accuracy, Macro F1, and WeightedF1, release evaluation artifacts, and include confusion analyses to clarify error structure. The practical takeaway is to start with a compact encoder and CE, then add lightweight calibration or thresholding when deployment requires higher macro balance.", "date": "2025-10-11", "categories": []}
{"id": "2510.17892", "title": "Advances in Pre-trained Language Models for Domain-Specific Text Classification: A Systematic Review", "abstract": "The exponential increase in scientific literature and online information necessitates efficient methods for extracting knowledge from textual data. Natural language processing (NLP) plays a crucial role in addressing this challenge, particularly in text classification tasks. While large language models (LLMs) have achieved remarkable success in NLP, their accuracy can suffer in domain-specific contexts due to specialized vocabulary, unique grammatical structures, and imbalanced data distributions. In this systematic literature review (SLR), we investigate the utilization of pre-trained language models (PLMs) for domain-specific text classification. We systematically review 41 articles published between 2018 and January 2024, adhering to the PRISMA statement (preferred reporting items for systematic reviews and meta-analyses). This review methodology involved rigorous inclusion criteria and a multi-step selection process employing AI-powered tools. We delve into the evolution of text classification techniques and differentiate between traditional and modern approaches. We emphasize transformer-based models and explore the challenges and considerations associated with using LLMs for domain-specific text classification. Furthermore, we categorize existing research based on various PLMs and propose a taxonomy of techniques used in the field. To validate our findings, we conducted a comparative experiment involving BERT, SciBERT, and BioBERT in biomedical sentence classification. Finally, we present a comparative study on the performance of LLMs in text classification tasks across different domains. In addition, we examine recent advancements in PLMs for domain-specific text classification and offer insights into future directions and limitations in this rapidly evolving domain.", "date": "2025-10-18", "categories": []}
{"id": "2510.18475", "title": "DART: A Structured Dataset of Regulatory Drug Documents in Italian for Clinical NLP", "abstract": "The extraction of pharmacological knowledge from regulatory documents has become a key focus in biomedical natural language processing, with applications ranging from adverse event monitoring to AI-assisted clinical decision support. However, research in this field has predominantly relied on English-language corpora such as DrugBank, leaving a significant gap in resources tailored to other healthcare systems. To address this limitation, we introduce DART (Drug Annotation from Regulatory Texts), the first structured corpus of Italian Summaries of Product Characteristics derived from the official repository of the Italian Medicines Agency (AIFA). The dataset was built through a reproducible pipeline encompassing web-scale document retrieval, semantic segmentation of regulatory sections, and clinical summarization using a few-shot-tuned large language model with low-temperature decoding. DART provides structured information on key pharmacological domains such as indications, adverse drug reactions, and drug-drug interactions. To validate its utility, we implemented an LLM-based drug interaction checker that leverages the dataset to infer clinically meaningful interactions. Experimental results show that instruction-tuned LLMs can accurately infer potential interactions and their clinical implications when grounded in the structured textual fields of DART. We publicly release our code on GitHub: https://github.com/PRAISELab-PicusLab/DART.", "date": "2025-10-21", "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": "2510.17437", "title": "Multilingual Clinical NER for Diseases and Medications Recognition in Cardiology Texts using BERT Embeddings", "abstract": "The rapidly increasing volume of electronic health record (EHR) data underscores a pressing need to unlock biomedical knowledge from unstructured clinical texts to support advancements in data-driven clinical systems, including patient diagnosis, disease progression monitoring, treatment effects assessment, prediction of future clinical events, etc. While contextualized language models have demonstrated impressive performance improvements for named entity recognition (NER) systems in English corpora, there remains a scarcity of research focused on clinical texts in low-resource languages. To bridge this gap, our study aims to develop multiple deep contextual embedding models to enhance clinical NER in the cardiology domain, as part of the BioASQ MultiCardioNER shared task. We explore the effectiveness of different monolingual and multilingual BERT-based models, trained on general domain text, for extracting disease and medication mentions from clinical case reports written in English, Spanish, and Italian. We achieved an F1-score of 77.88% on Spanish Diseases Recognition (SDR), 92.09% on Spanish Medications Recognition (SMR), 91.74% on English Medications Recognition (EMR), and 88.9% on Italian Medications Recognition (IMR). These results outperform the mean and median F1 scores in the test leaderboard across all subtasks, with the mean/median values being: 69.61%/75.66% for SDR, 81.22%/90.18% for SMR, 89.2%/88.96% for EMR, and 82.8%/87.76% for IMR.", "date": "2025-10-20", "categories": []}
{"id": "2502.21107", "title": "Generating patient cohorts from electronic health records using two-step retrieval-augmented text-to-SQL generation", "abstract": "Clinical cohort definition is crucial for patient recruitment and observational studies, yet translating inclusion/exclusion criteria into SQL queries remains challenging and manual. We present an automated system utilizing large language models that combines criteria parsing, two-level retrieval augmented generation with specialized knowledge bases, medical concept standardization, and SQL generation to retrieve patient cohorts with patient funnels. The system achieves 0.75 F1-score in cohort identification on EHR data, effectively capturing complex temporal and logical relationships. These results demonstrate the feasibility of automated cohort generation for epidemiological research.", "date": "2025-02-28", "categories": []}
{"id": "2510.14169", "title": "JEDA: Query-Free Clinical Order Search from Ambient Dialogues", "abstract": "Clinical conversations mix explicit directives (order a chest X-ray) with implicit reasoning (the cough worsened overnight, we should check for pneumonia). Many systems rely on LLM rewriting, adding latency, instability, and opacity that hinder real-time ordering. We present JEDA (Joint Embedding for Direct and Ambient clinical orders), a domain-initialized bi-encoder that retrieves canonical orders directly and, in a query-free mode, encodes a short rolling window of ambient dialogue to trigger retrieval. Initialized from PubMedBERT and fine-tuned with a duplicate-safe contrastive objective, JEDA aligns heterogeneous expressions of intent to shared order concepts. Training uses constrained LLM guidance to tie each signed order to complementary formulations (command only, context only, command+context, context+reasoning), producing clearer inter-order separation, tighter query extendash order coupling, and stronger generalization. The query-free mode is noise-resilient, reducing sensitivity to disfluencies and ASR errors by conditioning on a short window rather than a single utterance. Deployed in practice, JEDA yields large gains and substantially outperforms its base encoder and recent open embedders (Linq Embed Mistral, SFR Embedding, GTE Qwen, BGE large, Embedding Gemma). The result is a fast, interpretable, LLM-free retrieval layer that links ambient context to actionable clinical orders in real time.", "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": "2510.13856", "title": "Multimodal Retrieval-Augmented Generation with Large Language Models for Medical VQA", "abstract": "Medical Visual Question Answering (MedVQA) enables natural language queries over medical images to support clinical decision-making and patient care. The MEDIQA-WV 2025 shared task addressed wound-care VQA, requiring systems to generate free-text responses and structured wound attributes from images and patient queries. We present the MasonNLP system, which employs a general-domain, instruction-tuned large language model with a retrieval-augmented generation (RAG) framework that incorporates textual and visual examples from in-domain data. This approach grounds outputs in clinically relevant exemplars, improving reasoning, schema adherence, and response quality across dBLEU, ROUGE, BERTScore, and LLM-based metrics. Our best-performing system ranked 3rd among 19 teams and 51 submissions with an average score of 41.37%, demonstrating that lightweight RAG with general-purpose LLMs -- a minimal inference-time layer that adds a few relevant exemplars via simple indexing and fusion, with no extra training or complex re-ranking -- provides a simple and effective baseline for multimodal clinical NLP tasks.", "date": "2025-10-12", "categories": []}
{"id": "2510.13873", "title": "FRACCO: A gold-standard annotated corpus of oncological entities with ICD-O-3.1 normalisation", "abstract": "Developing natural language processing tools for clinical text requires annotated datasets, yet French oncology resources remain scarce. We present FRACCO (FRench Annotated Corpus for Clinical Oncology) an expert-annotated corpus of 1301 synthetic French clinical cases, initially translated from the Spanish CANTEMIST corpus as part of the FRASIMED initiative. Each document is annotated with terms related to morphology, topography, and histologic differentiation, using the International Classification of Diseases for Oncology (ICD-O) as reference. An additional annotation layer captures composite expression-level normalisations that combine multiple ICD-O elements into unified clinical concepts. Annotation quality was ensured through expert review: 1301 texts were manually annotated for entity spans by two domain experts. A total of 71127 ICD-O normalisations were produced through a combination of automated matching and manual validation by a team of five annotators. The final dataset representing 399 unique morphology codes (from 2549 different expressions), 272 topography codes (from 3143 different expressions), and 2043 unique composite expressions (from 11144 different expressions). This dataset provides a reference standard for named entity recognition and concept normalisation in French oncology texts.", "date": "2025-10-13", "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": "2510.14332", "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": "2510.14937", "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": "2502.14614", "title": "ICA-RAG: Information Completeness Guided Adaptive Retrieval-Augmented Generation for Disease Diagnosis", "abstract": "Retrieval-Augmented Large Language Models (LLMs), which integrate external knowledge, have shown remarkable performance in medical domains, including clinical diagnosis. However, existing RAG methods often struggle to tailor retrieval strategies to diagnostic difficulty and input sample informativeness. This limitation leads to excessive and often unnecessary retrieval, impairing computational efficiency and increasing the risk of introducing noise that can degrade diagnostic accuracy. To address this, we propose ICA-RAG (\\textbf{I}nformation \\textbf{C}ompleteness Guided \\textbf{A}daptive \\textbf{R}etrieval-\\textbf{A}ugmented \\textbf{G}eneration), a novel framework for enhancing RAG reliability in disease diagnosis. ICA-RAG utilizes an adaptive control module to assess the necessity of retrieval based on the input's information completeness. By optimizing retrieval and incorporating knowledge filtering, ICA-RAG better aligns retrieval operations with clinical requirements. Experiments on three Chinese electronic medical record datasets demonstrate that ICA-RAG significantly outperforms baseline methods, highlighting its effectiveness in clinical diagnosis.", "date": "2025-02-20", "categories": []}
{"id": "2505.21908", "title": "Reinforcement Learning for Out-of-Distribution Reasoning in LLMs: An Empirical Study on Diagnosis-Related Group Coding", "abstract": "Diagnosis-Related Group (DRG) codes are essential for hospital reimbursement and operations but require labor-intensive assignment. Large Language Models (LLMs) struggle with DRG coding due to the out-of-distribution (OOD) nature of the task: pretraining corpora rarely contain private clinical or billing data. We introduce DRG-Sapphire, which uses large-scale reinforcement learning (RL) for automated DRG coding from clinical notes. Built on Qwen2.5-7B and trained with Group Relative Policy Optimization (GRPO) using rule-based rewards, DRG-Sapphire introduces a series of RL enhancements to address domain-specific challenges not seen in previous mathematical tasks. Our model achieves state-of-the-art accuracy on the MIMIC-IV benchmark and generates physician-validated reasoning for DRG assignments, significantly enhancing explainability. Our study further sheds light on broader challenges of applying RL to knowledge-intensive, OOD tasks. We observe that RL performance scales approximately linearly with the logarithm of the number of supervised fine-tuning (SFT) examples, suggesting that RL effectiveness is fundamentally constrained by the domain knowledge encoded in the base model. For OOD tasks like DRG coding, strong RL performance requires sufficient knowledge infusion prior to RL. Consequently, scaling SFT may be more effective and computationally efficient than scaling RL alone for such tasks.", "date": "2025-05-28", "categories": []}
{"id": "2510.12813", "title": "Cancer Diagnosis Categorization in Electronic Health Records Using Large Language Models and BioBERT: Model Performance Evaluation Study", "abstract": "Electronic health records contain inconsistently structured or free-text data, requiring efficient preprocessing to enable predictive health care models. Although artificial intelligence-driven natural language processing tools show promise for automating diagnosis classification, their comparative performance and clinical reliability require systematic evaluation. The aim of this study is to evaluate the performance of 4 large language models (GPT-3.5, GPT-4o, Llama 3.2, and Gemini 1.5) and BioBERT in classifying cancer diagnoses from structured and unstructured electronic health records data. We analyzed 762 unique diagnoses (326 International Classification of Diseases (ICD) code descriptions, 436free-text entries) from 3456 records of patients with cancer. Models were tested on their ability to categorize diagnoses into 14predefined categories. Two oncology experts validated classifications. BioBERT achieved the highest weighted macro F1-score for ICD codes (84.2) and matched GPT-4o in ICD code accuracy (90.8). For free-text diagnoses, GPT-4o outperformed BioBERT in weighted macro F1-score (71.8 vs 61.5) and achieved slightly higher accuracy (81.9 vs 81.6). GPT-3.5, Gemini, and Llama showed lower overall performance on both formats. Common misclassification patterns included confusion between metastasis and central nervous system tumors, as well as errors involving ambiguous or overlapping clinical terminology. Although current performance levels appear sufficient for administrative and research use, reliable clinical applications will require standardized documentation practices alongside robust human oversight for high-stakes decision-making.", "date": "2025-10-08", "categories": []}
{"id": "2510.13115", "title": "Multi-Label Clinical Text Eligibility Classification and Summarization System", "abstract": "Clinical trials are central to medical progress because they help improve understanding of human health and the healthcare system. They play a key role in discovering new ways to detect, prevent, or treat diseases, and it is essential that clinical trials include participants with appropriate and diverse medical backgrounds. In this paper, we propose a system that leverages Natural Language Processing (NLP) and Large Language Models (LLMs) to automate multi-label clinical text eligibility classification and summarization. The system combines feature extraction methods such as word embeddings (Word2Vec) and named entity recognition to identify relevant medical concepts, along with traditional vectorization techniques such as count vectorization and TF-IDF (Term Frequency-Inverse Document Frequency). We further explore weighted TF-IDF word embeddings that integrate both count-based and embedding-based strengths to capture term importance effectively. Multi-label classification using Random Forest and SVM models is applied to categorize documents based on eligibility criteria. Summarization techniques including TextRank, Luhn, and GPT-3 are evaluated to concisely summarize eligibility requirements. Evaluation with ROUGE scores demonstrates the effectiveness of the proposed methods. This system shows potential for automating clinical trial eligibility assessment using data-driven approaches, thereby improving research efficiency.", "date": "2025-10-15", "categories": []}
{"id": "2510.13624", "title": "Unlocking Public Catalogues: Instruction-Tuning LLMs for ICD Coding of German Tumor Diagnoses", "abstract": "Accurate coding of tumor diagnoses with ICD-10-GM and ICD-O-3 is essential for structured cancer documentation in Germany. Smaller open-weight LLMs are appealing for privacy-preserving automation but often struggle with coding accuracy in German-language contexts. This study investigates whether instruction-based fine-tuning on public datasets improves the coding accuracy of open-weight LLMs for German tumor diagnosis texts. The evaluation uses coded diagnoses from the local tumor documentation system as test data. In a systematic data quality assessment, the upper limit for ICD-10 coding performance was estimated at 60-79% for exact and 81-94% for partial (three-character codes only) derivation. As training data, over 500,000 question-answer pairs were created based on the ICD-10-GM, ICD-O-3, and OPS catalogues. Eight open-weight models from the Qwen, Llama, and Mistral families (7-70 B parameters) were fine-tuned. ICD-10-GM accuracy rose from 1.4-24% to 41-58%, and partial accuracy from 31-74% to 73-83%. The accuracy of ICD-O-3 topography coding also improved but started and remained considerably lower with an exact accuracy of 22-40% and a partial accuracy of 56-67% after fine-tuning. Malformed code outputs dropped to 0% for all models. Tumor-diagnosis recognition reached 99%. Accuracy correlated positively with model size, but gaps between small and large models narrowed after fine-tuning. The reasoning mode in Qwen3 generally yielded a lower performance than fine-tuning and was over 100 times slower. Our findings highlight the potential of leveraging public catalogues to build instruction datasets that improve LLMs in medical documentation tasks. The complete training dataset and the best-performing checkpoints of the fine-tuned models are available from https://huggingface.co/datasets/stefan-m-lenz/ICDOPS-QA-2024.", "date": "2025-10-15", "categories": []}
{"id": "2510.10475", "title": "Assessing Large Language Models for Structured Medical Order Extraction", "abstract": "Medical order extraction is essential for structuring actionable clinical information, supporting decision-making, and enabling downstream applications such as documentation and workflow automation. Orders may be embedded in diverse sources, including electronic health records, discharge summaries, and multi-turn doctor-patient dialogues, and can span categories such as medications, laboratory tests, imaging studies, and follow-up actions. The MEDIQA-OE 2025 shared task focuses on extracting structured medical orders from extended conversational transcripts, requiring the identification of order type, description, reason, and provenance. We present the MasonNLP submission, which ranked 5th among 17 participating teams with 105 total submissions. Our approach uses a general-purpose, instruction-tuned LLaMA-4 17B model without domain-specific fine-tuning, guided by a single in-context example. This few-shot configuration achieved an average F1 score of 37.76, with notable improvements in reason and provenance accuracy. These results demonstrate that large, non-domain-specific LLMs, when paired with effective prompt engineering, can serve as strong, scalable baselines for specialized clinical NLP tasks.", "date": "2025-10-12", "categories": []}
{"id": "2510.04888", "title": "Revealing Interconnections between Diseases: from Statistical Methods to Large Language Models", "abstract": "Identifying disease interconnections through manual analysis of large-scale clinical data is labor-intensive, subjective, and prone to expert disagreement. While machine learning (ML) shows promise, three critical challenges remain: (1) selecting optimal methods from the vast ML landscape, (2) determining whether real-world clinical data (e.g., electronic health records, EHRs) or structured disease descriptions yield more reliable insights, (3) the lack of \"ground truth,\" as some disease interconnections remain unexplored in medicine. Large language models (LLMs) demonstrate broad utility, yet they often lack specialized medical knowledge. To address these gaps, we conduct a systematic evaluation of seven approaches for uncovering disease relationships based on two data sources: (i) sequences of ICD-10 codes from MIMIC-IV EHRs and (ii) the full set of ICD-10 codes, both with and without textual descriptions. Our framework integrates the following: (i) a statistical co-occurrence analysis and a masked language modeling (MLM) approach using real clinical data; (ii) domain-specific BERT variants (Med-BERT and BioClinicalBERT); (iii) a general-purpose BERT and document retrieval; and (iv) four LLMs (Mistral, DeepSeek, Qwen, and YandexGPT). Our graph-based comparison of the obtained interconnection matrices shows that the LLM-based approach produces interconnections with the lowest diversity of ICD code connections to different diseases compared to other methods, including text-based and domain-based approaches. This suggests an important implication: LLMs have limited potential for discovering new interconnections. In the absence of ground truth databases for medical interconnections between ICD codes, our results constitute a valuable medical disease ontology that can serve as a foundational resource for future clinical research and artificial intelligence applications in healthcare.", "date": "2025-10-06", "categories": []}
{"id": "2507.17717", "title": "From Feedback to Checklists: Grounded Evaluation of AI-Generated Clinical Notes", "abstract": "AI-generated clinical notes are increasingly used in healthcare, but evaluating their quality remains a challenge due to high subjectivity and limited scalability of expert review. Existing automated metrics often fail to align with real-world physician preferences. To address this, we propose a pipeline that systematically distills real user feedback into structured checklists for note evaluation. These checklists are designed to be interpretable, grounded in human feedback, and enforceable by LLM-based evaluators. Using deidentified data from over 21,000 clinical encounters (prepared in accordance with the HIPAA safe harbor standard) from a deployed AI medical scribe system, we show that our feedback-derived checklist outperforms a baseline approach in our offline evaluations in coverage, diversity, and predictive power for human ratings. Extensive experiments confirm the checklist's robustness to quality-degrading perturbations, significant alignment with clinician preferences, and practical value as an evaluation methodology. In offline research settings, our checklist offers a practical tool for flagging notes that may fall short of our defined quality standards.", "date": "2025-07-23", "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": "2509.05378", "title": "Code Like Humans: A Multi-Agent Solution for Medical Coding", "abstract": "In medical coding, experts map unstructured clinical notes to alphanumeric codes for diagnoses and procedures. We introduce Code Like Humans: a new agentic framework for medical coding with large language models. It implements official coding guidelines for human experts, and it is the first solution that can support the full ICD-10 coding system (+70K labels). It achieves the best performance to date on rare diagnosis codes (fine-tuned discriminative classifiers retain an advantage for high-frequency codes, to which they are limited). Towards future work, we also contribute an analysis of system performance and identify its `blind spots' (codes that are systematically undercoded).", "date": "2025-09-04", "categories": []}
{"id": "2510.06232", "title": "Neu-RadBERT for Enhanced Diagnosis of Brain Injuries and Conditions", "abstract": "Objective: We sought to develop a classification algorithm to extract diagnoses from free-text radiology reports of brain imaging performed in patients with acute respiratory failure (ARF) undergoing invasive mechanical ventilation. Methods: We developed and fine-tuned Neu-RadBERT, a BERT-based model, to classify unstructured radiology reports. We extracted all the brain imaging reports (computed tomography and magnetic resonance imaging) from MIMIC-IV database, performed in patients with ARF. Initial manual labelling was performed on a subset of reports for various brain abnormalities, followed by fine-tuning Neu-RadBERT using three strategies: 1) baseline RadBERT, 2) Neu-RadBERT with Masked Language Modeling (MLM) pretraining, and 3) Neu-RadBERT with MLM pretraining and oversampling to address data skewness. We compared the performance of this model to Llama-2-13B, an autoregressive LLM. Results: The Neu-RadBERT model, particularly with oversampling, demonstrated significant improvements in diagnostic accuracy compared to baseline RadBERT for brain abnormalities, achieving up to 98.0% accuracy for acute brain injuries. Llama-2-13B exhibited relatively lower performance, peaking at 67.5% binary classification accuracy. This result highlights potential limitations of current autoregressive LLMs for this specific classification task, though it remains possible that larger models or further fine-tuning could improve performance. Conclusion: Neu-RadBERT, enhanced through target domain pretraining and oversampling techniques, offered a robust tool for accurate and reliable diagnosis of neurological conditions from radiology reports. This study underscores the potential of transformer-based NLP models in automatically extracting diagnoses from free text reports with potential applications to both research and patient care.", "date": "2025-10-01", "categories": []}
{"id": "2510.06263", "title": "Dual-stage and Lightweight Patient Chart Summarization for Emergency Physicians", "abstract": "Electronic health records (EHRs) contain extensive unstructured clinical data that can overwhelm emergency physicians trying to identify critical information. We present a two-stage summarization system that runs entirely on embedded devices, enabling offline clinical summarization while preserving patient privacy. In our approach, a dual-device architecture first retrieves relevant patient record sections using the Jetson Nano-R (Retrieve), then generates a structured summary on another Jetson Nano-S (Summarize), communicating via a lightweight socket link. The summarization output is two-fold: (1) a fixed-format list of critical findings, and (2) a context-specific narrative focused on the clinician's query. The retrieval stage uses locally stored EHRs, splits long notes into semantically coherent sections, and searches for the most relevant sections per query. The generation stage uses a locally hosted small language model (SLM) to produce the summary from the retrieved text, operating within the constraints of two NVIDIA Jetson devices. We first benchmarked six open-source SLMs under 7B parameters to identify viable models. We incorporated an LLM-as-Judge evaluation mechanism to assess summary quality in terms of factual accuracy, completeness, and clarity. Preliminary results on MIMIC-IV and de-identified real EHRs demonstrate that our fully offline system can effectively produce useful summaries in under 30 seconds.", "date": "2025-10-05", "categories": []}
{"id": "2504.08596", "title": "MedHal: An Evaluation Dataset for Medical Hallucination Detection", "abstract": "We present MedHal, a novel large-scale dataset specifically designed to evaluate if models can detect hallucinations in medical texts. Current hallucination detection methods face significant limitations when applied to specialized domains like medicine, where they can have disastrous consequences. Existing medical datasets are either too small, containing only a few hundred samples, or focus on a single task like Question Answering or Natural Language Inference. MedHal addresses these gaps by: (1) incorporating diverse medical text sources and tasks; (2) providing a substantial volume of annotated samples suitable for training medical hallucination detection models; and (3) including explanations for factual inconsistencies to guide model learning. We demonstrate MedHal's utility by training and evaluating a baseline medical hallucination detection model, showing improvements over general-purpose hallucination detection approaches. This resource enables more efficient evaluation of medical text generation systems while reducing reliance on costly expert review, potentially accelerating the development of medical AI research.", "date": "2025-04-11", "categories": []}
{"id": "2510.05128", "title": "Advancing Automated Spatio-Semantic Analysis in Picture Description Using Language Models", "abstract": "Current methods for automated assessment of cognitive-linguistic impairment via picture description often neglect the visual narrative path - the sequence and locations of elements a speaker described in the picture. Analyses of spatio-semantic features capture this path using content information units (CIUs), but manual tagging or dictionary-based mapping is labor-intensive. This study proposes a BERT-based pipeline, fine tuned with binary cross-entropy and pairwise ranking loss, for automated CIU extraction and ordering from the Cookie Theft picture description. Evaluated by 5-fold cross-validation, it achieves 93% median precision, 96% median recall in CIU detection, and 24% sequence error rates. The proposed method extracts features that exhibit strong Pearson correlations with ground truth, surpassing the dictionary-based baseline in external validation. These features also perform comparably to those derived from manual annotations in evaluating group differences via ANCOVA. The pipeline is shown to effectively characterize visual narrative paths for cognitive impairment assessment, with the implementation and models open-sourced to public.", "date": "2025-09-30", "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": "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": "2510.03102", "title": "Semantic Similarity in Radiology Reports via LLMs and NER", "abstract": "Radiology report evaluation is a crucial part of radiologists' training and plays a key role in ensuring diagnostic accuracy. As part of the standard reporting workflow, a junior radiologist typically prepares a preliminary report, which is then reviewed and edited by a senior radiologist to produce the final report. Identifying semantic differences between preliminary and final reports is essential for junior doctors, both as a training tool and to help uncover gaps in clinical knowledge. While AI in radiology is a rapidly growing field, the application of large language models (LLMs) remains challenging due to the need for specialised domain knowledge. In this paper, we explore the ability of LLMs to provide explainable and accurate comparisons of reports in the radiology domain. We begin by comparing the performance of several LLMs in comparing radiology reports. We then assess a more traditional approach based on Named-Entity-Recognition (NER). However, both approaches exhibit limitations in delivering accurate feedback on semantic similarity. To address this, we propose Llama-EntScore, a semantic similarity scoring method using a combination of Llama 3.1 and NER with tunable weights to emphasise or de-emphasise specific types of differences. Our approach generates a quantitative similarity score for tracking progress and also gives an interpretation of the score that aims to offer valuable guidance in reviewing and refining their reporting. We find our method achieves 67% exact-match accuracy and 93% accuracy within +/- 1 when compared to radiologist-provided ground truth scores - outperforming both LLMs and NER used independently. Code is available at: https://github.com/otmive/llama_reports", "date": "2025-10-03", "categories": []}
{"id": "2510.03577", "title": "LLM, Reporting In! Medical Information Extraction Across Prompting, Fine-tuning and Post-correction", "abstract": "This work presents our participation in the EvalLLM 2025 challenge on biomedical Named Entity Recognition (NER) and health event extraction in French (few-shot setting). For NER, we propose three approaches combining large language models (LLMs), annotation guidelines, synthetic data, and post-processing: (1) in-context learning (ICL) with GPT-4.1, incorporating automatic selection of 10 examples and a summary of the annotation guidelines into the prompt, (2) the universal NER system GLiNER, fine-tuned on a synthetic corpus and then verified by an LLM in post-processing, and (3) the open LLM LLaMA-3.1-8B-Instruct, fine-tuned on the same synthetic corpus. Event extraction uses the same ICL strategy with GPT-4.1, reusing the guideline summary in the prompt. Results show GPT-4.1 leads with a macro-F1 of 61.53% for NER and 15.02% for event extraction, highlighting the importance of well-crafted prompting to maximize performance in very low-resource scenarios.", "date": "2025-10-03", "categories": []}
{"id": "2510.03700", "title": "H-DDx: A Hierarchical Evaluation Framework for Differential Diagnosis", "abstract": "An accurate differential diagnosis (DDx) is essential for patient care, shaping therapeutic decisions and influencing outcomes. Recently, Large Language Models (LLMs) have emerged as promising tools to support this process by generating a DDx list from patient narratives. However, existing evaluations of LLMs in this domain primarily rely on flat metrics, such as Top-k accuracy, which fail to distinguish between clinically relevant near-misses and diagnostically distant errors. To mitigate this limitation, we introduce H-DDx, a hierarchical evaluation framework that better reflects clinical relevance. H-DDx leverages a retrieval and reranking pipeline to map free-text diagnoses to ICD-10 codes and applies a hierarchical metric that credits predictions closely related to the ground-truth diagnosis. In benchmarking 22 leading models, we show that conventional flat metrics underestimate performance by overlooking clinically meaningful outputs, with our results highlighting the strengths of domain-specialized open-source models. Furthermore, our framework enhances interpretability by revealing hierarchical error patterns, demonstrating that LLMs often correctly identify the broader clinical context even when the precise diagnosis is missed.", "date": "2025-10-04", "categories": []}
{"id": "2510.03997", "title": "Mapping Patient-Perceived Physician Traits from Nationwide Online Reviews with LLMs", "abstract": "Understanding how patients perceive their physicians is essential to improving trust, communication, and satisfaction. We present a large language model (LLM)-based pipeline that infers Big Five personality traits and five patient-oriented subjective judgments. The analysis encompasses 4.1 million patient reviews of 226,999 U.S. physicians from an initial pool of one million. We validate the method through multi-model comparison and human expert benchmarking, achieving strong agreement between human and LLM assessments (correlation coefficients 0.72-0.89) and external validity through correlations with patient satisfaction (r = 0.41-0.81, all p<0.001). National-scale analysis reveals systematic patterns: male physicians receive higher ratings across all traits, with largest disparities in clinical competence perceptions; empathy-related traits predominate in pediatrics and psychiatry; and all traits positively predict overall satisfaction. Cluster analysis identifies four distinct physician archetypes, from \"Well-Rounded Excellent\" (33.8%, uniformly high traits) to \"Underperforming\" (22.6%, consistently low). These findings demonstrate that automated trait extraction from patient narratives can provide interpretable, validated metrics for understanding physician-patient relationships at scale, with implications for quality measurement, bias detection, and workforce development in healthcare.", "date": "2025-10-05", "categories": []}
{"id": "2510.04032", "title": "Small Language Models for Emergency Departments Decision Support: A Benchmark Study", "abstract": "Large language models (LLMs) have become increasingly popular in medical domains to assist physicians with a variety of clinical and operational tasks. Given the fast-paced and high-stakes environment of emergency departments (EDs), small language models (SLMs), characterized by a reduction in parameter count compared to LLMs, offer significant potential due to their inherent reasoning capability and efficient performance. This enables SLMs to support physicians by providing timely and accurate information synthesis, thereby improving clinical decision-making and workflow efficiency. In this paper, we present a comprehensive benchmark designed to identify SLMs suited for ED decision support, taking into account both specialized medical expertise and broad general problem-solving capabilities. In our evaluations, we focus on SLMs that have been trained on a mixture of general-domain and medical corpora. A key motivation for emphasizing SLMs is the practical hardware limitations, operational cost constraints, and privacy concerns in the typical real-world deployments. Our benchmark datasets include MedMCQA, MedQA-4Options, and PubMedQA, with the medical abstracts dataset emulating tasks aligned with real ED physicians' daily tasks. Experimental results reveal that general-domain SLMs surprisingly outperform their medically fine-tuned counterparts across these diverse benchmarks for ED. This indicates that for ED, specialized medical fine-tuning of the model may not be required.", "date": "2025-10-05", "categories": []}
{"id": "2510.04338", "title": "Evaluation of Clinical Trials Reporting Quality using Large Language Models", "abstract": "Reporting quality is an important topic in clinical trial research articles, as it can impact clinical decisions. In this article, we test the ability of large language models to assess the reporting quality of this type of article using the Consolidated Standards of Reporting Trials (CONSORT). We create CONSORT-QA, an evaluation corpus from two studies on abstract reporting quality with CONSORT-abstract standards. We then evaluate the ability of different large generative language models (from the general domain or adapted to the biomedical domain) to correctly assess CONSORT criteria with different known prompting methods, including Chain-of-thought. Our best combination of model and prompting method achieves 85% accuracy. Using Chain-of-thought adds valuable information on the model's reasoning for completing the task.", "date": "2025-10-05", "categories": []}
{"id": "2510.04623", "title": "MedPAO: A Protocol-Driven Agent for Structuring Medical Reports", "abstract": "The deployment of Large Language Models (LLMs) for structuring clinical data is critically hindered by their tendency to hallucinate facts and their inability to follow domain-specific rules. To address this, we introduce MedPAO, a novel agentic framework that ensures accuracy and verifiable reasoning by grounding its operation in established clinical protocols such as the ABCDEF protocol for CXR analysis. MedPAO decomposes the report structuring task into a transparent process managed by a Plan-Act-Observe (PAO) loop and specialized tools. This protocol-driven method provides a verifiable alternative to opaque, monolithic models. The efficacy of our approach is demonstrated through rigorous evaluation: MedPAO achieves an F1-score of 0.96 on the critical sub-task of concept categorization. Notably, expert radiologists and clinicians rated the final structured outputs with an average score of 4.52 out of 5, indicating a level of reliability that surpasses baseline approaches relying solely on LLM-based foundation models. The code is available at: https://github.com/MiRL-IITM/medpao-agent", "date": "2025-10-06", "categories": []}
{"id": "2510.02338", "title": "Optimizing Long-Form Clinical Text Generation with Claim-Based Rewards", "abstract": "Automating clinical documentation with large language models requires precise alignment with priorities such as completeness and factual grounding. We present an evaluation-integrated reinforcement learning framework for long-form clinical text generation that couples Group Relative Policy Optimization (GRPO) with DocLens, a claim-level evaluator that provides deterministic, dialogue-grounded rewards. Our method directly optimizes factual grounding and completeness without training a separate reward model or relying on human-authored references. Empirically, the approach improves clinical note quality and reduces training cost via a simple reward-gating strategy. An independent GPT-5 qualitative evaluation further supports these gains, showing higher preference for GRPO outputs in factuality, completeness, and brevity, with fewer omissions and hallucinations. Because the benchmarks are relatively clean and the base model already well aligned, these improvements likely represent a conservative lower bound. The framework is scalable to real-world settings and can incorporate custom objectives such as guideline adherence or billing preferences.", "date": "2025-09-26", "categories": []}
{"id": "2510.01224", "title": "Context Matters: Comparison of commercial large language tools in veterinary medicine", "abstract": "Large language models (LLMs) are increasingly used in clinical settings, yet their performance in veterinary medicine remains underexplored. We evaluated three commercially available veterinary-focused LLM summarization tools (Product 1 [Hachiko] and Products 2 and 3) on a standardized dataset of veterinary oncology records. Using a rubric-guided LLM-as-a-judge framework, summaries were scored across five domains: Factual Accuracy, Completeness, Chronological Order, Clinical Relevance, and Organization. Product 1 achieved the highest overall performance, with a median average score of 4.61 (IQR: 0.73), compared to 2.55 (IQR: 0.78) for Product 2 and 2.45 (IQR: 0.92) for Product 3. It also received perfect median scores in Factual Accuracy and Chronological Order. To assess the internal consistency of the grading framework itself, we repeated the evaluation across three independent runs. The LLM grader demonstrated high reproducibility, with Average Score standard deviations of 0.015 (Product 1), 0.088 (Product 2), and 0.034 (Product 3). These findings highlight the importance of veterinary-specific commercial LLM tools and demonstrate that LLM-as-a-judge evaluation is a scalable and reproducible method for assessing clinical NLP summarization in veterinary medicine.", "date": "2025-09-22", "categories": []}
{"id": "2510.01244", "title": "Feasibility of Structuring Stress Documentation Using an Ontology-Guided Large Language Model", "abstract": "Stress, arising from the dynamic interaction between external stressors, individual appraisals, and physiological or psychological responses, significantly impacts health yet is often underreported and inconsistently documented, typically captured as unstructured free-text in electronic health records. Ambient AI technologies offer promise in reducing documentation burden, but predominantly generate unstructured narratives, limiting downstream clinical utility.\n  This study aimed to develop an ontology for mental stress and evaluate the feasibility of using a Large Language Model (LLM) to extract ontology-guided stress-related information from narrative text. The Mental Stress Ontology (MeSO) was developed by integrating theoretical models like the Transactional Model of Stress with concepts from 11 validated stress assessment tools. MeSO's structure and content were refined using Ontology Pitfall Scanner! and expert validation.\n  Using MeSO, six categories of stress-related information--stressor, stress response, coping strategy, duration, onset, and temporal profile--were extracted from 35 Reddit posts using Claude Sonnet 4. Human reviewers evaluated accuracy and ontology coverage. The final ontology included 181 concepts across eight top-level classes. Of 220 extractable stress-related items, the LLM correctly identified 172 (78.2%), misclassified 27 (12.3%), and missed 21 (9.5%). All correctly extracted items were accurately mapped to MeSO, although 24 relevant concepts were not yet represented in the ontology.\n  This study demonstrates the feasibility of using an ontology-guided LLM for structured extraction of stress-related information, offering potential to enhance the consistency and utility of stress documentation in ambient AI systems. Future work should involve clinical dialogue data and comparison across LLMs.", "date": "2025-09-24", "categories": []}
{"id": "2510.01363", "title": "Retrieval-Augmented Framework for LLM-Based Clinical Decision Support", "abstract": "The increasing complexity of clinical decision-making, alongside the rapid expansion of electronic health records (EHR), presents both opportunities and challenges for delivering data-informed care. This paper proposes a clinical decision support system powered by Large Language Models (LLMs) to assist prescribing clinicians. The system generates therapeutic suggestions by analyzing historical EHR data, including patient demographics, presenting complaints, clinical symptoms, diagnostic information, and treatment histories. The framework integrates natural language processing with structured clinical inputs to produce contextually relevant recommendations. Rather than replacing clinician judgment, it is designed to augment decision-making by retrieving and synthesizing precedent cases with comparable characteristics, drawing on local datasets or federated sources where applicable. At its core, the system employs a retrieval-augmented generation (RAG) pipeline that harmonizes unstructured narratives and codified data to support LLM-based inference. We outline the system's technical components, including representation representation alignment and generation strategies. Preliminary evaluations, conducted with de-identified and synthetic clinical datasets, examine the clinical plausibility and consistency of the model's outputs. Early findings suggest that LLM-based tools may provide valuable decision support in prescribing workflows when appropriately constrained and rigorously validated. This work represents an initial step toward integration of generative AI into real-world clinical decision-making with an emphasis on transparency, safety, and alignment with established practices.", "date": "2025-10-01", "categories": []}
{"id": "2509.22041", "title": "Taxonomy of Comprehensive Safety for Clinical Agents", "abstract": "Safety is a paramount concern in clinical chatbot applications, where inaccurate or harmful responses can lead to serious consequences. Existing methods--such as guardrails and tool calling--often fall short in addressing the nuanced demands of the clinical domain. In this paper, we introduce TACOS (TAxonomy of COmprehensive Safety for Clinical Agents), a fine-grained, 21-class taxonomy that integrates safety filtering and tool selection into a single user intent classification step. TACOS is a taxonomy that can cover a wide spectrum of clinical and non-clinical queries, explicitly modeling varying safety thresholds and external tool dependencies. To validate our taxonomy, we curate a TACOS-annotated dataset and perform extensive experiments. Our results demonstrate the value of a new taxonomy specialized for clinical agent settings, and reveal useful insights about train data distribution and pretrained knowledge of base models.", "date": "2025-09-26", "categories": []}
{"id": "2510.00280", "title": "ReEvalMed: Rethinking Medical Report Evaluation by Aligning Metrics with Real-World Clinical Judgment", "abstract": "Automatically generated radiology reports often receive high scores from existing evaluation metrics but fail to earn clinicians' trust. This gap reveals fundamental flaws in how current metrics assess the quality of generated reports. We rethink the design and evaluation of these metrics and propose a clinically grounded Meta-Evaluation framework. We define clinically grounded criteria spanning clinical alignment and key metric capabilities, including discrimination, robustness, and monotonicity. Using a fine-grained dataset of ground truth and rewritten report pairs annotated with error types, clinical significance labels, and explanations, we systematically evaluate existing metrics and reveal their limitations in interpreting clinical semantics, such as failing to distinguish clinically significant errors, over-penalizing harmless variations, and lacking consistency across error severity levels. Our framework offers guidance for building more clinically reliable evaluation methods.", "date": "2025-09-30", "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": "2509.25992", "title": "MHINDR -- a DSM5 based mental health diagnosis and recommendation framework using LLM", "abstract": "Mental health forums offer valuable insights into psychological issues, stressors, and potential solutions. We propose MHINDR, a large language model (LLM) based framework integrated with DSM-5 criteria to analyze user-generated text, dignose mental health conditions, and generate personalized interventions and insights for mental health practitioners. Our approach emphasizes on the extraction of temporal information for accurate diagnosis and symptom progression tracking, together with psychological features to create comprehensive mental health summaries of users. The framework delivers scalable, customizable, and data-driven therapeutic recommendations, adaptable to diverse clinical contexts, patient needs, and workplace well-being programs.", "date": "2025-09-30", "categories": []}
{"id": "2508.15845", "title": "Coarse-to-Fine Personalized LLM Impressions for Streamlined Radiology Reports", "abstract": "The manual creation of the \"Impression\" section in radiology reports is a primary driver of radiologist burnout. To address this challenge, we propose a coarse-to-fine framework that leverages open-source large language models (LLMs) to automatically generate and personalize impressions from clinical findings. The system first produces a draft impression and then refines it using machine learning and reinforcement learning from human feedback (RLHF) to align with individual radiologists' styles while ensuring factual accuracy. We fine-tune LLaMA and Mistral models on a large dataset of reports from the University of Chicago Medicine. Our approach is designed to significantly reduce administrative workload and improve reporting efficiency while maintaining high standards of clinical precision.", "date": "2025-08-19", "categories": []}
{"id": "2509.14597", "title": "Position: Thematic Analysis of Unstructured Clinical Transcripts with Large Language Models", "abstract": "This position paper examines how large language models (LLMs) can support thematic analysis of unstructured clinical transcripts, a widely used but resource-intensive method for uncovering patterns in patient and provider narratives. We conducted a systematic review of recent studies applying LLMs to thematic analysis, complemented by an interview with a practicing clinician. Our findings reveal that current approaches remain fragmented across multiple dimensions including types of thematic analysis, datasets, prompting strategies and models used, most notably in evaluation. Existing evaluation methods vary widely (from qualitative expert review to automatic similarity metrics), hindering progress and preventing meaningful benchmarking across studies. We argue that establishing standardized evaluation practices is critical for advancing the field. To this end, we propose an evaluation framework centered on three dimensions: validity, reliability, and interpretability.", "date": "2025-09-18", "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": "2502.21263", "title": "RuCCoD: Towards Automated ICD Coding in Russian", "abstract": "This study investigates the feasibility of automating clinical coding in Russian, a language with limited biomedical resources. We present a new dataset for ICD coding, which includes diagnosis fields from electronic health records (EHRs) annotated with over 10,000 entities and more than 1,500 unique ICD codes. This dataset serves as a benchmark for several state-of-the-art models, including BERT, LLaMA with LoRA, and RAG, with additional experiments examining transfer learning across domains (from PubMed abstracts to medical diagnosis) and terminologies (from UMLS concepts to ICD codes). We then apply the best-performing model to label an in-house EHR dataset containing patient histories from 2017 to 2021. Our experiments, conducted on a carefully curated test set, demonstrate that training with the automated predicted codes leads to a significant improvement in accuracy compared to manually annotated data from physicians. We believe our findings offer valuable insights into the potential for automating clinical coding in resource-limited languages like Russian, which could enhance clinical efficiency and data accuracy in these contexts. Our code and dataset are available at https://github.com/auto-icd-coding/ruccod.", "date": "2025-02-28", "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": "2509.19224", "title": "Systematic Comparative Analysis of Large Pretrained Language Models on Contextualized Medication Event Extraction", "abstract": "Attention-based models have become the leading approach in modeling medical language for Natural Language Processing (NLP) in clinical notes. These models outperform traditional techniques by effectively capturing contextual representations of language. In this research a comparative analysis is done amongst pre-trained attention based models namely Bert Base, BioBert, two variations of Bio+Clinical Bert, RoBerta, and Clinical Longformer on task related to Electronic Health Record (EHR) information extraction. The tasks from Track 1 of Harvard Medical School's 2022 National Clinical NLP Challenges (n2c2) are considered for this comparison, with the Contextualized Medication Event Dataset (CMED) given for these task. CMED is a dataset of unstructured EHRs and annotated notes that contain task relevant information about the EHRs. The goal of the challenge is to develop effective solutions for extracting contextual information related to patient medication events from EHRs using data driven methods. Each pre-trained model is fine-tuned and applied on CMED to perform medication extraction, medical event detection, and multi-dimensional medication event context classification. Processing methods are also detailed for breaking down EHRs for compatibility with the applied models. Performance analysis has been carried out using a script based on constructing medical terms from the evaluation portion of CMED with metrics including recall, precision, and F1-Score. The results demonstrate that models pre-trained on clinical data are more effective in detecting medication and medication events, but Bert Base, pre-trained on general domain data showed to be the most effective for classifying the context of events related to medications.", "date": "2025-09-23", "categories": []}
{"id": "2509.20567", "title": "SwasthLLM: a Unified Cross-Lingual, Multi-Task, and Meta-Learning Zero-Shot Framework for Medical Diagnosis Using Contrastive Representations", "abstract": "In multilingual healthcare environments, automatic disease diagnosis from clinical text remains a challenging task due to the scarcity of annotated medical data in low-resource languages and the linguistic variability across populations. This paper proposes SwasthLLM, a unified, zero-shot, cross-lingual, and multi-task learning framework for medical diagnosis that operates effectively across English, Hindi, and Bengali without requiring language-specific fine-tuning. At its core, SwasthLLM leverages the multilingual XLM-RoBERTa encoder augmented with a language-aware attention mechanism and a disease classification head, enabling the model to extract medically relevant information regardless of the language structure. To align semantic representations across languages, a Siamese contrastive learning module is introduced, ensuring that equivalent medical texts in different languages produce similar embeddings. Further, a translation consistency module and a contrastive projection head reinforce language-invariant representation learning. SwasthLLM is trained using a multi-task learning strategy, jointly optimizing disease classification, translation alignment, and contrastive learning objectives. Additionally, we employ Model-Agnostic Meta-Learning (MAML) to equip the model with rapid adaptation capabilities for unseen languages or tasks with minimal data. Our phased training pipeline emphasizes robust representation alignment before task-specific fine-tuning. Extensive evaluation shows that SwasthLLM achieves high diagnostic performance, with a test accuracy of 97.22% and an F1-score of 97.17% in supervised settings. Crucially, in zero-shot scenarios, it attains 92.78% accuracy on Hindi and 73.33% accuracy on Bengali medical text, demonstrating strong generalization in low-resource contexts.", "date": "2025-09-24", "categories": []}
{"id": "2409.09324", "title": "Efficient Fine-Tuning of Large Language Models for Automated Medical Documentation", "abstract": "Scientific research indicates that for every hour spent in direct patient care, physicians spend nearly two additional hours on administrative tasks, particularly on electronic health records (EHRs) and desk work. This excessive administrative burden not only reduces the time available for patient care but also contributes to physician burnout and inefficiencies in healthcare delivery. To address these challenges, this study introduces MediGen, a fine-tuned large language model (LLM) designed to automate the generation of medical reports from medical dialogues. By leveraging state-of-the-art methodologies for fine-tuning open-source pretrained models, including LLaMA3-8B, MediGen achieves high accuracy in transcribing and summarizing clinical interactions. The fine-tuned LLaMA3-8B model demonstrated promising results, achieving a ROUGE score of 58% and a BERTScore-F1 of 72%, indicating its effectiveness in generating accurate and clinically relevant medical reports. These findings suggest that MediGen has the potential to significantly reduce the administrative workload on physicians, improving both healthcare efficiency and physician well-being.", "date": "2024-09-14", "categories": []}
{"id": "2410.01841", "title": "A GEN AI Framework for Medical Note Generation", "abstract": "The increasing administrative burden of medical documentation, particularly through Electronic Health Records (EHR), significantly reduces the time available for direct patient care and contributes to physician burnout. To address this issue, we propose MediNotes, an advanced generative AI framework designed to automate the creation of SOAP (Subjective, Objective, Assessment, Plan) notes from medical conversations. MediNotes integrates Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and Automatic Speech Recognition (ASR) to capture and process both text and voice inputs in real time or from recorded audio, generating structured and contextually accurate medical notes. The framework also incorporates advanced techniques like Quantized Low-Rank Adaptation (QLoRA) and Parameter-Efficient Fine-Tuning (PEFT) for efficient model fine-tuning in resource-constrained environments. Additionally, MediNotes offers a query-based retrieval system, allowing healthcare providers and patients to access relevant medical information quickly and accurately. Evaluations using the ACI-BENCH dataset demonstrate that MediNotes significantly improves the accuracy, efficiency, and usability of automated medical documentation, offering a robust solution to reduce the administrative burden on healthcare professionals while improving the quality of clinical workflows.", "date": "2024-09-27", "categories": []}
{"id": "2509.18439", "title": "Developing an AI framework to automatically detect shared decision-making in patient-doctor conversations", "abstract": "Shared decision-making (SDM) is necessary to achieve patient-centred care. Currently no methodology exists to automatically measure SDM at scale. This study aimed to develop an automated approach to measure SDM by using language modelling and the conversational alignment (CA) score. A total of 157 video-recorded patient-doctor conversations from a randomized multi-centre trial evaluating SDM decision aids for anticoagulation in atrial fibrillations were transcribed and segmented into 42,559 sentences. Context-response pairs and negative sampling were employed to train deep learning (DL) models and fine-tuned BERT models via the next sentence prediction (NSP) task. Each top-performing model was used to calculate four types of CA scores. A random-effects analysis by clinician, adjusting for age, sex, race, and trial arm, assessed the association between CA scores and SDM outcomes: the Decisional Conflict Scale (DCS) and the Observing Patient Involvement in Decision-Making 12 (OPTION12) scores. p-values were corrected for multiple comparisons with the Benjamini-Hochberg method. Among 157 patients (34% female, mean age 70 SD 10.8), clinicians on average spoke more words than patients (1911 vs 773). The DL model without the stylebook strategy achieved a recall@1 of 0.227, while the fine-tuned BERTbase (110M) achieved the highest recall@1 with 0.640. The AbsMax (18.36 SE7.74 p=0.025) and Max CA (21.02 SE7.63 p=0.012) scores generated with the DL without stylebook were associated with OPTION12. The Max CA score generated with the fine-tuned BERTbase (110M) was associated with the DCS score (-27.61 SE12.63 p=0.037). BERT model sizes did not have an impact the association between CA scores and SDM. This study introduces an automated, scalable methodology to measure SDM in patient-doctor conversations through explainable CA scores, with potential to evaluate SDM strategies at scale.", "date": "2025-09-22", "categories": []}
{"id": "2504.21800", "title": "How Real Are Synthetic Therapy Conversations? Evaluating Fidelity in Prolonged Exposure Dialogues", "abstract": "Synthetic data adoption in healthcare is driven by privacy concerns, data access limitations, and high annotation costs. We explore synthetic Prolonged Exposure (PE) therapy conversations for PTSD as a scalable alternative for training clinical models. We systematically compare real and synthetic dialogues using linguistic, structural, and protocol-specific metrics like turn-taking and treatment fidelity. We introduce and evaluate PE-specific metrics, offering a novel framework for assessing clinical fidelity beyond surface fluency. Our findings show that while synthetic data successfully mitigates data scarcity and protects privacy, capturing the most subtle therapeutic dynamics remains a complex challenge. Synthetic dialogues successfully replicate key linguistic features of real conversations, for instance, achieving a similar Readability Score (89.2 vs. 88.1), while showing differences in some key fidelity markers like distress monitoring. This comparison highlights the need for fidelity-aware metrics that go beyond surface fluency to identify clinically significant nuances. Our model-agnostic framework is a critical tool for developers and clinicians to benchmark generative model fidelity before deployment in sensitive applications. Our findings help clarify where synthetic data can effectively complement real-world datasets, while also identifying areas for future refinement.", "date": "2025-04-30", "categories": []}
{"id": "2509.16326", "title": "HARE: an entity and relation centric evaluation framework for histopathology reports", "abstract": "Medical domain automated text generation is an active area of research and development; however, evaluating the clinical quality of generated reports remains a challenge, especially in instances where domain-specific metrics are lacking, e.g. histopathology. We propose HARE (Histopathology Automated Report Evaluation), a novel entity and relation centric framework, composed of a benchmark dataset, a named entity recognition (NER) model, a relation extraction (RE) model, and a novel metric, which prioritizes clinically relevant content by aligning critical histopathology entities and relations between reference and generated reports. To develop the HARE benchmark, we annotated 813 de-identified clinical diagnostic histopathology reports and 652 histopathology reports from The Cancer Genome Atlas (TCGA) with domain-specific entities and relations. We fine-tuned GatorTronS, a domain-adapted language model to develop HARE-NER and HARE-RE which achieved the highest overall F1-score (0.915) among the tested models. The proposed HARE metric outperformed traditional metrics including ROUGE and Meteor, as well as radiology metrics such as RadGraph-XL, with the highest correlation and the best regression to expert evaluations (higher than the second best method, GREEN, a large language model based radiology report evaluator, by Pearson $r = 0.168$, Spearman $\\rho = 0.161$, Kendall $\\tau = 0.123$, $R^2 = 0.176$, $RMSE = 0.018$). We release HARE, datasets, and the models at https://github.com/knowlab/HARE to foster advancements in histopathology report generation, providing a robust framework for improving the quality of reports.", "date": "2025-09-19", "categories": []}
{"id": "2505.16325", "title": "CLEAR: A Clinically-Grounded Tabular Framework for Radiology Report Evaluation", "abstract": "Existing metrics often lack the granularity and interpretability to capture nuanced clinical differences between candidate and ground-truth radiology reports, resulting in suboptimal evaluation. We introduce a Clinically-grounded tabular framework with Expert-curated labels and Attribute-level comparison for Radiology report evaluation (CLEAR). CLEAR not only examines whether a report can accurately identify the presence or absence of medical conditions, but also assesses whether it can precisely describe each positively identified condition across five key attributes: first occurrence, change, severity, descriptive location, and recommendation. Compared to prior works, CLEAR's multi-dimensional, attribute-level outputs enable a more comprehensive and clinically interpretable evaluation of report quality. Additionally, to measure the clinical alignment of CLEAR, we collaborate with five board-certified radiologists to develop CLEAR-Bench, a dataset of 100 chest X-ray reports from MIMIC-CXR, annotated across 6 curated attributes and 13 CheXpert conditions. Our experiments show that CLEAR achieves high accuracy in extracting clinical attributes and provides automated metrics that are strongly aligned with clinical judgment.", "date": "2025-05-22", "categories": []}
{"id": "2509.15419", "title": "Deep learning and abstractive summarisation for radiological reports: an empirical study for adapting the PEGASUS models' family with scarce data", "abstract": "Regardless of the rapid development of artificial intelligence, abstractive summarisation is still challenging for sensitive and data-restrictive domains like medicine. With the increasing number of imaging, the relevance of automated tools for complex medical text summarisation is expected to become highly relevant. In this paper, we investigated the adaptation via fine-tuning process of a non-domain-specific abstractive summarisation encoder-decoder model family, and gave insights to practitioners on how to avoid over- and underfitting. We used PEGASUS and PEGASUS-X, on a medium-sized radiological reports public dataset. For each model, we comprehensively evaluated two different checkpoints with varying sizes of the same training data. We monitored the models' performances with lexical and semantic metrics during the training history on the fixed-size validation set. PEGASUS exhibited different phases, which can be related to epoch-wise double-descent, or peak-drop-recovery behaviour. For PEGASUS-X, we found that using a larger checkpoint led to a performance detriment. This work highlights the challenges and risks of fine-tuning models with high expressivity when dealing with scarce training data, and lays the groundwork for future investigations into more robust fine-tuning strategies for summarisation models in specialised domains.", "date": "2025-09-18", "categories": []}
{"id": "2509.15957", "title": "EHR-MCP: Real-world Evaluation of Clinical Information Retrieval by Large Language Models via Model Context Protocol", "abstract": "Background: Large language models (LLMs) show promise in medicine, but their deployment in hospitals is limited by restricted access to electronic health record (EHR) systems. The Model Context Protocol (MCP) enables integration between LLMs and external tools.\n  Objective: To evaluate whether an LLM connected to an EHR database via MCP can autonomously retrieve clinically relevant information in a real hospital setting.\n  Methods: We developed EHR-MCP, a framework of custom MCP tools integrated with the hospital EHR database, and used GPT-4.1 through a LangGraph ReAct agent to interact with it. Six tasks were tested, derived from use cases of the infection control team (ICT). Eight patients discussed at ICT conferences were retrospectively analyzed. Agreement with physician-generated gold standards was measured.\n  Results: The LLM consistently selected and executed the correct MCP tools. Except for two tasks, all tasks achieved near-perfect accuracy. Performance was lower in the complex task requiring time-dependent calculations. Most errors arose from incorrect arguments or misinterpretation of tool results. Responses from EHR-MCP were reliable, though long and repetitive data risked exceeding the context window.\n  Conclusions: LLMs can retrieve clinical data from an EHR via MCP tools in a real hospital setting, achieving near-perfect performance in simple tasks while highlighting challenges in complex ones. EHR-MCP provides an infrastructure for secure, consistent data access and may serve as a foundation for hospital AI agents. Future work should extend beyond retrieval to reasoning, generation, and clinical impact assessment, paving the way for effective integration of generative AI into clinical practice.", "date": "2025-09-19", "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": "2509.14464", "title": "Not What the Doctor Ordered: Surveying LLM-based De-identification and Quantifying Clinical Information Loss", "abstract": "De-identification in the healthcare setting is an application of NLP where automated algorithms are used to remove personally identifying information of patients (and, sometimes, providers). With the recent rise of generative large language models (LLMs), there has been a corresponding rise in the number of papers that apply LLMs to de-identification. Although these approaches often report near-perfect results, significant challenges concerning reproducibility and utility of the research papers persist. This paper identifies three key limitations in the current literature: inconsistent reporting metrics hindering direct comparisons, the inadequacy of traditional classification metrics in capturing errors which LLMs may be more prone to (i.e., altering clinically relevant information), and lack of manual validation of automated metrics which aim to quantify these errors. To address these issues, we first present a survey of LLM-based de-identification research, highlighting the heterogeneity in reporting standards. Second, we evaluated a diverse set of models to quantify the extent of inappropriate removal of clinical information. Next, we conduct a manual validation of an existing evaluation metric to measure the removal of clinical information, employing clinical experts to assess their efficacy. We highlight poor performance and describe the inherent limitations of such metrics in identifying clinically significant changes. Lastly, we propose a novel methodology for the detection of clinically relevant information removal.", "date": "2025-09-17", "categories": []}
{"id": "2509.14797", "title": "SINAI at eRisk@CLEF 2023: Approaching Early Detection of Gambling with Natural Language Processing", "abstract": "This paper describes the participation of the SINAI team in the eRisk@CLEF lab. Specifically, one of the proposed tasks has been addressed: Task 2 on the early detection of signs of pathological gambling. The approach presented in Task 2 is based on pre-trained models from Transformers architecture with comprehensive preprocessing data and data balancing techniques. Moreover, we integrate Long-short Term Memory (LSTM) architecture with automodels from Transformers. In this Task, our team has been ranked in seventh position, with an F1 score of 0.126, out of 49 participant submissions and achieves the highest values in recall metrics and metrics related to early detection.", "date": "2025-09-18", "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": "2509.13706", "title": "Automated Triaging and Transfer Learning of Incident Learning Safety Reports Using Large Language Representational Models", "abstract": "PURPOSE: Incident reports are an important tool for safety and quality improvement in healthcare, but manual review is time-consuming and requires subject matter expertise. Here we present a natural language processing (NLP) screening tool to detect high-severity incident reports in radiation oncology across two institutions.\n  METHODS AND MATERIALS: We used two text datasets to train and evaluate our NLP models: 7,094 reports from our institution (Inst.), and 571 from IAEA SAFRON (SF), all of which had severity scores labeled by clinical content experts. We trained and evaluated two types of models: baseline support vector machines (SVM) and BlueBERT which is a large language model pretrained on PubMed abstracts and hospitalized patient data. We assessed for generalizability of our model in two ways. First, we evaluated models trained using Inst.-train on SF-test. Second, we trained a BlueBERT_TRANSFER model that was first fine-tuned on Inst.-train then on SF-train before testing on SF-test set. To further analyze model performance, we also examined a subset of 59 reports from our Inst. dataset, which were manually edited for clarity.\n  RESULTS Classification performance on the Inst. test achieved AUROC 0.82 using SVM and 0.81 using BlueBERT. Without cross-institution transfer learning, performance on the SF test was limited to an AUROC of 0.42 using SVM and 0.56 using BlueBERT. BlueBERT_TRANSFER, which was fine-tuned on both datasets, improved the performance on SF test to AUROC 0.78. Performance of SVM, and BlueBERT_TRANSFER models on the manually curated Inst. reports (AUROC 0.85 and 0.74) was similar to human performance (AUROC 0.81).\n  CONCLUSION: In summary, we successfully developed cross-institution NLP models on incident report text from radiation oncology centers. These models were able to detect high-severity reports similarly to humans on a curated dataset.", "date": "2025-09-17", "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": "2509.10843", "title": "Evaluating Large Language Models for Evidence-Based Clinical Question Answering", "abstract": "Large Language Models (LLMs) have demonstrated substantial progress in biomedical and clinical applications, motivating rigorous evaluation of their ability to answer nuanced, evidence-based questions. We curate a multi-source benchmark drawing from Cochrane systematic reviews and clinical guidelines, including structured recommendations from the American Heart Association and narrative guidance used by insurers. Using GPT-4o-mini and GPT-5, we observe consistent performance patterns across sources and clinical domains: accuracy is highest on structured guideline recommendations (90%) and lower on narrative guideline and systematic review questions (60--70%). We also find a strong correlation between accuracy and the citation count of the underlying systematic reviews, where each doubling of citations is associated with roughly a 30% increase in the odds of a correct answer. Models show moderate ability to reason about evidence quality when contextual information is supplied. When we incorporate retrieval-augmented prompting, providing the gold-source abstract raises accuracy on previously incorrect items to 0.79; providing top 3 PubMed abstracts (ranked by semantic relevance) improves accuracy to 0.23, while random abstracts reduce accuracy (0.10, within temperature variation). These effects are mirrored in GPT-4o-mini, underscoring that source clarity and targeted retrieval -- not just model size -- drive performance. Overall, our results highlight both the promise and current limitations of LLMs for evidence-based clinical question answering. Retrieval-augmented prompting emerges as a useful strategy to improve factual accuracy and alignment with source evidence, while stratified evaluation by specialty and question type remains essential to understand current knowledge access and to contextualize model performance.", "date": "2025-09-13", "categories": []}
{"id": "2509.10882", "title": "Term2Note: Synthesising Differentially Private Clinical Notes from Medical Terms", "abstract": "Training data is fundamental to the success of modern machine learning models, yet in high-stakes domains such as healthcare, the use of real-world training data is severely constrained by concerns over privacy leakage. A promising solution to this challenge is the use of differentially private (DP) synthetic data, which offers formal privacy guarantees while maintaining data utility. However, striking the right balance between privacy protection and utility remains challenging in clinical note synthesis, given its domain specificity and the complexity of long-form text generation. In this paper, we present Term2Note, a methodology to synthesise long clinical notes under strong DP constraints. By structurally separating content and form, Term2Note generates section-wise note content conditioned on DP medical terms, with each governed by separate DP constraints. A DP quality maximiser further enhances synthetic notes by selecting high-quality outputs. Experimental results show that Term2Note produces synthetic notes with statistical properties closely aligned with real clinical notes, demonstrating strong fidelity. In addition, multi-label classification models trained on these synthetic notes perform comparably to those trained on real data, confirming their high utility. Compared to existing DP text generation baselines, Term2Note achieves substantial improvements in both fidelity and utility while operating under fewer assumptions, suggesting its potential as a viable privacy-preserving alternative to using sensitive clinical notes.", "date": "2025-09-13", "categories": []}
{"id": "2509.11802", "title": "When Curiosity Signals Danger: Predicting Health Crises Through Online Medication Inquiries", "abstract": "Online medical forums are a rich and underutilized source of insight into patient concerns, especially regarding medication use. Some of the many questions users pose may signal confusion, misuse, or even the early warning signs of a developing health crisis. Detecting these critical questions that may precede severe adverse events or life-threatening complications is vital for timely intervention and improving patient safety. This study introduces a novel annotated dataset of medication-related questions extracted from online forums. Each entry is manually labelled for criticality based on clinical risk factors. We benchmark the performance of six traditional machine learning classifiers using TF-IDF textual representations, alongside three state-of-the-art large language model (LLM)-based classification approaches that leverage deep contextual understanding. Our results highlight the potential of classical and modern methods to support real-time triage and alert systems in digital health spaces. The curated dataset is made publicly available to encourage further research at the intersection of patient-generated data, natural language processing, and early warning systems for critical health events. The dataset and benchmark are available at: https://github.com/Dvora-coder/LLM-Medication-QA-Risk-Classifier-MediGuard.", "date": "2025-09-15", "categories": []}
{"id": "2509.11803", "title": "From Fuzzy Speech to Medical Insight: Benchmarking LLMs on Noisy Patient Narratives", "abstract": "The widespread adoption of large language models (LLMs) in healthcare raises critical questions about their ability to interpret patient-generated narratives, which are often informal, ambiguous, and noisy. Existing benchmarks typically rely on clean, structured clinical text, offering limited insight into model performance under realistic conditions. In this work, we present a novel synthetic dataset designed to simulate patient self-descriptions characterized by varying levels of linguistic noise, fuzzy language, and layperson terminology. Our dataset comprises clinically consistent scenarios annotated with ground-truth diagnoses, spanning a spectrum of communication clarity to reflect diverse real-world reporting styles. Using this benchmark, we fine-tune and evaluate several state-of-the-art models (LLMs), including BERT-based and encoder-decoder T5 models. To support reproducibility and future research, we release the Noisy Diagnostic Benchmark (NDB), a structured dataset of noisy, synthetic patient descriptions designed to stress-test and compare the diagnostic capabilities of large language models (LLMs) under realistic linguistic conditions. We made the benchmark available for the community: https://github.com/lielsheri/PatientSignal", "date": "2025-09-15", "categories": []}
{"id": "2506.09279", "title": "A Topic Modeling Analysis of Stigma Dimensions, Social, and Related Behavioral Circumstances in Clinical Notes Among Patients with HIV", "abstract": "Objective: To characterize stigma dimensions, social, and related behavioral circumstances in people living with HIV(PLWHs) seeking care, using NLP methods applied to a large collection of EHR clinical notes from a large integrated health system in the southeast United States. Methods: We identified a cohort of PLWHs from the UF Health IDR and performed topic modeling analysis using Latent Dirichlet Allocation to uncover stigma-related dimensions and related social and behavioral contexts. Domain experts created a seed list of HIV-related stigma keywords, then applied a snowball strategy to review notes for additional terms until saturation was reached iteratively. To identify more target topics, we tested three keyword-based filtering strategies. The detected topics were evaluated using three widely used metrics and manually reviewed by specialists. In addition, we conducted word frequency analysis and topic variation analysis among subgroups to examine differences across age and sex-specific demographics. Results: We identified 9140 PLWHs at UF Health and collected 2.9 million clinical notes. Through the iterative keyword approach, we generated a list of 91 keywords associated with HIV-related stigma. Topic modeling on sentences containing at least one keyword uncovered a wide range of topic themes, such as \"Mental Health Concern, Stigma\", \"Treatment Refusal, Isolation\", and \"Substance Abuse\". Topic variation analysis across age subgroups revealed substantial differences. Conclusion: Extracting and understanding the HIV-related stigma and associated social and behavioral circumstances from EHR clinical notes enables scalable, time-efficient assessment and overcoming the limitations of traditional questionnaires. Findings from this research provide actionable insights to inform patient care and interventions to improve HIV-care outcomes.", "date": "2025-06-10", "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": "2509.10108", "title": "Scaling Arabic Medical Chatbots Using Synthetic Data: Enhancing Generative AI with Synthetic Patient Records", "abstract": "The development of medical chatbots in Arabic is significantly constrained by the scarcity of large-scale, high-quality annotated datasets. While prior efforts compiled a dataset of 20,000 Arabic patient-doctor interactions from social media to fine-tune large language models (LLMs), model scalability and generalization remained limited. In this study, we propose a scalable synthetic data augmentation strategy to expand the training corpus to 100,000 records. Using advanced generative AI systems ChatGPT-4o and Gemini 2.5 Pro we generated 80,000 contextually relevant and medically coherent synthetic question-answer pairs grounded in the structure of the original dataset. These synthetic samples were semantically filtered, manually validated, and integrated into the training pipeline. We fine-tuned five LLMs, including Mistral-7B and AraGPT2, and evaluated their performance using BERTScore metrics and expert-driven qualitative assessments. To further analyze the effectiveness of synthetic sources, we conducted an ablation study comparing ChatGPT-4o and Gemini-generated data independently. The results showed that ChatGPT-4o data consistently led to higher F1-scores and fewer hallucinations across all models. Overall, our findings demonstrate the viability of synthetic augmentation as a practical solution for enhancing domain-specific language models in-low resource medical NLP, paving the way for more inclusive, scalable, and accurate Arabic healthcare chatbot systems.", "date": "2025-09-12", "categories": []}
{"id": "2509.06602", "title": "Demo: Healthcare Agent Orchestrator (HAO) for Patient Summarization in Molecular Tumor Boards", "abstract": "Molecular Tumor Boards (MTBs) are multidisciplinary forums where oncology specialists collaboratively assess complex patient cases to determine optimal treatment strategies. A central element of this process is the patient summary, typically compiled by a medical oncologist, radiation oncologist, or surgeon, or their trained medical assistant, who distills heterogeneous medical records into a concise narrative to facilitate discussion. This manual approach is often labor-intensive, subjective, and prone to omissions of critical information. To address these limitations, we introduce the Healthcare Agent Orchestrator (HAO), a Large Language Model (LLM)-driven AI agent that coordinates a multi-agent clinical workflow to generate accurate and comprehensive patient summaries for MTBs. Evaluating predicted patient summaries against ground truth presents additional challenges due to stylistic variation, ordering, synonym usage, and phrasing differences, which complicate the measurement of both succinctness and completeness. To overcome these evaluation hurdles, we propose TBFact, a ``model-as-a-judge'' framework designed to assess the comprehensiveness and succinctness of generated summaries. Using a benchmark dataset derived from de-identified tumor board discussions, we applied TBFact to evaluate our Patient History agent. Results show that the agent captured 94% of high-importance information (including partial entailments) and achieved a TBFact recall of 0.84 under strict entailment criteria. We further demonstrate that TBFact enables a data-free evaluation framework that institutions can deploy locally without sharing sensitive clinical data. Together, HAO and TBFact establish a robust foundation for delivering reliable and scalable support to MTBs.", "date": "2025-09-08", "categories": []}
{"id": "2509.09602", "title": "LAVA: Language Model Assisted Verbal Autopsy for Cause-of-Death Determination", "abstract": "Verbal autopsy (VA) is a critical tool for estimating causes of death in resource-limited settings where medical certification is unavailable. This study presents LA-VA, a proof-of-concept pipeline that combines Large Language Models (LLMs) with traditional algorithmic approaches and embedding-based classification for improved cause-of-death prediction. Using the Population Health Metrics Research Consortium (PHMRC) dataset across three age categories (Adult: 7,580; Child: 1,960; Neonate: 2,438), we evaluate multiple approaches: GPT-5 predictions, LCVA baseline, text embeddings, and meta-learner ensembles. Our results demonstrate that GPT-5 achieves the highest individual performance with average test site accuracies of 48.6% (Adult), 50.5% (Child), and 53.5% (Neonate), outperforming traditional statistical machine learning baselines by 5-10%. Our findings suggest that simple off-the-shelf LLM-assisted approaches could substantially improve verbal autopsy accuracy, with important implications for global health surveillance in low-resource settings.", "date": "2025-09-11", "categories": []}
{"id": "2509.07462", "title": "Understanding Stigmatizing Language Lexicons: A Comparative Analysis in Clinical Contexts", "abstract": "Stigmatizing language results in healthcare inequities, yet there is no universally accepted or standardized lexicon defining which words, terms, or phrases constitute stigmatizing language in healthcare. We conducted a systematic search of the literature to identify existing stigmatizing language lexicons and then analyzed them comparatively to examine: 1) similarities and discrepancies between these lexicons, and 2) the distribution of positive, negative, or neutral terms based on an established sentiment dataset. Our search identified four lexicons. The analysis results revealed moderate semantic similarity among them, and that most stigmatizing terms are related to judgmental expressions by clinicians to describe perceived negative behaviors. Sentiment analysis showed a predominant proportion of negatively classified terms, though variations exist across lexicons. Our findings underscore the need for a standardized lexicon and highlight challenges in defining stigmatizing language in clinical texts.", "date": "2025-09-09", "categories": []}
{"id": "2509.07622", "title": "MaLei at MultiClinSUM: Summarisation of Clinical Documents using Perspective-Aware Iterative Self-Prompting with LLMs", "abstract": "Efficient communication between patients and clinicians plays an important role in shared decision-making. However, clinical reports are often lengthy and filled with clinical jargon, making it difficult for domain experts to identify important aspects in the document efficiently. This paper presents the methodology we applied in the MultiClinSUM shared task for summarising clinical case documents. We used an Iterative Self-Prompting technique on large language models (LLMs) by asking LLMs to generate task-specific prompts and refine them via example-based few-shot learning. Furthermore, we used lexical and embedding space metrics, ROUGE and BERT-score, to guide the model fine-tuning with epochs. Our submission using perspective-aware ISP on GPT-4 and GPT-4o achieved ROUGE scores (46.53, 24.68, 30.77) and BERTscores (87.84, 83.25, 85.46) for (P, R, F1) from the official evaluation on 3,396 clinical case reports from various specialties extracted from open journals. The high BERTscore indicates that the model produced semantically equivalent output summaries compared to the references, even though the overlap at the exact lexicon level is lower, as reflected in the lower ROUGE scores. This work sheds some light on how perspective-aware ISP (PA-ISP) can be deployed for clinical report summarisation and support better communication between patients and clinicians.", "date": "2025-09-09", "categories": []}
{"id": "2406.06056", "title": "Synth-SBDH: A Synthetic Dataset of Social and Behavioral Determinants of Health for Clinical Text", "abstract": "Social and behavioral determinants of health (SBDH) play a crucial role in health outcomes and are frequently documented in clinical text. Automatically extracting SBDH information from clinical text relies on publicly available good-quality datasets. However, existing SBDH datasets exhibit substantial limitations in their availability and coverage. In this study, we introduce Synth-SBDH, a novel synthetic dataset with detailed SBDH annotations, encompassing status, temporal information, and rationale across 15 SBDH categories. We showcase the utility of Synth-SBDH on three tasks using real-world clinical datasets from two distinct hospital settings, highlighting its versatility, generalizability, and distillation capabilities. Models trained on Synth-SBDH consistently outperform counterparts with no Synth-SBDH training, achieving up to 63.75% macro-F improvements. Additionally, Synth-SBDH proves effective for rare SBDH categories and under-resource constraints while being substantially cheaper than expert-annotated real-world data. Human evaluation reveals a 71.06% Human-LLM alignment and uncovers areas for future refinements.", "date": "2024-06-10", "categories": []}
{"id": "2509.05484", "title": "From Staff Messages to Actionable Insights: A Multi-Stage LLM Classification Framework for Healthcare Analytics", "abstract": "Hospital call centers serve as the primary contact point for patients within a hospital system. They also generate substantial volumes of staff messages as navigators process patient requests and communicate with the hospital offices following the established protocol restrictions and guidelines. This continuously accumulated large amount of text data can be mined and processed to retrieve insights; however, traditional supervised learning approaches require annotated data, extensive training, and model tuning. Large Language Models (LLMs) offer a paradigm shift toward more computationally efficient methodologies for healthcare analytics. This paper presents a multi-stage LLM-based framework that identifies staff message topics and classifies messages by their reasons in a multi-class fashion. In the process, multiple LLM types, including reasoning, general-purpose, and lightweight models, were evaluated. The best-performing model was o3, achieving 78.4% weighted F1-score and 79.2% accuracy, followed closely by gpt-5 (75.3% Weighted F1-score and 76.2% accuracy). The proposed methodology incorporates data security measures and HIPAA compliance requirements essential for healthcare environments. The processed LLM outputs are integrated into a visualization decision support tool that transforms the staff messages into actionable insights accessible to healthcare professionals. This approach enables more efficient utilization of the collected staff messaging data, identifies navigator training opportunities, and supports improved patient experience and care quality.", "date": "2025-09-05", "categories": []}
{"id": "2509.06169", "title": "Reasoning Language Model for Personalized Lung Cancer Screening", "abstract": "Accurate risk assessment in lung cancer screening is critical for enabling early cancer detection and minimizing unnecessary invasive procedures. The Lung CT Screening Reporting and Data System (Lung-RADS) has been widely used as the standard framework for patient management and follow-up. Nevertheless, Lung-RADS faces trade-offs between sensitivity and specificity, as it stratifies risk solely based on lung nodule characteristics without incorporating various risk factors. Here we propose a reasoning language model (RLM) to integrate radiology findings with longitudinal medical records for individualized lung cancer risk assessment. Through a systematic study including dataset construction and distillation, supervised fine-tuning, reinforcement learning, and comprehensive evaluation, our model makes significant improvements in risk prediction performance on datasets in the national lung screening trial. Notably, RLM can decompose the risk evaluation task into sub-components, analyze the contributions of diverse risk factors, and synthesize them into a final risk score computed using our data-driven system equation. Our approach improves both predictive accuracy and monitorability through the chain of thought reasoning process, thereby facilitating clinical translation into lung cancer screening.", "date": "2025-09-07", "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": "2509.04497", "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": "2509.04519", "title": "Hierarchical Section Matching Prediction (HSMP) BERT for Fine-Grained Extraction of Structured Data from Hebrew Free-Text Radiology Reports in Crohn's Disease", "abstract": "Extracting structured clinical information from radiology reports is challenging, especially in low-resource languages. This is pronounced in Crohn's disease, with sparsely represented multi-organ findings. We developed Hierarchical Structured Matching Prediction BERT (HSMP-BERT), a prompt-based model for extraction from Hebrew radiology text. In an administrative database study, we analyzed 9,683 reports from Crohn's patients imaged 2010-2023 across Israeli providers. A subset of 512 reports was radiologist-annotated for findings across six gastrointestinal organs and 15 pathologies, yielding 90 structured labels per subject. Multilabel-stratified split (66% train+validation; 33% test), preserving label prevalence. Performance was evaluated with accuracy, F1, Cohen's $\\kappa$, AUC, PPV, NPV, and recall. On 24 organ-finding combinations with $>$15 positives, HSMP-BERT achieved mean F1 0.83$\\pm$0.08 and $\\kappa$ 0.65$\\pm$0.17, outperforming the SMP zero-shot baseline (F1 0.49$\\pm$0.07, $\\kappa$ 0.06$\\pm$0.07) and standard fine-tuning (F1 0.30$\\pm$0.27, $\\kappa$ 0.27$\\pm$0.34; paired t-test $p < 10^{-7}$). Hierarchical inference cuts runtime 5.1$\\times$ vs. traditional inference. Applied to all reports, it revealed associations among ileal wall thickening, stenosis, and pre-stenotic dilatation, plus age- and sex-specific trends in inflammatory findings. HSMP-BERT offers a scalable solution for structured extraction in radiology, enabling population-level analysis of Crohn's disease and demonstrating AI's potential in low-resource settings.", "date": "2025-09-03", "categories": []}
{"id": "2509.04534", "title": "Quantized Large Language Models in Biomedical Natural Language Processing: Evaluation and Recommendation", "abstract": "Large language models have demonstrated remarkable capabilities in biomedical natural language processing, yet their rapid growth in size and computational requirements present a major barrier to adoption in healthcare settings where data privacy precludes cloud deployment and resources are limited. In this study, we systematically evaluated the impact of quantization on 12 state-of-the-art large language models, including both general-purpose and biomedical-specific models, across eight benchmark datasets covering four key tasks: named entity recognition, relation extraction, multi-label classification, and question answering. We show that quantization substantially reduces GPU memory requirements-by up to 75%-while preserving model performance across diverse tasks, enabling the deployment of 70B-parameter models on 40GB consumer-grade GPUs. In addition, domain-specific knowledge and responsiveness to advanced prompting methods are largely maintained. These findings provide significant practical and guiding value, highlighting quantization as a practical and effective strategy for enabling the secure, local deployment of large yet high-capacity language models in biomedical contexts, bridging the gap between technical advances in AI and real-world clinical translation.", "date": "2025-09-04", "categories": []}
{"id": "2509.04753", "title": "A Study of Large Language Models for Patient Information Extraction: Model Architecture, Fine-Tuning Strategy, and Multi-task Instruction Tuning", "abstract": "Natural language processing (NLP) is a key technology to extract important patient information from clinical narratives to support healthcare applications. The rapid development of large language models (LLMs) has revolutionized many NLP tasks in the clinical domain, yet their optimal use in patient information extraction tasks requires further exploration. This study examines LLMs' effectiveness in patient information extraction, focusing on LLM architectures, fine-tuning strategies, and multi-task instruction tuning techniques for developing robust and generalizable patient information extraction systems. This study aims to explore key concepts of using LLMs for clinical concept and relation extraction tasks, including: (1) encoder-only or decoder-only LLMs, (2) prompt-based parameter-efficient fine-tuning (PEFT) algorithms, and (3) multi-task instruction tuning on few-shot learning performance. We benchmarked a suite of LLMs, including encoder-based LLMs (BERT, GatorTron) and decoder-based LLMs (GatorTronGPT, Llama 3.1, GatorTronLlama), across five datasets. We compared traditional full-size fine-tuning and prompt-based PEFT. We explored a multi-task instruction tuning framework that combines both tasks across four datasets to evaluate the zero-shot and few-shot learning performance using the leave-one-dataset-out strategy.", "date": "2025-09-05", "categories": []}
{"id": "2509.04868", "title": "Using LLMs for Multilingual Clinical Entity Linking to ICD-10", "abstract": "The linking of clinical entities is a crucial part of extracting structured information from clinical texts. It is the process of assigning a code from a medical ontology or classification to a phrase in the text. The International Classification of Diseases - 10th revision (ICD-10) is an international standard for classifying diseases for statistical and insurance purposes. Automatically assigning the correct ICD-10 code to terms in discharge summaries will simplify the work of healthcare professionals and ensure consistent coding in hospitals. Our paper proposes an approach for linking clinical terms to ICD-10 codes in different languages using Large Language Models (LLMs). The approach consists of a multistage pipeline that uses clinical dictionaries to match unambiguous terms in the text and then applies in-context learning with GPT-4.1 to predict the ICD-10 code for the terms that do not match the dictionary. Our system shows promising results in predicting ICD-10 codes on different benchmark datasets in Spanish - 0.89 F1 for categories and 0.78 F1 on subcategories on CodiEsp, and Greek - 0.85 F1 on ElCardioCC.", "date": "2025-09-05", "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": "2509.03662", "title": "Semantic Analysis of SNOMED CT Concept Co-occurrences in Clinical Documentation using MIMIC-IV", "abstract": "Clinical notes contain rich clinical narratives but their unstructured format poses challenges for large-scale analysis. Standardized terminologies such as SNOMED CT improve interoperability, yet understanding how concepts relate through co-occurrence and semantic similarity remains underexplored. In this study, we leverage the MIMIC-IV database to investigate the relationship between SNOMED CT concept co-occurrence patterns and embedding-based semantic similarity. Using Normalized Pointwise Mutual Information (NPMI) and pretrained embeddings (e.g., ClinicalBERT, BioBERT), we examine whether frequently co-occurring concepts are also semantically close, whether embeddings can suggest missing concepts, and how these relationships evolve temporally and across specialties. Our analyses reveal that while co-occurrence and semantic similarity are weakly correlated, embeddings capture clinically meaningful associations not always reflected in documentation frequency. Embedding-based suggestions frequently matched concepts later documented, supporting their utility for augmenting clinical annotations. Clustering of concept embeddings yielded coherent clinical themes (symptoms, labs, diagnoses, cardiovascular conditions) that map to patient phenotypes and care patterns. Finally, co-occurrence patterns linked to outcomes such as mortality and readmission demonstrate the practical utility of this approach. Collectively, our findings highlight the complementary value of co-occurrence statistics and semantic embeddings in improving documentation completeness, uncovering latent clinical relationships, and informing decision support and phenotyping applications.", "date": "2025-09-03", "categories": []}
{"id": "2505.17067", "title": "Unveil Multi-Picture Descriptions for Multilingual Mild Cognitive Impairment Detection via Contrastive Learning", "abstract": "Detecting Mild Cognitive Impairment from picture descriptions is critical yet challenging, especially in multilingual and multiple picture settings. Prior work has primarily focused on English speakers describing a single picture (e.g., the 'Cookie Theft'). The TAUKDIAL-2024 challenge expands this scope by introducing multilingual speakers and multiple pictures, which presents new challenges in analyzing picture-dependent content. To address these challenges, we propose a framework with three components: (1) enhancing discriminative representation learning via supervised contrastive learning, (2) involving image modality rather than relying solely on speech and text modalities, and (3) applying a Product of Experts (PoE) strategy to mitigate spurious correlations and overfitting. Our framework improves MCI detection performance, achieving a +7.1% increase in Unweighted Average Recall (UAR) (from 68.1% to 75.2%) and a +2.9% increase in F1 score (from 80.6% to 83.5%) compared to the text unimodal baseline. Notably, the contrastive learning component yields greater gains for the text modality compared to speech. These results highlight our framework's effectiveness in multilingual and multi-picture MCI detection.", "date": "2025-05-19", "categories": []}
{"id": "2412.15907", "title": "Development of a Large-scale Dataset of Chest Computed Tomography Reports in Japanese and a High-performance Finding Classification Model", "abstract": "Background: Recent advances in large language models highlight the need for high-quality multilingual medical datasets. While Japan leads globally in CT scanner deployment and utilization, the lack of large-scale Japanese radiology datasets has hindered the development of specialized language models for medical imaging analysis. Objective: To develop a comprehensive Japanese CT report dataset through machine translation and establish a specialized language model for structured finding classification. Additionally, to create a rigorously validated evaluation dataset through expert radiologist review. Methods: We translated the CT-RATE dataset (24,283 CT reports from 21,304 patients) into Japanese using GPT-4o mini. The training dataset consisted of 22,778 machine-translated reports, while the validation dataset included 150 radiologist-revised reports. We developed CT-BERT-JPN based on \"tohoku-nlp/bert-base-japanese-v3\" architecture for extracting 18 structured findings from Japanese radiology reports. Results: Translation metrics showed strong performance with BLEU scores of 0.731 and 0.690, and ROUGE scores ranging from 0.770 to 0.876 for Findings and from 0.748 to 0.857 for Impression sections. CT-BERT-JPN demonstrated superior performance compared to GPT-4o in 11 out of 18 conditions, including lymphadenopathy (+14.2%), interlobular septal thickening (+10.9%), and atelectasis (+7.4%). The model maintained F1 scores exceeding 0.95 in 14 out of 18 conditions and achieved perfect scores in four conditions. Conclusions: Our study establishes a robust Japanese CT report dataset and demonstrates the effectiveness of a specialized language model for structured finding classification. The hybrid approach of machine translation and expert validation enables the creation of large-scale medical datasets while maintaining high quality.", "date": "2024-12-20", "categories": []}
{"id": "2508.08876", "title": "Weakly Supervised Fine-grained Span-Level Framework for Chinese Radiology Report Quality Assurance", "abstract": "Quality Assurance (QA) for radiology reports refers to judging whether the junior reports (written by junior doctors) are qualified. The QA scores of one junior report are given by the senior doctor(s) after reviewing the image and junior report. This process requires intensive labor costs for senior doctors. Additionally, the QA scores may be inaccurate for reasons like diagnosis bias, the ability of senior doctors, and so on. To address this issue, we propose a Span-level Quality Assurance EvaluaTOR (Sqator) to mark QA scores automatically. Unlike the common document-level semantic comparison method, we try to analyze the semantic difference by exploring more fine-grained text spans. Specifically, Sqator measures QA scores by measuring the importance of revised spans between junior and senior reports, and outputs the final QA scores by merging all revised span scores. We evaluate Sqator using a collection of 12,013 radiology reports. Experimental results show that Sqator can achieve competitive QA scores. Moreover, the importance scores of revised spans can be also consistent with the judgments of senior doctors.", "date": "2025-08-12", "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": "2509.01885", "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": "2509.01899", "title": "Weakly Supervised Medical Entity Extraction and Linking for Chief Complaints", "abstract": "A Chief complaint (CC) is the reason for the medical visit as stated in the patient's own words. It helps medical professionals to quickly understand a patient's situation, and also serves as a short summary for medical text mining. However, chief complaint records often take a variety of entering methods, resulting in a wide variation of medical notations, which makes it difficult to standardize across different medical institutions for record keeping or text mining. In this study, we propose a weakly supervised method to automatically extract and link entities in chief complaints in the absence of human annotation. We first adopt a split-and-match algorithm to produce weak annotations, including entity mention spans and class labels, on 1.2 million real-world de-identified and IRB approved chief complaint records. Then we train a BERT-based model with generated weak labels to locate entity mentions in chief complaint text and link them to a pre-defined ontology. We conducted extensive experiments, and the results showed that our Weakly Supervised Entity Extraction and Linking (\\ours) method produced superior performance over previous methods without any human annotation.", "date": "2025-09-02", "categories": []}
{"id": "2509.02446", "title": "An Ensemble Classification Approach in A Multi-Layered Large Language Model Framework for Disease Prediction", "abstract": "Social telehealth has made remarkable progress in healthcare by allowing patients to post symptoms and participate in medical consultations remotely. Users frequently post symptoms on social media and online health platforms, creating a huge repository of medical data that can be leveraged for disease classification. Large language models (LLMs) such as LLAMA3 and GPT-3.5, along with transformer-based models like BERT, have demonstrated strong capabilities in processing complex medical text. In this study, we evaluate three Arabic medical text preprocessing methods such as summarization, refinement, and Named Entity Recognition (NER) before applying fine-tuned Arabic transformer models (CAMeLBERT, AraBERT, and AsafayaBERT). To enhance robustness, we adopt a majority voting ensemble that combines predictions from original and preprocessed text representations. This approach achieved the best classification accuracy of 80.56%, thus showing its effectiveness in leveraging various text representations and model predictions to improve the understanding of medical texts. To the best of our knowledge, this is the first work that integrates LLM-based preprocessing with fine-tuned Arabic transformer models and ensemble learning for disease classification in Arabic social telehealth data.", "date": "2025-09-02", "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": "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": "2508.19274", "title": "Leveraging Language Models and Machine Learning in Verbal Autopsy Analysis", "abstract": "In countries without civil registration and vital statistics, verbal autopsy (VA) is a critical tool for estimating cause of death (COD) and inform policy priorities. In VA, interviewers ask proximal informants for details on the circumstances preceding a death, in the form of unstructured narratives and structured questions. Existing automated VA cause classification algorithms only use the questions and ignore the information in the narratives. In this thesis, we investigate how the VA narrative can be used for automated COD classification using pretrained language models (PLMs) and machine learning (ML) techniques. Using empirical data from South Africa, we demonstrate that with the narrative alone, transformer-based PLMs with task-specific fine-tuning outperform leading question-only algorithms at both the individual and population levels, particularly in identifying non-communicable diseases. We explore various multimodal fusion strategies combining narratives and questions in unified frameworks. Multimodal approaches further improve performance in COD classification, confirming that each modality has unique contributions and may capture valuable information that is not present in the other modality. We also characterize physician-perceived information sufficiency in VA. We describe variations in sufficiency levels by age and COD and demonstrate that classification accuracy is affected by sufficiency for both physicians and models. Overall, this thesis advances the growing body of knowledge at the intersection of natural language processing, epidemiology, and global health. It demonstrates the value of narrative in enhancing COD classification. Our findings underscore the need for more high-quality data from more diverse settings to use in training and fine-tuning PLM/ML methods, and offer valuable insights to guide the rethinking and redesign of the VA instrument and interview.", "date": "2025-08-22", "categories": []}
{"id": "2508.19467", "title": "Inference Gap in Domain Expertise and Machine Intelligence in Named Entity Recognition: Creation of and Insights from a Substance Use-related Dataset", "abstract": "Nonmedical opioid use is an urgent public health challenge, with far-reaching clinical and social consequences that are often underreported in traditional healthcare settings. Social media platforms, where individuals candidly share first-person experiences, offer a valuable yet underutilized source of insight into these impacts. In this study, we present a named entity recognition (NER) framework to extract two categories of self-reported consequences from social media narratives related to opioid use: ClinicalImpacts (e.g., withdrawal, depression) and SocialImpacts (e.g., job loss). To support this task, we introduce RedditImpacts 2.0, a high-quality dataset with refined annotation guidelines and a focus on first-person disclosures, addressing key limitations of prior work. We evaluate both fine-tuned encoder-based models and state-of-the-art large language models (LLMs) under zero- and few-shot in-context learning settings. Our fine-tuned DeBERTa-large model achieves a relaxed token-level F1 of 0.61 [95% CI: 0.43-0.62], consistently outperforming LLMs in precision, span accuracy, and adherence to task-specific guidelines. Furthermore, we show that strong NER performance can be achieved with substantially less labeled data, emphasizing the feasibility of deploying robust models in resource-limited settings. Our findings underscore the value of domain-specific fine-tuning for clinical NLP tasks and contribute to the responsible development of AI tools that may enhance addiction surveillance, improve interpretability, and support real-world healthcare decision-making. The best performing model, however, still significantly underperforms compared to inter-expert agreement (Cohen's kappa: 0.81), demonstrating that a gap persists between expert intelligence and current state-of-the-art NER/AI capabilities for tasks requiring deep domain knowledge.", "date": "2025-08-26", "categories": []}
{"id": "2505.10717", "title": "A Modular Approach for Clinical SLMs Driven by Synthetic Data with Pre-Instruction Tuning, Model Merging, and Clinical-Tasks Alignment", "abstract": "High computation costs and latency of large language models such as GPT-4 have limited their deployment in clinical settings. Small language models (SLMs) offer a cost-effective alternative, but their limited capacity requires biomedical domain adaptation, which remains challenging. An additional bottleneck is the unavailability and high sensitivity of clinical data. To address these challenges, we propose a novel framework for adapting SLMs into high-performing clinical models. We introduce the MediPhi collection of 3.8B-parameter SLMs developed with our novel framework: pre-instruction tuning of experts on relevant medical and clinical corpora (PMC, Medical Guideline, MedWiki, etc.), model merging, and clinical-tasks alignment. To cover most clinical tasks, we extended the CLUE benchmark to CLUE+, doubling its size. Our expert models deliver relative improvements on this benchmark over the base model without any task-specific fine-tuning: 64.3% on medical entities, 49.5% on radiology reports, and 44% on ICD-10 coding (outperforming GPT-4-0125 by 14%). We unify the expert models into MediPhi via model merging, preserving gains across benchmarks. Furthermore, we built the MediFlow collection, a synthetic dataset of 2.5 million high-quality instructions on 14 medical NLP tasks, 98 fine-grained document types, and JSON format support. Alignment of MediPhi using supervised fine-tuning and direct preference optimization achieves further gains of 18.9% on average.", "date": "2025-05-15", "categories": []}
{"id": "2508.18607", "title": "A New NMT Model for Translating Clinical Texts from English to Spanish", "abstract": "Translating electronic health record (EHR) narratives from English to Spanish is a clinically important yet challenging task due to the lack of a parallel-aligned corpus and the abundant unknown words contained. To address such challenges, we propose \\textbf{NOOV} (for No OOV), a new neural machine translation (NMT) system that requires little in-domain parallel-aligned corpus for training. NOOV integrates a bilingual lexicon automatically learned from parallel-aligned corpora and a phrase look-up table extracted from a large biomedical knowledge resource, to alleviate both the unknown word problem and the word-repeat challenge in NMT, enhancing better phrase generation of NMT systems. Evaluation shows that NOOV is able to generate better translation of EHR with improvement in both accuracy and fluency.", "date": "2025-08-26", "categories": []}
{"id": "2508.19077", "title": "\"Where does it hurt?\" -- Dataset and Study on Physician Intent Trajectories in Doctor Patient Dialogues", "abstract": "In a doctor-patient dialogue, the primary objective of physicians is to diagnose patients and propose a treatment plan. Medical doctors guide these conversations through targeted questioning to efficiently gather the information required to provide the best possible outcomes for patients. To the best of our knowledge, this is the first work that studies physician intent trajectories in doctor-patient dialogues. We use the `Ambient Clinical Intelligence Benchmark' (Aci-bench) dataset for our study. We collaborate with medical professionals to develop a fine-grained taxonomy of physician intents based on the SOAP framework (Subjective, Objective, Assessment, and Plan). We then conduct a large-scale annotation effort to label over 5000 doctor-patient turns with the help of a large number of medical experts recruited using Prolific, a popular crowd-sourcing platform. This large labeled dataset is an important resource contribution that we use for benchmarking the state-of-the-art generative and encoder models for medical intent classification tasks. Our findings show that our models understand the general structure of medical dialogues with high accuracy, but often fail to identify transitions between SOAP categories. We also report for the first time common trajectories in medical dialogue structures that provide valuable insights for designing `differential diagnosis' systems. Finally, we extensively study the impact of intent filtering for medical dialogue summarization and observe a significant boost in performance. We make the codes and data, including annotation guidelines, publicly available at https://github.com/DATEXIS/medical-intent-classification.", "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": "2409.17054", "title": "Using LLM for Real-Time Transcription and Summarization of Doctor-Patient Interactions into ePuskesmas in Indonesia: A Proof-of-Concept Study", "abstract": "One of the critical issues contributing to inefficiency in Puskesmas (Indonesian community health centers) is the time-consuming nature of documenting doctor-patient interactions. Doctors must conduct thorough consultations and manually transcribe detailed notes into ePuskesmas electronic health records (EHR), which creates substantial administrative burden to already overcapacitated physicians. This paper presents a proof-of-concept framework using large language models (LLMs) to automate real-time transcription and summarization of doctor-patient conversations in Bahasa Indonesia. Our system combines Whisper model for transcription with GPT-3.5 for medical summarization, implemented as a browser extension that automatically populates ePuskesmas forms. Through controlled roleplay experiments with medical validation, we demonstrate the technical feasibility of processing detailed 300+ seconds trimmed consultations in under 30 seconds while maintaining clinical accuracy. This work establishes the foundation for AI-assisted clinical documentation in resource-constrained healthcare environments. However, concerns have also been raised regarding privacy compliance and large-scale clinical evaluation addressing language and cultural biases for LLMs.", "date": "2024-09-25", "categories": []}
{"id": "2505.17095", "title": "Are LLMs reliable? An exploration of the reliability of large language models in clinical note generation", "abstract": "Due to the legal and ethical responsibilities of healthcare providers (HCPs) for accurate documentation and protection of patient data privacy, the natural variability in the responses of large language models (LLMs) presents challenges for incorporating clinical note generation (CNG) systems, driven by LLMs, into real-world clinical processes. The complexity is further amplified by the detailed nature of texts in CNG. To enhance the confidence of HCPs in tools powered by LLMs, this study evaluates the reliability of 12 open-weight and proprietary LLMs from Anthropic, Meta, Mistral, and OpenAI in CNG in terms of their ability to generate notes that are string equivalent (consistency rate), have the same meaning (semantic consistency) and are correct (semantic similarity), across several iterations using the same prompt. The results show that (1) LLMs from all model families are stable, such that their responses are semantically consistent despite being written in various ways, and (2) most of the LLMs generated notes close to the corresponding notes made by experts. Overall, Meta's Llama 70B was the most reliable, followed by Mistral's Small model. With these findings, we recommend the local deployment of these relatively smaller open-weight models for CNG to ensure compliance with data privacy regulations, as well as to improve the efficiency of HCPs in clinical documentation.", "date": "2025-05-21", "categories": []}
{"id": "2507.22939", "title": "PARROT: An Open Multilingual Radiology Reports Dataset", "abstract": "Rationale and Objectives: To develop and validate PARROT (Polyglottal Annotated Radiology Reports for Open Testing), a large, multicentric, open-access dataset of fictional radiology reports spanning multiple languages for testing natural language processing applications in radiology. Materials and Methods: From May to September 2024, radiologists were invited to contribute fictional radiology reports following their standard reporting practices. Contributors provided at least 20 reports with associated metadata including anatomical region, imaging modality, clinical context, and for non-English reports, English translations. All reports were assigned ICD-10 codes. A human vs. AI report differentiation study was conducted with 154 participants (radiologists, healthcare professionals, and non-healthcare professionals) assessing whether reports were human-authored or AI-generated. Results: The dataset comprises 2,658 radiology reports from 76 authors across 21 countries and 13 languages. Reports cover multiple imaging modalities (CT: 36.1%, MRI: 22.8%, radiography: 19.0%, ultrasound: 16.8%) and anatomical regions, with chest (19.9%), abdomen (18.6%), head (17.3%), and pelvis (14.1%) being most prevalent. In the differentiation study, participants achieved 53.9% accuracy (95% CI: 50.7%-57.1%) in distinguishing between human and AI-generated reports, with radiologists performing significantly better (56.9%, 95% CI: 53.3%-60.6%, p<0.05) than other groups. Conclusion: PARROT represents the largest open multilingual radiology report dataset, enabling development and validation of natural language processing applications across linguistic, geographic, and clinical boundaries without privacy constraints.", "date": "2025-07-25", "categories": []}
{"id": "2505.03427", "title": "MedArabiQ: Benchmarking Large Language Models on Arabic Medical Tasks", "abstract": "Large Language Models (LLMs) have demonstrated significant promise for various applications in healthcare. However, their efficacy in the Arabic medical domain remains unexplored due to the lack of high-quality domain-specific datasets and benchmarks. This study introduces MedArabiQ, a novel benchmark dataset consisting of seven Arabic medical tasks, covering multiple specialties and including multiple choice questions, fill-in-the-blank, and patient-doctor question answering. We first constructed the dataset using past medical exams and publicly available datasets. We then introduced different modifications to evaluate various LLM capabilities, including bias mitigation. We conducted an extensive evaluation with five state-of-the-art open-source and proprietary LLMs, including GPT-4o, Claude 3.5-Sonnet, and Gemini 1.5. Our findings highlight the need for the creation of new high-quality benchmarks that span different languages to ensure fair deployment and scalability of LLMs in healthcare. By establishing this benchmark and releasing the dataset, we provide a foundation for future research aimed at evaluating and enhancing the multilingual capabilities of LLMs for the equitable use of generative AI in healthcare.", "date": "2025-05-06", "categories": []}
{"id": "2508.15797", "title": "Benchmarking the Medical Understanding and Reasoning of Large Language Models in Arabic Healthcare Tasks", "abstract": "Recent progress in large language models (LLMs) has showcased impressive proficiency in numerous Arabic natural language processing (NLP) applications. Nevertheless, their effectiveness in Arabic medical NLP domains has received limited investigation. This research examines the degree to which state-of-the-art LLMs demonstrate and articulate healthcare knowledge in Arabic, assessing their capabilities across a varied array of Arabic medical tasks. We benchmark several LLMs using a medical dataset proposed in the Arabic NLP AraHealthQA challenge in MedArabiQ2025 track. Various base LLMs were assessed on their ability to accurately provide correct answers from existing choices in multiple-choice questions (MCQs) and fill-in-the-blank scenarios. Additionally, we evaluated the capacity of LLMs in answering open-ended questions aligned with expert answers. Our results reveal significant variations in correct answer prediction accuracy and low variations in semantic alignment of generated answers, highlighting both the potential and limitations of current LLMs in Arabic clinical contexts. Our analysis shows that for MCQs task, the proposed majority voting solution, leveraging three base models (Gemini Flash 2.5, Gemini Pro 2.5, and GPT o3), outperforms others, achieving up to 77% accuracy and securing first place overall in the Arahealthqa 2025 shared task-track 2 (sub-task 1) challenge. Moreover, for the open-ended questions task, several LLMs were able to demonstrate excellent performance in terms of semantic alignment and achieve a maximum BERTScore of 86.44%.", "date": "2025-08-13", "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": "2410.15186", "title": "Fine-tuning foundational models to code diagnoses from veterinary health records", "abstract": "Veterinary medical records represent a large data resource for application to veterinary and One Health clinical research efforts. Use of the data is limited by interoperability challenges including inconsistent data formats and data siloing. Clinical coding using standardized medical terminologies enhances the quality of medical records and facilitates their interoperability with veterinary and human health records from other sites. Previous studies, such as DeepTag and VetTag, evaluated the application of Natural Language Processing (NLP) to automate veterinary diagnosis coding, employing long short-term memory (LSTM) and transformer models to infer a subset of Systemized Nomenclature of Medicine - Clinical Terms (SNOMED-CT) diagnosis codes from free-text clinical notes. This study expands on these efforts by incorporating all 7,739 distinct SNOMED-CT diagnosis codes recognized by the Colorado State University (CSU) Veterinary Teaching Hospital (VTH) and by leveraging the increasing availability of pre-trained language models (LMs). 13 freely-available pre-trained LMs were fine-tuned on the free-text notes from 246,473 manually-coded veterinary patient visits included in the CSU VTH's electronic health records (EHRs), which resulted in superior performance relative to previous efforts. The most accurate results were obtained when expansive labeled data were used to fine-tune relatively large clinical LMs, but the study also showed that comparable results can be obtained using more limited resources and non-clinical LMs. The results of this study contribute to the improvement of the quality of veterinary EHRs by investigating accessible methods for automated coding and support both animal and human health research by paving the way for more integrated and comprehensive health databases that span species and institutions.", "date": "2024-10-19", "categories": []}
{"id": "2508.15149", "title": "A Robust BERT-Based Deep Learning Model for Automated Cancer Type Extraction from Unstructured Pathology Reports", "abstract": "The accurate extraction of clinical information from electronic medical records is particularly critical to clinical research but require much trained expertise and manual labor. In this study we developed a robust system for automated extraction of the specific cancer types for the purpose of supporting precision oncology research. from pathology reports using a fine-tuned RoBERTa model. This model significantly outperformed the baseline model and a Large Language Model, Mistral 7B, achieving F1_Bertscore 0.98 and overall exact match of 80.61%. This fine-tuning approach demonstrates the potential for scalability that can integrate seamlessly into the molecular tumour board process. Fine-tuning domain-specific models for precision tasks in oncology, may pave the way for more efficient and accurate clinical information extraction.", "date": "2025-08-21", "categories": []}
{"id": "2409.15163", "title": "Lessons Learned on Information Retrieval in Electronic Health Records: A Comparison of Embedding Models and Pooling Strategies", "abstract": "Objective: Applying large language models (LLMs) to the clinical domain is challenging due to the context-heavy nature of processing medical records. Retrieval-augmented generation (RAG) offers a solution by facilitating reasoning over large text sources. However, there are many parameters to optimize in just the retrieval system alone. This paper presents an ablation study exploring how different embedding models and pooling methods affect information retrieval for the clinical domain.\n  Methods: Evaluating on three retrieval tasks on two electronic health record (EHR) data sources, we compared seven models, including medical- and general-domain models, specialized encoder embedding models, and off-the-shelf decoder LLMs. We also examine the choice of embedding pooling strategy for each model, independently on the query and the text to retrieve.\n  Results: We found that the choice of embedding model significantly impacts retrieval performance, with BGE, a comparatively small general-domain model, consistently outperforming all others, including medical-specific models. However, our findings also revealed substantial variability across datasets and query text phrasings. We also determined the best pooling methods for each of these models to guide future design of retrieval systems.\n  Discussion: The choice of embedding model, pooling strategy, and query formulation can significantly impact retrieval performance and the performance of these models on other public benchmarks does not necessarily transfer to new domains. Further studies such as this one are vital for guiding empirically-grounded development of retrieval frameworks, such as in the context of RAG, for the clinical domain.", "date": "2024-09-23", "categories": []}
{"id": "2504.19061", "title": "Hallucinations and Key Information Extraction in Medical Texts: A Comprehensive Assessment of Open-Source Large Language Models", "abstract": "Clinical summarization is crucial in healthcare as it distills complex medical data into digestible information, enhancing patient understanding and care management. Large language models (LLMs) have shown significant potential in automating and improving the accuracy of such summarizations due to their advanced natural language understanding capabilities. These models are particularly applicable in the context of summarizing medical/clinical texts, where precise and concise information transfer is essential. In this paper, we investigate the effectiveness of open-source LLMs in extracting key events from discharge reports, including admission reasons, major in-hospital events, and critical follow-up actions. In addition, we also assess the prevalence of various types of hallucinations in the summaries produced by these models. Detecting hallucinations is vital as it directly influences the reliability of the information, potentially affecting patient care and treatment outcomes. We conduct comprehensive simulations to rigorously evaluate the performance of these models, further probing the accuracy and fidelity of the extracted content in clinical summarization. Our results reveal that while the LLMs (e.g., Qwen2.5 and DeepSeek-v2) perform quite well in capturing admission reasons and hospitalization events, they are generally less consistent when it comes to identifying follow-up recommendations, highlighting broader challenges in leveraging LLMs for comprehensive summarization.", "date": "2025-04-27", "categories": []}
{"id": "2508.14817", "title": "Evaluating Retrieval-Augmented Generation vs. Long-Context Input for Clinical Reasoning over EHRs", "abstract": "Electronic health records (EHRs) are long, noisy, and often redundant, posing a major challenge for the clinicians who must navigate them. Large language models (LLMs) offer a promising solution for extracting and reasoning over this unstructured text, but the length of clinical notes often exceeds even state-of-the-art models' extended context windows. Retrieval-augmented generation (RAG) offers an alternative by retrieving task-relevant passages from across the entire EHR, potentially reducing the amount of required input tokens. In this work, we propose three clinical tasks designed to be replicable across health systems with minimal effort: 1) extracting imaging procedures, 2) generating timelines of antibiotic use, and 3) identifying key diagnoses. Using EHRs from actual hospitalized patients, we test three state-of-the-art LLMs with varying amounts of provided context, using either targeted text retrieval or the most recent clinical notes. We find that RAG closely matches or exceeds the performance of using recent notes, and approaches the performance of using the models' full context while requiring drastically fewer input tokens. Our results suggest that RAG remains a competitive and efficient approach even as newer models become capable of handling increasingly longer amounts of text.", "date": "2025-08-20", "categories": []}
{"id": "2502.00943", "title": "Universal Abstraction: Harnessing Frontier Models to Structure Real-World Data at Scale", "abstract": "A significant fraction of real-world patient information resides in unstructured clinical text. Medical abstraction extracts and normalizes key structured attributes from free-text clinical notes, which is the prerequisite for a variety of important downstream applications, including registry curation, clinical trial operations, and real-world evidence generation. Prior medical abstraction methods typically resort to building attribute-specific models, each of which requires extensive manual effort such as rule creation or supervised label annotation for the individual attribute, thus limiting scalability. In this paper, we show that existing frontier models already possess the universal abstraction capability for scaling medical abstraction to a wide range of clinical attributes. We present UniMedAbstractor (UMA), a unifying framework for zero-shot medical abstraction with a modular, customizable prompt template and the selection of any frontier large language models. Given a new attribute for abstraction, users only need to conduct lightweight prompt adaptation in UMA to adjust the specification in natural languages. Compared to traditional methods, UMA eliminates the need for attribute-specific training labels or handcrafted rules, thus substantially reducing the development time and cost. We conducted a comprehensive evaluation of UMA in oncology using a wide range of marquee attributes representing the cancer patient journey. These include relatively simple attributes typically specified within a single clinical note (e.g. performance status), as well as complex attributes requiring sophisticated reasoning across multiple notes at various time points (e.g. tumor staging). Based on a single frontier model such as GPT-4o, UMA matched or even exceeded the performance of state-of-the-art attribute-specific methods, each of which was tailored to the individual attribute.", "date": "2025-02-02", "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": "2508.14032", "title": "The Promise of Large Language Models in Digital Health: Evidence from Sentiment Analysis in Online Health Communities", "abstract": "Digital health analytics face critical challenges nowadays. The sophisticated analysis of patient-generated health content, which contains complex emotional and medical contexts, requires scarce domain expertise, while traditional ML approaches are constrained by data shortage and privacy limitations in healthcare settings. Online Health Communities (OHCs) exemplify these challenges with mixed-sentiment posts, clinical terminology, and implicit emotional expressions that demand specialised knowledge for accurate Sentiment Analysis (SA). To address these challenges, this study explores how Large Language Models (LLMs) can integrate expert knowledge through in-context learning for SA, providing a scalable solution for sophisticated health data analysis. Specifically, we develop a structured codebook that systematically encodes expert interpretation guidelines, enabling LLMs to apply domain-specific knowledge through targeted prompting rather than extensive training. Six GPT models validated alongside DeepSeek and LLaMA 3.1 are compared with pre-trained language models (BioBERT variants) and lexicon-based methods, using 400 expert-annotated posts from two OHCs. LLMs achieve superior performance while demonstrating expert-level agreement. This high agreement, with no statistically significant difference from inter-expert agreement levels, suggests knowledge integration beyond surface-level pattern recognition. The consistent performance across diverse LLM models, supported by in-context learning, offers a promising solution for digital health analytics. This approach addresses the critical challenge of expert knowledge shortage in digital health research, enabling real-time, expert-quality analysis for patient monitoring, intervention assessment, and evidence-based health strategies.", "date": "2025-08-19", "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": "2508.09991", "title": "Bridging AI Innovation and Healthcare Needs: Lessons Learned from Incorporating Modern NLP at The BC Cancer Registry", "abstract": "Automating data extraction from clinical documents offers significant potential to improve efficiency in healthcare settings, yet deploying Natural Language Processing (NLP) solutions presents practical challenges. Drawing upon our experience implementing various NLP models for information extraction and classification tasks at the British Columbia Cancer Registry (BCCR), this paper shares key lessons learned throughout the project lifecycle. We emphasize the critical importance of defining problems based on clear business objectives rather than solely technical accuracy, adopting an iterative approach to development, and fostering deep interdisciplinary collaboration and co-design involving domain experts, end-users, and ML specialists from inception. Further insights highlight the need for pragmatic model selection (including hybrid approaches and simpler methods where appropriate), rigorous attention to data quality (representativeness, drift, annotation), robust error mitigation strategies involving human-in-the-loop validation and ongoing audits, and building organizational AI literacy. These practical considerations, generalizable beyond cancer registries, provide guidance for healthcare organizations seeking to successfully implement AI/NLP solutions to enhance data management processes and ultimately improve patient care and public health outcomes.", "date": "2025-07-27", "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": "2508.10025", "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": "2508.10226", "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": "2508.10777", "title": "The Knowledge-Reasoning Dissociation: Fundamental Limitations of LLMs in Clinical Natural Language Inference", "abstract": "Large language models are often assumed to acquire increasingly structured, generalizable internal representations simply by scaling data and parameters. We interrogate this assumption by introducing a Clinical Trial Natural Language Inference benchmark comprising four reasoning families, Causal Attribution, Compositional Grounding, Epistemic Verification, and Risk State Abstraction. Each item is paired with a targeted Ground Knowledge and Meta-Level Reasoning Verification (GKMRV) probe, allowing us to dissociate failures of factual access from failures of inference. We evaluate six contemporary LLMs under both direct and chain of thought prompting.\n  Models achieve near-ceiling GKMRV accuracy (mean accuracy 0.918) yet perform poorly on the main reasoning tasks (mean accuracy 0.25). Despite low accuracy, output inferences are highly consistent across samples (mean 0.87), indicating a systematic application of underlying heuristics and shortcuts.\n  These results reveal fundamental structural and representational limitations: current LLMs often possess the relevant clinical knowledge but lack the structured, composable internal representations needed to deploy it reliably (e.g., integrating constraints, weighing evidence, or simulating counterfactuals). Decoupling knowledge from reasoning with GKMRV makes this dissociation explicit and measurable, providing an effective framework for probing the reliability of LLMs in high-stakes domains.", "date": "2025-08-14", "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": "2508.09952", "title": "Specialised or Generic? Tokenization Choices for Radiology Language Models", "abstract": "The vocabulary used by language models (LM) - defined by the tokenizer - plays a key role in text generation quality. However, its impact remains under-explored in radiology. In this work, we address this gap by systematically comparing general, medical, and domain-specific tokenizers on the task of radiology report summarisation across three imaging modalities. We also investigate scenarios with and without LM pre-training on PubMed abstracts. Our findings demonstrate that medical and domain-specific vocabularies outperformed widely used natural language alternatives when models are trained from scratch. Pre-training partially mitigates performance differences between tokenizers, whilst the domain-specific tokenizers achieve the most favourable results. Domain-specific tokenizers also reduce memory requirements due to smaller vocabularies and shorter sequences. These results demonstrate that adapting the vocabulary of LMs to the clinical domain provides practical benefits, including improved performance and reduced computational demands, making such models more accessible and effective for both research and real-world healthcare settings.", "date": "2025-08-13", "categories": []}
{"id": "2503.21004", "title": "Evaluating Large Language Models for Automated Clinical Abstraction in Pulmonary Embolism Registries: Performance Across Model Sizes, Versions, and Parameters", "abstract": "Pulmonary embolism (PE) registries accelerate practice-improving research but depend on resource-intensive manual abstraction of radiology reports. We evaluated whether openly available large-language models (LLMs) can automate concept extraction from computed-tomography PE (CTPE) reports without sacrificing data quality. Four Llama-3 (L3) variants (3.0 8 B, 3.1 8 B, 3.1 70 B, 3.3 70 B) and two reviewer models Phi-4 (P4) 14 B and Gemma-3 27 B (G3) were tested on 250 dual-annotated CTPE reports each from MIMIC-IV and Duke University. Outcomes were accuracy, positive predictive value (PPV), and negative predictive value (NPV) versus a human gold standard across model sizes, temperature settings, and shot counts. Mean accuracy across all concepts increased with scale: 0.83 (L3-0 8 B), 0.91 (L3-1 8 B), and 0.96 for both 70 B variants; P4 14 B achieved 0.98; G3 matched. Accuracy differed by < 0.03 between datasets, underscoring external robustness. In dual-model concordance analysis (L3 70 B + P4 14 B), PE-presence PPV was >= 0.95 and NPV >= 0.98, while location, thrombus burden, right-heart strain, and image-quality artifacts each maintained PPV >= 0.90 and NPV >= 0.95. Fewer than 4% of individual concept annotations were discordant, and complete agreement was observed in more than 75% of reports. G3 performed comparably. LLMs therefore offer a scalable, accurate solution for PE registry abstraction, and a dual-model review workflow can further safeguard data quality with minimal human oversight.", "date": "2025-03-26", "categories": []}
{"id": "2508.08273", "title": "TT-XAI: Trustworthy Clinical Text Explanations via Keyword Distillation and LLM Reasoning", "abstract": "Clinical language models often struggle to provide trustworthy predictions and explanations when applied to lengthy, unstructured electronic health records (EHRs). This work introduces TT-XAI, a lightweight and effective framework that improves both classification performance and interpretability through domain-aware keyword distillation and reasoning with large language models (LLMs). First, we demonstrate that distilling raw discharge notes into concise keyword representations significantly enhances BERT classifier performance and improves local explanation fidelity via a focused variant of LIME. Second, we generate chain-of-thought clinical explanations using keyword-guided prompts to steer LLMs, producing more concise and clinically relevant reasoning. We evaluate explanation quality using deletion-based fidelity metrics, self-assessment via LLaMA-3 scoring, and a blinded human study with domain experts. All evaluation modalities consistently favor the keyword-augmented method, confirming that distillation enhances both machine and human interpretability. TT-XAI offers a scalable pathway toward trustworthy, auditable AI in clinical decision support.", "date": "2025-07-30", "categories": []}
{"id": "2508.08591", "title": "DepressLLM: Interpretable domain-adapted language model for depression detection from real-world narratives", "abstract": "Advances in large language models (LLMs) have enabled a wide range of applications. However, depression prediction is hindered by the lack of large-scale, high-quality, and rigorously annotated datasets. This study introduces DepressLLM, trained and evaluated on a novel corpus of 3,699 autobiographical narratives reflecting both happiness and distress. DepressLLM provides interpretable depression predictions and, via its Score-guided Token Probability Summation (SToPS) module, delivers both improved classification performance and reliable confidence estimates, achieving an AUC of 0.789, which rises to 0.904 on samples with confidence $\\geq$ 0.95. To validate its robustness to heterogeneous data, we evaluated DepressLLM on in-house datasets, including an Ecological Momentary Assessment (EMA) corpus of daily stress and mood recordings, and on public clinical interview data. Finally, a psychiatric review of high-confidence misclassifications highlighted key model and data limitations that suggest directions for future refinements. These findings demonstrate that interpretable AI can enable earlier diagnosis of depression and underscore the promise of medical AI in psychiatry.", "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": "2506.23998", "title": "Auto-TA: Towards Scalable Automated Thematic Analysis (TA) via Multi-Agent Large Language Models with Reinforcement Learning", "abstract": "Congenital heart disease (CHD) presents complex, lifelong challenges often underrepresented in traditional clinical metrics. While unstructured narratives offer rich insights into patient and caregiver experiences, manual thematic analysis (TA) remains labor-intensive and unscalable. We propose a fully automated large language model (LLM) pipeline that performs end-to-end TA on clinical narratives, which eliminates the need for manual coding or full transcript review. Our system employs a novel multi-agent framework, where specialized LLM agents assume roles to enhance theme quality and alignment with human analysis. To further improve thematic relevance, we optionally integrate reinforcement learning from human feedback (RLHF). This supports scalable, patient-centered analysis of large qualitative datasets and allows LLMs to be fine-tuned for specific clinical contexts.", "date": "2025-06-30", "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": "2508.08140", "title": "Data-Efficient Biomedical In-Context Learning: A Diversity-Enhanced Submodular Perspective", "abstract": "Recent progress in large language models (LLMs) has leveraged their in-context learning (ICL) abilities to enable quick adaptation to unseen biomedical NLP tasks. By incorporating only a few input-output examples into prompts, LLMs can rapidly perform these new tasks. While the impact of these demonstrations on LLM performance has been extensively studied, most existing approaches prioritize representativeness over diversity when selecting examples from large corpora. To address this gap, we propose Dual-Div, a diversity-enhanced data-efficient framework for demonstration selection in biomedical ICL. Dual-Div employs a two-stage retrieval and ranking process: First, it identifies a limited set of candidate examples from a corpus by optimizing both representativeness and diversity (with optional annotation for unlabeled data). Second, it ranks these candidates against test queries to select the most relevant and non-redundant demonstrations. Evaluated on three biomedical NLP tasks (named entity recognition (NER), relation extraction (RE), and text classification (TC)) using LLaMA 3.1 and Qwen 2.5 for inference, along with three retrievers (BGE-Large, BMRetriever, MedCPT), Dual-Div consistently outperforms baselines-achieving up to 5% higher macro-F1 scores-while demonstrating robustness to prompt permutations and class imbalance. Our findings establish that diversity in initial retrieval is more critical than ranking-stage optimization, and limiting demonstrations to 3-5 examples maximizes performance efficiency.", "date": "2025-08-11", "categories": []}
{"id": "2508.06145", "title": "Retrieval Augmented Large Language Model System for Comprehensive Drug Contraindications", "abstract": "The versatility of large language models (LLMs) has been explored across various sectors, but their application in healthcare poses challenges, particularly in the domain of pharmaceutical contraindications where accurate and reliable information is required. This study enhances the capability of LLMs to address contraindications effectively by implementing a Retrieval Augmented Generation (RAG) pipeline. Utilizing OpenAI's GPT-4o-mini as the base model, and the text-embedding-3-small model for embeddings, our approach integrates Langchain to orchestrate a hybrid retrieval system with re-ranking. This system leverages Drug Utilization Review (DUR) data from public databases, focusing on contraindications for specific age groups, pregnancy, and concomitant drug use. The dataset includes 300 question-answer pairs across three categories, with baseline model accuracy ranging from 0.49 to 0.57. Post-integration of the RAG pipeline, we observed a significant improvement in model accuracy, achieving rates of 0.94, 0.87, and 0.89 for contraindications related to age groups, pregnancy, and concomitant drug use, respectively. The results indicate that augmenting LLMs with a RAG framework can substantially reduce uncertainty in prescription and drug intake decisions by providing more precise and reliable drug contraindication information.", "date": "2025-08-08", "categories": []}
{"id": "2501.12106", "title": "Can open source large language models be used for tumor documentation in Germany? -- An evaluation on urological doctors' notes", "abstract": "Tumor documentation in Germany is largely done manually, requiring reading patient records and entering data into structured databases. Large language models (LLMs) could potentially enhance this process by improving efficiency and reliability. This evaluation tests eleven different open source LLMs with sizes ranging from 1-70 billion model parameters on three basic tasks of the tumor documentation process: identifying tumor diagnoses, assigning ICD-10 codes, and extracting the date of first diagnosis. For evaluating the LLMs on these tasks, a dataset of annotated text snippets based on anonymized doctors' notes from urology was prepared. Different prompting strategies were used to investigate the effect of the number of examples in few-shot prompting and to explore the capabilities of the LLMs in general. The models Llama 3.1 8B, Mistral 7B, and Mistral NeMo 12 B performed comparably well in the tasks. Models with less extensive training data or having fewer than 7 billion parameters showed notably lower performance, while larger models did not display performance gains. Examples from a different medical domain than urology could also improve the outcome in few-shot prompting, which demonstrates the ability of LLMs to handle tasks needed for tumor documentation. Open source LLMs show a strong potential for automating tumor documentation. Models from 7-12 billion parameters could offer an optimal balance between performance and resource efficiency. With tailored fine-tuning and well-designed prompting, these models might become important tools for clinical documentation in the future. The code for the evaluation is available from https://github.com/stefan-m-lenz/UroLlmEval. We also release the dataset as a new valuable resource that addresses the shortage of authentic and easily accessible benchmarks in German-language medical NLP.", "date": "2025-01-21", "categories": []}
{"id": "2508.05003", "title": "A Multi-Stage Large Language Model Framework for Extracting Suicide-Related Social Determinants of Health", "abstract": "Background: Understanding social determinants of health (SDoH) factors contributing to suicide incidents is crucial for early intervention and prevention. However, data-driven approaches to this goal face challenges such as long-tailed factor distributions, analyzing pivotal stressors preceding suicide incidents, and limited model explainability. Methods: We present a multi-stage large language model framework to enhance SDoH factor extraction from unstructured text. Our approach was compared to other state-of-the-art language models (i.e., pre-trained BioBERT and GPT-3.5-turbo) and reasoning models (i.e., DeepSeek-R1). We also evaluated how the model's explanations help people annotate SDoH factors more quickly and accurately. The analysis included both automated comparisons and a pilot user study. Results: We show that our proposed framework demonstrated performance boosts in the overarching task of extracting SDoH factors and in the finer-grained tasks of retrieving relevant context. Additionally, we show that fine-tuning a smaller, task-specific model achieves comparable or better performance with reduced inference costs. The multi-stage design not only enhances extraction but also provides intermediate explanations, improving model explainability. Conclusions: Our approach improves both the accuracy and transparency of extracting suicide-related SDoH from unstructured texts. These advancements have the potential to support early identification of individuals at risk and inform more effective prevention strategies.", "date": "2025-08-07", "categories": []}
{"id": "2508.05019", "title": "Skin-SOAP: A Weakly Supervised Framework for Generating Structured SOAP Notes", "abstract": "Skin carcinoma is the most prevalent form of cancer globally, accounting for over $8 billion in annual healthcare expenditures. Early diagnosis, accurate and timely treatment are critical to improving patient survival rates. In clinical settings, physicians document patient visits using detailed SOAP (Subjective, Objective, Assessment, and Plan) notes. However, manually generating these notes is labor-intensive and contributes to clinician burnout. In this work, we propose skin-SOAP, a weakly supervised multimodal framework to generate clinically structured SOAP notes from limited inputs, including lesion images and sparse clinical text. Our approach reduces reliance on manual annotations, enabling scalable, clinically grounded documentation while alleviating clinician burden and reducing the need for large annotated data. Our method achieves performance comparable to GPT-4o, Claude, and DeepSeek Janus Pro across key clinical relevance metrics. To evaluate this clinical relevance, we introduce two novel metrics MedConceptEval and Clinical Coherence Score (CCS) which assess semantic alignment with expert medical concepts and input features, respectively.", "date": "2025-08-07", "categories": []}
{"id": "2507.01810", "title": "Evaluating Structured Output Robustness of Small Language Models for Open Attribute-Value Extraction from Clinical Notes", "abstract": "We present a comparative analysis of the parseability of structured outputs generated by small language models for open attribute-value extraction from clinical notes. We evaluate three widely used serialization formats: JSON, YAML, and XML, and find that JSON consistently yields the highest parseability. Structural robustness improves with targeted prompting and larger models, but declines for longer documents and certain note types. Our error analysis identifies recurring format-specific failure patterns. These findings offer practical guidance for selecting serialization formats and designing prompts when deploying language models in privacy-sensitive clinical settings.", "date": "2025-07-02", "categories": []}
{"id": "2508.03399", "title": "ReDSM5: A Reddit Dataset for DSM-5 Depression Detection", "abstract": "Depression is a pervasive mental health condition that affects hundreds of millions of individuals worldwide, yet many cases remain undiagnosed due to barriers in traditional clinical access and pervasive stigma. Social media platforms, and Reddit in particular, offer rich, user-generated narratives that can reveal early signs of depressive symptomatology. However, existing computational approaches often label entire posts simply as depressed or not depressed, without linking language to specific criteria from the DSM-5, the standard clinical framework for diagnosing depression. This limits both clinical relevance and interpretability. To address this gap, we introduce ReDSM5, a novel Reddit corpus comprising 1484 long-form posts, each exhaustively annotated at the sentence level by a licensed psychologist for the nine DSM-5 depression symptoms. For each label, the annotator also provides a concise clinical rationale grounded in DSM-5 methodology. We conduct an exploratory analysis of the collection, examining lexical, syntactic, and emotional patterns that characterize symptom expression in social media narratives. Compared to prior resources, ReDSM5 uniquely combines symptom-specific supervision with expert explanations, facilitating the development of models that not only detect depression but also generate human-interpretable reasoning. We establish baseline benchmarks for both multi-label symptom classification and explanation generation, providing reference results for future research on detection and interpretability.", "date": "2025-08-05", "categories": []}
{"id": "2505.00008", "title": "A Scoping Review of Natural Language Processing in Addressing Medically Inaccurate Information: Errors, Misinformation, and Hallucination", "abstract": "Objective: This review aims to explore the potential and challenges of using Natural Language Processing (NLP) to detect, correct, and mitigate medically inaccurate information, including errors, misinformation, and hallucination. By unifying these concepts, the review emphasizes their shared methodological foundations and their distinct implications for healthcare. Our goal is to advance patient safety, improve public health communication, and support the development of more reliable and transparent NLP applications in healthcare.\n  Methods: A scoping review was conducted following PRISMA guidelines, analyzing studies from 2020 to 2024 across five databases. Studies were selected based on their use of NLP to address medically inaccurate information and were categorized by topic, tasks, document types, datasets, models, and evaluation metrics.\n  Results: NLP has shown potential in addressing medically inaccurate information on the following tasks: (1) error detection (2) error correction (3) misinformation detection (4) misinformation correction (5) hallucination detection (6) hallucination mitigation. However, challenges remain with data privacy, context dependency, and evaluation standards.\n  Conclusion: This review highlights the advancements in applying NLP to tackle medically inaccurate information while underscoring the need to address persistent challenges. Future efforts should focus on developing real-world datasets, refining contextual methods, and improving hallucination management to ensure reliable and transparent healthcare applications.", "date": "2025-04-16", "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": "2508.01999", "title": "Prompting Large Language Models to Detect Dementia Family Caregivers", "abstract": "Social media, such as Twitter, provides opportunities for caregivers of dementia patients to share their experiences and seek support for a variety of reasons. Availability of this information online also paves the way for the development of internet-based interventions in their support. However, for this purpose, tweets written by caregivers of dementia patients must first be identified. This paper demonstrates our system for the SMM4H 2025 shared task 3, which focuses on detecting tweets posted by individuals who have a family member with dementia. The task is outlined as a binary classification problem, differentiating between tweets that mention dementia in the context of a family member and those that do not. Our solution to this problem explores large language models (LLMs) with various prompting methods. Our results show that a simple zero-shot prompt on a fine-tuned model yielded the best results. Our final system achieved a macro F1-score of 0.95 on the validation set and the test set. Our full code is available on GitHub.", "date": "2025-08-04", "categories": []}
{"id": "2508.02082", "title": "S-RRG-Bench: Structured Radiology Report Generation with Fine-Grained Evaluation Framework", "abstract": "Radiology report generation (RRG) for diagnostic images, such as chest X-rays, plays a pivotal role in both clinical practice and AI. Traditional free-text reports suffer from redundancy and inconsistent language, complicating the extraction of critical clinical details. Structured radiology report generation (S-RRG) offers a promising solution by organizing information into standardized, concise formats. However, existing approaches often rely on classification or visual question answering (VQA) pipelines that require predefined label sets and produce only fragmented outputs. Template-based approaches, which generate reports by replacing keywords within fixed sentence patterns, further compromise expressiveness and often omit clinically important details. In this work, we present a novel approach to S-RRG that includes dataset construction, model training, and the introduction of a new evaluation framework. We first create a robust chest X-ray dataset (MIMIC-STRUC) that includes disease names, severity levels, probabilities, and anatomical locations, ensuring that the dataset is both clinically relevant and well-structured. We train an LLM-based model to generate standardized, high-quality reports. To assess the generated reports, we propose a specialized evaluation metric (S-Score) that not only measures disease prediction accuracy but also evaluates the precision of disease-specific details, thus offering a clinically meaningful metric for report quality that focuses on elements critical to clinical decision-making and demonstrates a stronger alignment with human assessments. Our approach highlights the effectiveness of structured reports and the importance of a tailored evaluation metric for S-RRG, providing a more clinically relevant measure of report quality.", "date": "2025-08-04", "categories": []}
{"id": "2508.02556", "title": "Automated SNOMED CT Concept Annotation in Clinical Text Using Bi-GRU Neural Networks", "abstract": "Automated annotation of clinical text with standardized medical concepts is critical for enabling structured data extraction and decision support. SNOMED CT provides a rich ontology for labeling clinical entities, but manual annotation is labor-intensive and impractical at scale. This study introduces a neural sequence labeling approach for SNOMED CT concept recognition using a Bidirectional GRU model. Leveraging a subset of MIMIC-IV, we preprocess text with domain-adapted SpaCy and SciBERT-based tokenization, segmenting sentences into overlapping 19-token chunks enriched with contextual, syntactic, and morphological features. The Bi-GRU model assigns IOB tags to identify concept spans and achieves strong performance with a 90 percent F1-score on the validation set. These results surpass traditional rule-based systems and match or exceed existing neural models. Qualitative analysis shows effective handling of ambiguous terms and misspellings. Our findings highlight that lightweight RNN-based architectures can deliver high-quality clinical concept annotation with significantly lower computational cost than transformer-based models, making them well-suited for real-world deployment.", "date": "2025-08-04", "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": "2508.00695", "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": "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": "2507.22943", "title": "A chart review process aided by natural language processing and multi-wave adaptive sampling to expedite validation of code-based algorithms for large database studies", "abstract": "Background: One of the ways to enhance analyses conducted with large claims databases is by validating the measurement characteristics of code-based algorithms used to identify health outcomes or other key study parameters of interest. These metrics can be used in quantitative bias analyses to assess the robustness of results for an inferential study given potential bias from outcome misclassification. However, extensive time and resource allocation are typically re-quired to create reference-standard labels through manual chart review of free-text notes from linked electronic health records. Methods: We describe an expedited process that introduces efficiency in a validation study us-ing two distinct mechanisms: 1) use of natural language processing (NLP) to reduce time spent by human reviewers to review each chart, and 2) a multi-wave adaptive sampling approach with pre-defined criteria to stop the validation study once performance characteristics are identified with sufficient precision. We illustrate this process in a case study that validates the performance of a claims-based outcome algorithm for intentional self-harm in patients with obesity. Results: We empirically demonstrate that the NLP-assisted annotation process reduced the time spent on review per chart by 40% and use of the pre-defined stopping rule with multi-wave samples would have prevented review of 77% of patient charts with limited compromise to precision in derived measurement characteristics. Conclusion: This approach could facilitate more routine validation of code-based algorithms used to define key study parameters, ultimately enhancing understanding of the reliability of find-ings derived from database studies.", "date": "2025-07-25", "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": "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": "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": "2507.19899", "title": "A Gold Standard Dataset and Evaluation Framework for Depression Detection and Explanation in Social Media using LLMs", "abstract": "Early detection of depression from online social media posts holds promise for providing timely mental health interventions. In this work, we present a high-quality, expert-annotated dataset of 1,017 social media posts labeled with depressive spans and mapped to 12 depression symptom categories. Unlike prior datasets that primarily offer coarse post-level labels \\cite{cohan-etal-2018-smhd}, our dataset enables fine-grained evaluation of both model predictions and generated explanations.\n  We develop an evaluation framework that leverages this clinically grounded dataset to assess the faithfulness and quality of natural language explanations generated by large language models (LLMs). Through carefully designed prompting strategies, including zero-shot and few-shot approaches with domain-adapted examples, we evaluate state-of-the-art proprietary LLMs including GPT-4.1, Gemini 2.5 Pro, and Claude 3.7 Sonnet.\n  Our comprehensive empirical analysis reveals significant differences in how these models perform on clinical explanation tasks, with zero-shot and few-shot prompting. Our findings underscore the value of human expertise in guiding LLM behavior and offer a step toward safer, more transparent AI systems for psychological well-being.", "date": "2025-07-26", "categories": []}
{"id": "2507.19973", "title": "Leveraging Fine-Tuned Large Language Models for Interpretable Pancreatic Cystic Lesion Feature Extraction and Risk Categorization", "abstract": "Background: Manual extraction of pancreatic cystic lesion (PCL) features from radiology reports is labor-intensive, limiting large-scale studies needed to advance PCL research. Purpose: To develop and evaluate large language models (LLMs) that automatically extract PCL features from MRI/CT reports and assign risk categories based on guidelines. Materials and Methods: We curated a training dataset of 6,000 abdominal MRI/CT reports (2005-2024) from 5,134 patients that described PCLs. Labels were generated by GPT-4o using chain-of-thought (CoT) prompting to extract PCL and main pancreatic duct features. Two open-source LLMs were fine-tuned using QLoRA on GPT-4o-generated CoT data. Features were mapped to risk categories per institutional guideline based on the 2017 ACR White Paper. Evaluation was performed on 285 held-out human-annotated reports. Model outputs for 100 cases were independently reviewed by three radiologists. Feature extraction was evaluated using exact match accuracy, risk categorization with macro-averaged F1 score, and radiologist-model agreement with Fleiss' Kappa. Results: CoT fine-tuning improved feature extraction accuracy for LLaMA (80% to 97%) and DeepSeek (79% to 98%), matching GPT-4o (97%). Risk categorization F1 scores also improved (LLaMA: 0.95; DeepSeek: 0.94), closely matching GPT-4o (0.97), with no statistically significant differences. Radiologist inter-reader agreement was high (Fleiss' Kappa = 0.888) and showed no statistically significant difference with the addition of DeepSeek-FT-CoT (Fleiss' Kappa = 0.893) or GPT-CoT (Fleiss' Kappa = 0.897), indicating that both models achieved agreement levels on par with radiologists. Conclusion: Fine-tuned open-source LLMs with CoT supervision enable accurate, interpretable, and efficient phenotyping for large-scale PCL research, achieving performance comparable to GPT-4o.", "date": "2025-07-26", "categories": []}
{"id": "2505.18380", "title": "RedactOR: An LLM-Powered Framework for Automatic Clinical Data De-Identification", "abstract": "Ensuring clinical data privacy while preserving utility is critical for AI-driven healthcare and data analytics. Existing de-identification (De-ID) methods, including rule-based techniques, deep learning models, and large language models (LLMs), often suffer from recall errors, limited generalization, and inefficiencies, limiting their real-world applicability. We propose a fully automated, multi-modal framework, RedactOR for de-identifying structured and unstructured electronic health records, including clinical audio records. Our framework employs cost-efficient De-ID strategies, including intelligent routing, hybrid rule and LLM based approaches, and a two-step audio redaction approach. We present a retrieval-based entity relexicalization approach to ensure consistent substitutions of protected entities, thereby enhancing data coherence for downstream applications. We discuss key design desiderata, de-identification and relexicalization methodology, and modular architecture of RedactOR and its integration with the Oracle Health Clinical AI system. Evaluated on the i2b2 2014 De-ID dataset using standard metrics with strict recall, our approach achieves competitive performance while optimizing token usage to reduce LLM costs. Finally, we discuss key lessons and insights from deployment in real-world AI- driven healthcare data pipelines.", "date": "2025-05-23", "categories": []}
{"id": "2507.08013", "title": "MedicalBERT: enhancing biomedical natural language processing using pretrained BERT-based model", "abstract": "Recent advances in natural language processing (NLP) have been driven bypretrained language models like BERT, RoBERTa, T5, and GPT. Thesemodels excel at understanding complex texts, but biomedical literature, withits domain-specific terminology, poses challenges that models likeWord2Vec and bidirectional long short-term memory (Bi-LSTM) can't fullyaddress. GPT and T5, despite capturing context, fall short in tasks needingbidirectional understanding, unlike BERT. Addressing this, we proposedMedicalBERT, a pretrained BERT model trained on a large biomedicaldataset and equipped with domain-specific vocabulary that enhances thecomprehension of biomedical terminology. MedicalBERT model is furtheroptimized and fine-tuned to address diverse tasks, including named entityrecognition, relation extraction, question answering, sentence similarity, anddocument classification. Performance metrics such as the F1-score,accuracy, and Pearson correlation are employed to showcase the efficiencyof our model in comparison to other BERT-based models such as BioBERT,SciBERT, and ClinicalBERT. MedicalBERT outperforms these models onmost of the benchmarks, and surpasses the general-purpose BERT model by5.67% on average across all the tasks evaluated respectively. This work alsounderscores the potential of leveraging pretrained BERT models for medicalNLP tasks, demonstrating the effectiveness of transfer learning techniques incapturing domain-specific information.\n  (PDF) MedicalBERT: enhancing biomedical natural language processing using pretrained BERT-based model. Available from: https://www.researchgate.net/publication/392489050_MedicalBERT_enhancing_biomedical_natural_language_processing_using_pretrained_BERT-based_model [accessed Jul 06 2025].", "date": "2025-07-06", "categories": []}
{"id": "2507.17754", "title": "A Custom-Built Ambient Scribe Reduces Cognitive Load and Documentation Burden for Telehealth Clinicians", "abstract": "Clinician burnout has motivated the growing adoption of ambient medical scribes in the clinic. In this work, we introduce a custom-built ambient scribe application integrated into the EHR system at Included Health, a personalized all-in-one healthcare company offering telehealth services. The application uses Whisper for transcription and a modular in-context learning pipeline with GPT-4o to automatically generate SOAP notes and patient instructions. Testing on mock visit data shows that the notes generated by the application exceed the quality of expert-written notes as determined by an LLM-as-a-judge. The application has been widely adopted by the clinical practice, with over 540 clinicians at Included Health using the application at least once. 94% (n = 63) of surveyed clinicians report reduced cognitive load during visits and 97% (n = 66) report less documentation burden when using the application. Additionally, we show that post-processing notes with a fine-tuned BART model improves conciseness. These findings highlight the potential for AI systems to ease administrative burdens and support clinicians in delivering efficient, high-quality care.", "date": "2025-05-02", "categories": []}
{"id": "2507.18123", "title": "Actively evaluating and learning the distinctions that matter: Vaccine safety signal detection from emergency triage notes", "abstract": "The rapid development of COVID-19 vaccines has showcased the global communitys ability to combat infectious diseases. However, the need for post-licensure surveillance systems has grown due to the limited window for safety data collection in clinical trials and early widespread implementation. This study aims to employ Natural Language Processing techniques and Active Learning to rapidly develop a classifier that detects potential vaccine safety issues from emergency department notes. ED triage notes, containing expert, succinct vital patient information at the point of entry to health systems, can significantly contribute to timely vaccine safety signal surveillance. While keyword-based classification can be effective, it may yield false positives and demand extensive keyword modifications. This is exacerbated by the infrequency of vaccination-related ED presentations and their similarity to other reasons for ED visits. NLP offers a more accurate and efficient alternative, albeit requiring annotated data, which is often scarce in the medical field. Active learning optimizes the annotation process and the quality of annotated data, which can result in faster model implementation and improved model performance. This work combines active learning, data augmentation, and active learning and evaluation techniques to create a classifier that is used to enhance vaccine safety surveillance from ED triage notes.", "date": "2025-07-24", "categories": []}
{"id": "2507.18451", "title": "Generation of Synthetic Clinical Text: A Systematic Review", "abstract": "Generating clinical synthetic text represents an effective solution for common clinical NLP issues like sparsity and privacy. This paper aims to conduct a systematic review on generating synthetic medical free-text by formulating quantitative analysis to three research questions concerning (i) the purpose of generation, (ii) the techniques, and (iii) the evaluation methods. We searched PubMed, ScienceDirect, Web of Science, Scopus, IEEE, Google Scholar, and arXiv databases for publications associated with generating synthetic medical unstructured free-text. We have identified 94 relevant articles out of 1,398 collected ones. A great deal of attention has been given to the generation of synthetic medical text from 2018 onwards, where the main purpose of such a generation is towards text augmentation, assistive writing, corpus building, privacy-preserving, annotation, and usefulness. Transformer architectures were the main predominant technique used to generate the text, especially the GPTs. On the other hand, there were four main aspects of evaluation, including similarity, privacy, structure, and utility, where utility was the most frequent method used to assess the generated synthetic medical text. Although the generated synthetic medical text demonstrated a moderate possibility to act as real medical documents in different downstream NLP tasks, it has proven to be a great asset as augmented, complementary to the real documents, towards improving the accuracy and overcoming sparsity/undersampling issues. Yet, privacy is still a major issue behind generating synthetic medical text, where more human assessments are needed to check for the existence of any sensitive information. Despite that, advances in generating synthetic medical text will considerably accelerate the adoption of workflows and pipeline development, discarding the time-consuming legalities of data transfer.", "date": "2025-07-24", "categories": []}
{"id": "2507.18542", "title": "Effective Multi-Task Learning for Biomedical Named Entity Recognition", "abstract": "Biomedical Named Entity Recognition presents significant challenges due to the complexity of biomedical terminology and inconsistencies in annotation across datasets. This paper introduces SRU-NER (Slot-based Recurrent Unit NER), a novel approach designed to handle nested named entities while integrating multiple datasets through an effective multi-task learning strategy. SRU-NER mitigates annotation gaps by dynamically adjusting loss computation to avoid penalizing predictions of entity types absent in a given dataset. Through extensive experiments, including a cross-corpus evaluation and human assessment of the model's predictions, SRU-NER achieves competitive performance in biomedical and general-domain NER tasks, while improving cross-domain generalization.", "date": "2025-07-24", "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": "2309.10770", "title": "FRASIMED: a Clinical French Annotated Resource Produced through Crosslingual BERT-Based Annotation Projection", "abstract": "Natural language processing (NLP) applications such as named entity recognition (NER) for low-resource corpora do not benefit from recent advances in the development of large language models (LLMs) where there is still a need for larger annotated datasets. This research article introduces a methodology for generating translated versions of annotated datasets through crosslingual annotation projection. Leveraging a language agnostic BERT-based approach, it is an efficient solution to increase low-resource corpora with few human efforts and by only using already available open data resources. Quantitative and qualitative evaluations are often lacking when it comes to evaluating the quality and effectiveness of semi-automatic data generation strategies. The evaluation of our crosslingual annotation projection approach showed both effectiveness and high accuracy in the resulting dataset. As a practical application of this methodology, we present the creation of French Annotated Resource with Semantic Information for Medical Entities Detection (FRASIMED), an annotated corpus comprising 2'051 synthetic clinical cases in French. The corpus is now available for researchers and practitioners to develop and refine French natural language processing (NLP) applications in the clinical field (https://zenodo.org/record/8355629), making it the largest open annotated corpus with linked medical concepts in French.", "date": "2023-09-19", "categories": []}
{"id": "2410.16543", "title": "Large Language Models Powered Multiagent Ensemble for Mitigating Hallucination and Efficient Atrial Fibrillation Annotation of ECG Reports", "abstract": "This study introduces a LLMs powered multiagent ensemble method to address challenges in hallucination and data labeling, particularly in large-scale EHR datasets. Manual labeling of such datasets requires domain expertise and is labor-intensive, time-consuming, expensive, and error-prone. To overcome this bottleneck, we developed an ensemble LLMs method and demonstrated its effectiveness in two real-world tasks: (1) labeling a large-scale unlabeled ECG dataset in MIMIC-IV; (2) identifying social determinants of health (SDOH) from the clinical notes of EHR. Trading off benefits and cost, we selected a pool of diverse open source LLMs with satisfactory performance. We treat each LLM's prediction as a vote and apply a mechanism of majority voting with minimal winning threshold for ensemble. We implemented an ensemble LLMs application for EHR data labeling tasks. By using the ensemble LLMs and natural language processing, we labeled MIMIC-IV ECG dataset of 623,566 ECG reports with an estimated accuracy of 98.2%. We applied the ensemble LLMs method to identify SDOH from social history sections of 1,405 EHR clinical notes, also achieving competitive performance. Our experiments show that the ensemble LLMs can outperform individual LLM even the best commercial one, and the method reduces hallucination errors. From the research, we found that (1) the ensemble LLMs method significantly reduces the time and effort required for labeling large-scale EHR data, automating the process with high accuracy and quality; (2) the method generalizes well to other text data labeling tasks, as shown by its application to SDOH identification; (3) the ensemble of a group of diverse LLMs can outperform or match the performance of the best individual LLM; and (4) the ensemble method substantially reduces hallucination errors. This approach provides a scalable and efficient solution to data-labeling challenges.", "date": "2024-10-21", "categories": []}
{"id": "2501.08208", "title": "ASTRID -- An Automated and Scalable TRIaD for the Evaluation of RAG-based Clinical Question Answering Systems", "abstract": "Large Language Models (LLMs) have shown impressive potential in clinical question answering (QA), with Retrieval Augmented Generation (RAG) emerging as a leading approach for ensuring the factual accuracy of model responses. However, current automated RAG metrics perform poorly in clinical and conversational use cases. Using clinical human evaluations of responses is expensive, unscalable, and not conducive to the continuous iterative development of RAG systems. To address these challenges, we introduce ASTRID - an Automated and Scalable TRIaD for evaluating clinical QA systems leveraging RAG - consisting of three metrics: Context Relevance (CR), Refusal Accuracy (RA), and Conversational Faithfulness (CF). Our novel evaluation metric, CF, is designed to better capture the faithfulness of a model's response to the knowledge base without penalising conversational elements. To validate our triad, we curate a dataset of over 200 real-world patient questions posed to an LLM-based QA agent during surgical follow-up for cataract surgery - the highest volume operation in the world - augmented with clinician-selected questions for emergency, clinical, and non-clinical out-of-domain scenarios. We demonstrate that CF can predict human ratings of faithfulness better than existing definitions for conversational use cases. Furthermore, we show that evaluation using our triad consisting of CF, RA, and CR exhibits alignment with clinician assessment for inappropriate, harmful, or unhelpful responses. Finally, using nine different LLMs, we demonstrate that the three metrics can closely agree with human evaluations, highlighting the potential of these metrics for use in LLM-driven automated evaluation pipelines. We also publish the prompts and datasets for these experiments, providing valuable resources for further research and development.", "date": "2025-01-14", "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": "2507.14079", "title": "DENSE: Longitudinal Progress Note Generation with Temporal Modeling of Heterogeneous Clinical Notes Across Hospital Visits", "abstract": "Progress notes are among the most clinically meaningful artifacts in an Electronic Health Record (EHR), offering temporally grounded insights into a patient's evolving condition, treatments, and care decisions. Despite their importance, they are severely underrepresented in large-scale EHR datasets. For instance, in the widely used Medical Information Mart for Intensive Care III (MIMIC-III) dataset, only about $8.56\\%$ of hospital visits include progress notes, leaving gaps in longitudinal patient narratives. In contrast, the dataset contains a diverse array of other note types, each capturing different aspects of care.\n  We present DENSE (Documenting Evolving Progress Notes from Scattered Evidence), a system designed to align with clinical documentation workflows by simulating how physicians reference past encounters while drafting progress notes. The system introduces a fine-grained note categorization and a temporal alignment mechanism that organizes heterogeneous notes across visits into structured, chronological inputs. At its core, DENSE leverages a clinically informed retrieval strategy to identify temporally and semantically relevant content from both current and prior visits. This retrieved evidence is used to prompt a large language model (LLM) to generate clinically coherent and temporally aware progress notes.\n  We evaluate DENSE on a curated cohort of patients with multiple visits and complete progress note documentation. The generated notes demonstrate strong longitudinal fidelity, achieving a temporal alignment ratio of $1.089$, surpassing the continuity observed in original notes. By restoring narrative coherence across fragmented documentation, our system supports improved downstream tasks such as summarization, predictive modeling, and clinical decision support, offering a scalable solution for LLM-driven note synthesis in real-world healthcare settings.", "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": "2507.12679", "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": "2507.10958", "title": "DS@GT at eRisk 2025: From prompts to predictions, benchmarking early depression detection with conversational agent based assessments and temporal attention models", "abstract": "This Working Note summarizes the participation of the DS@GT team in two eRisk 2025 challenges. For the Pilot Task on conversational depression detection with large language-models (LLMs), we adopted a prompt-engineering strategy in which diverse LLMs conducted BDI-II-based assessments and produced structured JSON outputs. Because ground-truth labels were unavailable, we evaluated cross-model agreement and internal consistency. Our prompt design methodology aligned model outputs with BDI-II criteria and enabled the analysis of conversational cues that influenced the prediction of symptoms. Our best submission, second on the official leaderboard, achieved DCHR = 0.50, ADODL = 0.89, and ASHR = 0.27.", "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": "2506.00200", "title": "Structuring Radiology Reports: Challenging LLMs with Lightweight Models", "abstract": "Radiology reports are critical for clinical decision-making but often lack a standardized format, limiting both human interpretability and machine learning (ML) applications. While large language models (LLMs) have shown strong capabilities in reformatting clinical text, their high computational requirements, lack of transparency, and data privacy concerns hinder practical deployment. To address these challenges, we explore lightweight encoder-decoder models (<300M parameters)-specifically T5 and BERT2BERT-for structuring radiology reports from the MIMIC-CXR and CheXpert Plus datasets. We benchmark these models against eight open-source LLMs (1B-70B), adapted using prefix prompting, in-context learning (ICL), and low-rank adaptation (LoRA) finetuning. Our best-performing lightweight model outperforms all LLMs adapted using prompt-based techniques on a human-annotated test set. While some LoRA-finetuned LLMs achieve modest gains over the lightweight model on the Findings section (BLEU 6.4%, ROUGE-L 4.8%, BERTScore 3.6%, F1-RadGraph 1.1%, GREEN 3.6%, and F1-SRR-BERT 4.3%), these improvements come at the cost of substantially greater computational resources. For example, LLaMA-3-70B incurred more than 400 times the inference time, cost, and carbon emissions compared to the lightweight model. These results underscore the potential of lightweight, task-specific models as sustainable and privacy-preserving solutions for structuring clinical text in resource-constrained healthcare settings.", "date": "2025-05-30", "categories": []}
{"id": "2507.08969", "title": "Application of CARE-SD text classifier tools to assess distribution of stigmatizing and doubt-marking language features in EHR", "abstract": "Introduction: Electronic health records (EHR) are a critical medium through which patient stigmatization is perpetuated among healthcare teams. Methods: We identified linguistic features of doubt markers and stigmatizing labels in MIMIC-III EHR via expanded lexicon matching and supervised learning classifiers. Predictors of rates of linguistic features were assessed using Poisson regression models. Results: We found higher rates of stigmatizing labels per chart among patients who were Black or African American (RR: 1.16), patients with Medicare/Medicaid or government-run insurance (RR: 2.46), self-pay (RR: 2.12), and patients with a variety of stigmatizing disease and mental health conditions. Patterns among doubt markers were similar, though male patients had higher rates of doubt markers (RR: 1.25). We found increased stigmatizing labels used by nurses (RR: 1.40), and social workers (RR: 2.25), with similar patterns of doubt markers. Discussion: Stigmatizing language occurred at higher rates among historically stigmatized patients, perpetuated by multiple provider types.", "date": "2025-07-11", "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": "2507.07421", "title": "SynthEHR-Eviction: Enhancing Eviction SDoH Detection with LLM-Augmented Synthetic EHR Data", "abstract": "Eviction is a significant yet understudied social determinants of health (SDoH), linked to housing instability, unemployment, and mental health. While eviction appears in unstructured electronic health records (EHRs), it is rarely coded in structured fields, limiting downstream applications. We introduce SynthEHR-Eviction, a scalable pipeline combining LLMs, human-in-the-loop annotation, and automated prompt optimization (APO) to extract eviction statuses from clinical notes. Using this pipeline, we created the largest public eviction-related SDoH dataset to date, comprising 14 fine-grained categories. Fine-tuned LLMs (e.g., Qwen2.5, LLaMA3) trained on SynthEHR-Eviction achieved Macro-F1 scores of 88.8% (eviction) and 90.3% (other SDoH) on human validated data, outperforming GPT-4o-APO (87.8%, 87.3%), GPT-4o-mini-APO (69.1%, 78.1%), and BioBERT (60.7%, 68.3%), while enabling cost-effective deployment across various model sizes. The pipeline reduces annotation effort by over 80%, accelerates dataset creation, enables scalable eviction detection, and generalizes to other information extraction tasks.", "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": "2502.11703", "title": "CMQCIC-Bench: A Chinese Benchmark for Evaluating Large Language Models in Medical Quality Control Indicator Calculation", "abstract": "Medical quality control indicators are essential to assess the qualifications of healthcare institutions for medical services. With the impressive performance of large language models (LLMs) like GPT-4 in the medical field, leveraging these technologies for the Medical Quality Control Indicator Calculation (MQCIC) presents a promising approach. In this work, (1) we introduce a real-world task MQCIC and propose an open-source Chinese electronic medical records (EMRs)-based dataset (CMQCIC-Bench) comprising 785 instances and 76 indicators. (2) We propose a semi-automatic method to enhance the rule representation. Then we propose the Clinical Facts-based Inferential Rule (CF-IR) method that disentangles the clinical fact verification and inferential rule reasoning actions. (3) We conduct comprehensive experiments on 20 representative LLMs, covering general and medical models. Our findings reveal that CF-IR outperforms Chain-of-Thought methods in MQCIC tasks. (4) We conduct an error analysis and investigate the capabilities of clinical fact verification and inferential rule reasoning, providing insights to improve performance in the MQCIC further. The dataset and code is available in this repository https://github.com/YuY-2001/C-MQCIC.", "date": "2025-02-17", "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": "2502.05638", "title": "ELMTEX: Fine-Tuning Large Language Models for Structured Clinical Information Extraction. A Case Study on Clinical Reports", "abstract": "Europe's healthcare systems require enhanced interoperability and digitalization, driving a demand for innovative solutions to process legacy clinical data. This paper presents the results of our project, which aims to leverage Large Language Models (LLMs) to extract structured information from unstructured clinical reports, focusing on patient history, diagnoses, treatments, and other predefined categories. We developed a workflow with a user interface and evaluated LLMs of varying sizes through prompting strategies and fine-tuning. Our results show that fine-tuned smaller models match or surpass larger counterparts in performance, offering efficiency for resource-limited settings. A new dataset of 60,000 annotated English clinical summaries and 24,000 German translations was validated with automated and manual checks. The evaluations used ROUGE, BERTScore, and entity-level metrics. The work highlights the approach's viability and outlines future improvements.", "date": "2025-02-08", "categories": []}
{"id": "2507.05319", "title": "LCDS: A Logic-Controlled Discharge Summary Generation System Supporting Source Attribution and Expert Review", "abstract": "Despite the remarkable performance of Large Language Models (LLMs) in automated discharge summary generation, they still suffer from hallucination issues, such as generating inaccurate content or fabricating information without valid sources. In addition, electronic medical records (EMRs) typically consist of long-form data, making it challenging for LLMs to attribute the generated content to the sources. To address these challenges, we propose LCDS, a Logic-Controlled Discharge Summary generation system. LCDS constructs a source mapping table by calculating textual similarity between EMRs and discharge summaries to constrain the scope of summarized content. Moreover, LCDS incorporates a comprehensive set of logical rules, enabling it to generate more reliable silver discharge summaries tailored to different clinical fields. Furthermore, LCDS supports source attribution for generated content, allowing experts to efficiently review, provide feedback, and rectify errors. The resulting golden discharge summaries are subsequently recorded for incremental fine-tuning of LLMs. Our project and demo video are in the GitHub repository https://github.com/ycycyc02/LCDS.", "date": "2025-07-07", "categories": []}
{"id": "2507.05816", "title": "Affective-ROPTester: Capability and Bias Analysis of LLMs in Predicting Retinopathy of Prematurity", "abstract": "Despite the remarkable progress of large language models (LLMs) across various domains, their capacity to predict retinopathy of prematurity (ROP) risk remains largely unexplored. To address this gap, we introduce a novel Chinese benchmark dataset, termed CROP, comprising 993 admission records annotated with low, medium, and high-risk labels. To systematically examine the predictive capabilities and affective biases of LLMs in ROP risk stratification, we propose Affective-ROPTester, an automated evaluation framework incorporating three prompting strategies: Instruction-based, Chain-of-Thought (CoT), and In-Context Learning (ICL). The Instruction scheme assesses LLMs' intrinsic knowledge and associated biases, whereas the CoT and ICL schemes leverage external medical knowledge to enhance predictive accuracy. Crucially, we integrate emotional elements at the prompt level to investigate how different affective framings influence the model's ability to predict ROP and its bias patterns. Empirical results derived from the CROP dataset yield two principal observations. First, LLMs demonstrate limited efficacy in ROP risk prediction when operating solely on intrinsic knowledge, yet exhibit marked performance gains when augmented with structured external inputs. Second, affective biases are evident in the model outputs, with a consistent inclination toward overestimating medium- and high-risk cases. Third, compared to negative emotions, positive emotional framing contributes to mitigating predictive bias in model outputs. These findings highlight the critical role of affect-sensitive prompt engineering in enhancing diagnostic reliability and emphasize the utility of Affective-ROPTester as a framework for evaluating and mitigating affective bias in clinical language modeling systems.", "date": "2025-07-08", "categories": []}
{"id": "2507.03001", "title": "Evaluating Hierarchical Clinical Document Classification Using Reasoning-Based LLMs", "abstract": "This study evaluates how well large language models (LLMs) can classify ICD-10 codes from hospital discharge summaries, a critical but error-prone task in healthcare. Using 1,500 summaries from the MIMIC-IV dataset and focusing on the 10 most frequent ICD-10 codes, the study tested 11 LLMs, including models with and without structured reasoning capabilities. Medical terms were extracted using a clinical NLP tool (cTAKES), and models were prompted in a consistent, coder-like format. None of the models achieved an F1 score above 57%, with performance dropping as code specificity increased. Reasoning-based models generally outperformed non-reasoning ones, with Gemini 2.5 Pro performing best overall. Some codes, such as those related to chronic heart disease, were classified more accurately than others. The findings suggest that while LLMs can assist human coders, they are not yet reliable enough for full automation. Future work should explore hybrid methods, domain-specific model training, and the use of structured clinical data.", "date": "2025-07-02", "categories": []}
{"id": "2507.03033", "title": "Preserving Privacy, Increasing Accessibility, and Reducing Cost: An On-Device Artificial Intelligence Model for Medical Transcription and Note Generation", "abstract": "Background: Clinical documentation represents a significant burden for healthcare providers, with physicians spending up to 2 hours daily on administrative tasks. Recent advances in large language models (LLMs) offer promising solutions, but privacy concerns and computational requirements limit their adoption in healthcare settings. Objective: To develop and evaluate a privacy-preserving, on-device medical transcription system using a fine-tuned Llama 3.2 1B model capable of generating structured medical notes from medical transcriptions while maintaining complete data sovereignty entirely in the browser. Methods: We fine-tuned a Llama 3.2 1B model using Parameter-Efficient Fine-Tuning (PEFT) with LoRA on 1,500 synthetic medical transcription-to-structured note pairs. The model was evaluated against the base Llama 3.2 1B on two datasets: 100 endocrinology transcripts and 140 modified ACI benchmark cases. Evaluation employed both statistical metrics (ROUGE, BERTScore, BLEURT) and LLM-as-judge assessments across multiple clinical quality dimensions. Results: The fine-tuned OnDevice model demonstrated substantial improvements over the base model. On the ACI benchmark, ROUGE-1 scores increased from 0.346 to 0.496, while BERTScore F1 improved from 0.832 to 0.866. Clinical quality assessments showed marked reduction in major hallucinations (from 85 to 35 cases) and enhanced factual correctness (2.81 to 3.54 on 5-point scale). Similar improvements were observed on the internal evaluation dataset, with composite scores increasing from 3.13 to 4.43 (+41.5%). Conclusions: Fine-tuning compact LLMs for medical transcription yields clinically meaningful improvements while enabling complete on-device browser deployment. This approach addresses key barriers to AI adoption in healthcare: privacy preservation, cost reduction, and accessibility for resource-constrained environments.", "date": "2025-07-03", "categories": []}
{"id": "2507.03433", "title": "Improving Social Determinants of Health Documentation in French EHRs Using Large Language Models", "abstract": "Social determinants of health (SDoH) significantly influence health outcomes, shaping disease progression, treatment adherence, and health disparities. However, their documentation in structured electronic health records (EHRs) is often incomplete or missing. This study presents an approach based on large language models (LLMs) for extracting 13 SDoH categories from French clinical notes. We trained Flan-T5-Large on annotated social history sections from clinical notes at Nantes University Hospital, France. We evaluated the model at two levels: (i) identification of SDoH categories and associated values, and (ii) extraction of detailed SDoH with associated temporal and quantitative information. The model performance was assessed across four datasets, including two that we publicly release as open resources. The model achieved strong performance for identifying well-documented categories such as living condition, marital status, descendants, job, tobacco, and alcohol use (F1 score > 0.80). Performance was lower for categories with limited training data or highly variable expressions, such as employment status, housing, physical activity, income, and education. Our model identified 95.8% of patients with at least one SDoH, compared to 2.8% for ICD-10 codes from structured EHR data. Our error analysis showed that performance limitations were linked to annotation inconsistencies, reliance on English-centric tokenizer, and reduced generalizability due to the model being trained on social history sections only. These results demonstrate the effectiveness of NLP in improving the completeness of real-world SDoH data in a non-English EHR system.", "date": "2025-07-04", "categories": []}
{"id": "2507.04142", "title": "Dissecting Clinical Reasoning in Language Models: A Comparative Study of Prompts and Model Adaptation Strategies", "abstract": "Recent works on large language models (LLMs) have demonstrated the impact of prompting strategies and fine-tuning techniques on their reasoning capabilities. Yet, their effectiveness on clinical natural language inference (NLI) remains underexplored. This study presents the first controlled evaluation of how prompt structure and efficient fine-tuning jointly shape model performance in clinical NLI. We inspect four classes of prompting strategies to elicit reasoning in LLMs at different levels of abstraction, and evaluate their impact on a range of clinically motivated reasoning types. For each prompting strategy, we construct high-quality demonstrations using a frontier model to distil multi-step reasoning capabilities into smaller models (4B parameters) via Low-Rank Adaptation (LoRA). Across different language models fine-tuned on the NLI4CT benchmark, we found that prompt type alone accounts for up to 44% of the variance in macro-F1. Moreover, LoRA fine-tuning yields consistent gains of +8 to 12 F1, raises output alignment above 97%, and narrows the performance gap to GPT-4o-mini to within 7.1%. Additional experiments on reasoning generalisation reveal that LoRA improves performance in 75% of the models on MedNLI and TREC Clinical Trials Track. Overall, these findings demonstrate that (i) prompt structure is a primary driver of clinical reasoning performance, (ii) compact models equipped with strong prompts and LoRA can rival frontier-scale systems, and (iii) reasoning-type-aware evaluation is essential to uncover prompt-induced trade-offs. Our results highlight the promise of combining prompt design and lightweight adaptation for more efficient and trustworthy clinical NLP systems, providing insights on the strengths and limitations of widely adopted prompting and parameter-efficient techniques in highly specialised domains.", "date": "2025-07-05", "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": "2408.01933", "title": "DiReCT: Diagnostic Reasoning for Clinical Notes via Large Language Models", "abstract": "Large language models (LLMs) have recently showcased remarkable capabilities, spanning a wide range of tasks and applications, including those in the medical domain. Models like GPT-4 excel in medical question answering but may face challenges in the lack of interpretability when handling complex tasks in real clinical settings. We thus introduce the diagnostic reasoning dataset for clinical notes (DiReCT), aiming at evaluating the reasoning ability and interpretability of LLMs compared to human doctors. It contains 511 clinical notes, each meticulously annotated by physicians, detailing the diagnostic reasoning process from observations in a clinical note to the final diagnosis. Additionally, a diagnostic knowledge graph is provided to offer essential knowledge for reasoning, which may not be covered in the training data of existing LLMs. Evaluations of leading LLMs on DiReCT bring out a significant gap between their reasoning ability and that of human doctors, highlighting the critical need for models that can reason effectively in real-world clinical scenarios.", "date": "2024-08-04", "categories": []}
{"id": "2411.03805", "title": "A Comparative Study of Recent Large Language Models on Generating Hospital Discharge Summaries for Lung Cancer Patients", "abstract": "Generating discharge summaries is a crucial yet time-consuming task in clinical practice, essential for conveying pertinent patient information and facilitating continuity of care. Recent advancements in large language models (LLMs) have significantly enhanced their capability in understanding and summarizing complex medical texts. This research aims to explore how LLMs can alleviate the burden of manual summarization, streamline workflow efficiencies, and support informed decision-making in healthcare settings. Clinical notes from a cohort of 1,099 lung cancer patients were utilized, with a subset of 50 patients for testing purposes, and 102 patients used for model fine-tuning. This study evaluates the performance of multiple LLMs, including GPT-3.5, GPT-4, GPT-4o, and LLaMA 3 8b, in generating discharge summaries. Evaluation metrics included token-level analysis (BLEU, ROUGE-1, ROUGE-2, ROUGE-L) and semantic similarity scores between model-generated summaries and physician-written gold standards. LLaMA 3 8b was further tested on clinical notes of varying lengths to examine the stability of its performance. The study found notable variations in summarization capabilities among LLMs. GPT-4o and fine-tuned LLaMA 3 demonstrated superior token-level evaluation metrics, while LLaMA 3 consistently produced concise summaries across different input lengths. Semantic similarity scores indicated GPT-4o and LLaMA 3 as leading models in capturing clinical relevance. This study contributes insights into the efficacy of LLMs for generating discharge summaries, highlighting LLaMA 3's robust performance in maintaining clarity and relevance across varying clinical contexts. These findings underscore the potential of automated summarization tools to enhance documentation precision and efficiency, ultimately improving patient care and operational capability in healthcare settings.", "date": "2024-11-06", "categories": []}
{"id": "2502.04397", "title": "Multimodal Medical Code Tokenizer", "abstract": "Foundation models trained on patient electronic health records (EHRs) require tokenizing medical data into sequences of discrete vocabulary items. Existing tokenizers treat medical codes from EHRs as isolated textual tokens. However, each medical code is defined by its textual description, its position in ontological hierarchies, and its relationships to other codes, such as disease co-occurrences and drug-treatment associations. Medical vocabularies contain more than 600,000 codes with critical information for clinical reasoning. We introduce MedTok, a multimodal medical code tokenizer that uses the text descriptions and relational context of codes. MedTok processes text using a language model encoder and encodes the relational structure with a graph encoder. It then quantizes both modalities into a unified token space, preserving modality-specific and cross-modality information. We integrate MedTok into five EHR models and evaluate it on operational and clinical tasks across in-patient and out-patient datasets, including outcome prediction, diagnosis classification, drug recommendation, and risk stratification. Swapping standard EHR tokenizers with MedTok improves AUPRC across all EHR models, by 4.10% on MIMIC-III, 4.78% on MIMIC-IV, and 11.32% on EHRShot, with the largest gains in drug recommendation. Beyond EHR modeling, we demonstrate using MedTok tokenizer with medical QA systems. Our results demonstrate the potential of MedTok as a unified tokenizer for medical codes, improving tokenization for medical foundation models.", "date": "2025-02-06", "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": "2503.22277", "title": "CFiCS: Graph-Based Classification of Common Factors and Microcounseling Skills", "abstract": "Common factors and microcounseling skills are critical to the effectiveness of psychotherapy. Understanding and measuring these elements provides valuable insights into therapeutic processes and outcomes. However, automatic identification of these change principles from textual data remains challenging due to the nuanced and context-dependent nature of therapeutic dialogue. This paper introduces CFiCS, a hierarchical classification framework integrating graph machine learning with pretrained contextual embeddings. We represent common factors, intervention concepts, and microcounseling skills as a heterogeneous graph, where textual information from ClinicalBERT enriches each node. This structure captures both the hierarchical relationships (e.g., skill-level nodes linking to broad factors) and the semantic properties of therapeutic concepts. By leveraging graph neural networks, CFiCS learns inductive node embeddings that generalize to unseen text samples lacking explicit connections. Our results demonstrate that integrating ClinicalBERT node features and graph structure significantly improves classification performance, especially in fine-grained skill prediction. CFiCS achieves substantial gains in both micro and macro F1 scores across all tasks compared to baselines, including random forests, BERT-based multi-task models, and graph-based methods.", "date": "2025-03-28", "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": "2506.23315", "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": "2502.11095", "title": "A Survey of Large Language Models in Psychotherapy: Current Landscape and Future Directions", "abstract": "Mental health is increasingly critical in contemporary healthcare, with psychotherapy demanding dynamic, context-sensitive interactions that traditional NLP methods struggle to capture. Large Language Models (LLMs) offer significant potential for addressing this gap due to their ability to handle extensive context and multi-turn reasoning. This review introduces a conceptual taxonomy dividing psychotherapy into interconnected stages--assessment, diagnosis, and treatment--to systematically examine LLM advancements and challenges. Our comprehensive analysis reveals imbalances in current research, such as a focus on common disorders, linguistic biases, fragmented methods, and limited theoretical integration. We identify critical challenges including capturing dynamic symptom fluctuations, overcoming linguistic and cultural biases, and ensuring diagnostic reliability. Highlighting future directions, we advocate for continuous multi-stage modeling, real-time adaptive systems grounded in psychological theory, and diversified research covering broader mental disorders and therapeutic approaches, aiming toward more holistic and clinically integrated psychotherapy LLMs systems.", "date": "2025-02-16", "categories": []}
{"id": "2506.21597", "title": "Overview of the ClinIQLink 2025 Shared Task on Medical Question-Answering", "abstract": "In this paper, we present an overview of ClinIQLink, a shared task, collocated with the 24th BioNLP workshop at ACL 2025, designed to stress-test large language models (LLMs) on medically-oriented question answering aimed at the level of a General Practitioner. The challenge supplies 4,978 expert-verified, medical source-grounded question-answer pairs that cover seven formats: true/false, multiple choice, unordered list, short answer, short-inverse, multi-hop, and multi-hop-inverse. Participating systems, bundled in Docker or Apptainer images, are executed on the CodaBench platform or the University of Maryland's Zaratan cluster. An automated harness (Task 1) scores closed-ended items by exact match and open-ended items with a three-tier embedding metric. A subsequent physician panel (Task 2) audits the top model responses.", "date": "2025-06-18", "categories": []}
{"id": "2506.20112", "title": "A Multi-Pass Large Language Model Framework for Precise and Efficient Radiology Report Error Detection", "abstract": "Background: The positive predictive value (PPV) of large language model (LLM)-based proofreading for radiology reports is limited due to the low error prevalence. Purpose: To assess whether a three-pass LLM framework enhances PPV and reduces operational costs compared with baseline approaches. Materials and Methods: A retrospective analysis was performed on 1,000 consecutive radiology reports (250 each: radiography, ultrasonography, CT, MRI) from the MIMIC-III database. Two external datasets (CheXpert and Open-i) were validation sets. Three LLM frameworks were tested: (1) single-prompt detector; (2) extractor plus detector; and (3) extractor, detector, and false-positive verifier. Precision was measured by PPV and absolute true positive rate (aTPR). Efficiency was calculated from model inference charges and reviewer remuneration. Statistical significance was tested using cluster bootstrap, exact McNemar tests, and Holm-Bonferroni correction. Results: Framework PPV increased from 0.063 (95% CI, 0.036-0.101, Framework 1) to 0.079 (0.049-0.118, Framework 2), and significantly to 0.159 (0.090-0.252, Framework 3; P<.001 vs. baselines). aTPR remained stable (0.012-0.014; P>=.84). Operational costs per 1,000 reports dropped to USD 5.58 (Framework 3) from USD 9.72 (Framework 1) and USD 6.85 (Framework 2), reflecting reductions of 42.6% and 18.5%, respectively. Human-reviewed reports decreased from 192 to 88. External validation supported Framework 3's superior PPV (CheXpert 0.133, Open-i 0.105) and stable aTPR (0.007). Conclusion: A three-pass LLM framework significantly enhanced PPV and reduced operational costs, maintaining detection performance, providing an effective strategy for AI-assisted radiology report quality assurance.", "date": "2025-06-25", "categories": []}
{"id": "2506.19525", "title": "Automatic Posology Structuration : What role for LLMs?", "abstract": "Automatically structuring posology instructions is essential for improving medication safety and enabling clinical decision support. In French prescriptions, these instructions are often ambiguous, irregular, or colloquial, limiting the effectiveness of classic ML pipelines. We explore the use of Large Language Models (LLMs) to convert free-text posologies into structured formats, comparing prompt-based methods and fine-tuning against a \"pre-LLM\" system based on Named Entity Recognition and Linking (NERL). Our results show that while prompting improves performance, only fine-tuned LLMs match the accuracy of the baseline. Through error analysis, we observe complementary strengths: NERL offers structural precision, while LLMs better handle semantic nuances. Based on this, we propose a hybrid pipeline that routes low-confidence cases from NERL (<0.8) to the LLM, selecting outputs based on confidence scores. This strategy achieves 91% structuration accuracy while minimizing latency and compute. Our results show that this hybrid approach improves structuration accuracy while limiting computational cost, offering a scalable solution for real-world clinical use.", "date": "2025-06-24", "categories": []}
{"id": "2506.19702", "title": "LLM-Driven Medical Document Analysis: Enhancing Trustworthy Pathology and Differential Diagnosis", "abstract": "Medical document analysis plays a crucial role in extracting essential clinical insights from unstructured healthcare records, supporting critical tasks such as differential diagnosis. Determining the most probable condition among overlapping symptoms requires precise evaluation and deep medical expertise. While recent advancements in large language models (LLMs) have significantly enhanced performance in medical document analysis, privacy concerns related to sensitive patient data limit the use of online LLMs services in clinical settings. To address these challenges, we propose a trustworthy medical document analysis platform that fine-tunes a LLaMA-v3 using low-rank adaptation, specifically optimized for differential diagnosis tasks. Our approach utilizes DDXPlus, the largest benchmark dataset for differential diagnosis, and demonstrates superior performance in pathology prediction and variable-length differential diagnosis compared to existing methods. The developed web-based platform allows users to submit their own unstructured medical documents and receive accurate, explainable diagnostic results. By incorporating advanced explainability techniques, the system ensures transparent and reliable predictions, fostering user trust and confidence. Extensive evaluations confirm that the proposed method surpasses current state-of-the-art models in predictive accuracy while offering practical utility in clinical settings. This work addresses the urgent need for reliable, explainable, and privacy-preserving artificial intelligence solutions, representing a significant advancement in intelligent medical document analysis for real-world healthcare applications. The code can be found at \\href{https://github.com/leitro/Differential-Diagnosis-LoRA}{https://github.com/leitro/Differential-Diagnosis-LoRA}.", "date": "2025-06-24", "categories": []}
{"id": "2409.00097", "title": "Large Language Models for Disease Diagnosis: A Scoping Review", "abstract": "Automatic disease diagnosis has become increasingly valuable in clinical practice. The advent of large language models (LLMs) has catalyzed a paradigm shift in artificial intelligence, with growing evidence supporting the efficacy of LLMs in diagnostic tasks. Despite the increasing attention in this field, a holistic view is still lacking. Many critical aspects remain unclear, such as the diseases and clinical data to which LLMs have been applied, the LLM techniques employed, and the evaluation methods used. In this article, we perform a comprehensive review of LLM-based methods for disease diagnosis. Our review examines the existing literature across various dimensions, including disease types and associated clinical specialties, clinical data, LLM techniques, and evaluation methods. Additionally, we offer recommendations for applying and evaluating LLMs for diagnostic tasks. Furthermore, we assess the limitations of current research and discuss future directions. To our knowledge, this is the first comprehensive review for LLM-based disease diagnosis.", "date": "2024-08-27", "categories": []}
{"id": "2506.18185", "title": "CareLab at #SMM4H-HeaRD 2025: Insomnia Detection and Food Safety Event Extraction with Domain-Aware Transformers", "abstract": "This paper presents our system for the SMM4H-HeaRD 2025 shared tasks, specifically Task 4 (Subtasks 1, 2a, and 2b) and Task 5 (Subtasks 1 and 2). Task 4 focused on detecting mentions of insomnia in clinical notes, while Task 5 addressed the extraction of food safety events from news articles. We participated in all subtasks and report key findings across them, with particular emphasis on Task 5 Subtask 1, where our system achieved strong performance-securing first place with an F1 score of 0.958 on the test set. To attain this result, we employed encoder-based models (e.g., RoBERTa), alongside GPT-4 for data augmentation. This paper outlines our approach, including preprocessing, model architecture, and subtask-specific adaptations", "date": "2025-06-22", "categories": []}
{"id": "2506.18387", "title": "Evaluating Causal Explanation in Medical Reports with LLM-Based and Human-Aligned Metrics", "abstract": "This study investigates how accurately different evaluation metrics capture the quality of causal explanations in automatically generated diagnostic reports. We compare six metrics: BERTScore, Cosine Similarity, BioSentVec, GPT-White, GPT-Black, and expert qualitative assessment across two input types: observation-based and multiple-choice-based report generation. Two weighting strategies are applied: one reflecting task-specific priorities, and the other assigning equal weights to all metrics. Our results show that GPT-Black demonstrates the strongest discriminative power in identifying logically coherent and clinically valid causal narratives. GPT-White also aligns well with expert evaluations, while similarity-based metrics diverge from clinical reasoning quality. These findings emphasize the impact of metric selection and weighting on evaluation outcomes, supporting the use of LLM-based evaluation for tasks requiring interpretability and causal reasoning.", "date": "2025-06-23", "categories": []}
{"id": "2506.16247", "title": "Comparative Analysis of Abstractive Summarization Models for Clinical Radiology Reports", "abstract": "The findings section of a radiology report is often detailed and lengthy, whereas the impression section is comparatively more compact and captures key diagnostic conclusions. This research explores the use of advanced abstractive summarization models to generate the concise impression from the findings section of a radiology report. We have used the publicly available MIMIC-CXR dataset. A comparative analysis is conducted on leading pre-trained and open-source large language models, including T5-base, BART-base, PEGASUS-x-base, ChatGPT-4, LLaMA-3-8B, and a custom Pointer Generator Network with a coverage mechanism. To ensure a thorough assessment, multiple evaluation metrics are employed, including ROUGE-1, ROUGE-2, ROUGE-L, METEOR, and BERTScore. By analyzing the performance of these models, this study identifies their respective strengths and limitations in the summarization of medical text. The findings of this paper provide helpful information for medical professionals who need automated summarization solutions in the healthcare sector.", "date": "2025-06-19", "categories": []}
{"id": "2506.16628", "title": "Initial Investigation of LLM-Assisted Development of Rule-Based Clinical NLP System", "abstract": "Despite advances in machine learning (ML) and large language models (LLMs), rule-based natural language processing (NLP) systems remain active in clinical settings due to their interpretability and operational efficiency. However, their manual development and maintenance are labor-intensive, particularly in tasks with large linguistic variability. To overcome these limitations, we proposed a novel approach employing LLMs solely during the rule-based systems development phase. We conducted the initial experiments focusing on the first two steps of developing a rule-based NLP pipeline: find relevant snippets from the clinical note; extract informative keywords from the snippets for the rule-based named entity recognition (NER) component. Our experiments demonstrated exceptional recall in identifying clinically relevant text snippets (Deepseek: 0.98, Qwen: 0.99) and 1.0 in extracting key terms for NER. This study sheds light on a promising new direction for NLP development, enabling semi-automated or automated development of rule-based systems with significantly faster, more cost-effective, and transparent execution compared with deep learning model-based solutions.", "date": "2025-06-19", "categories": []}
{"id": "2412.18043", "title": "Aligning AI Research with the Needs of Clinical Coding Workflows: Eight Recommendations Based on US Data Analysis and Critical Review", "abstract": "Clinical coding is crucial for healthcare billing and data analysis. Manual clinical coding is labour-intensive and error-prone, which has motivated research towards full automation of the process. However, our analysis, based on US English electronic health records and automated coding research using these records, shows that widely used evaluation methods are not aligned with real clinical contexts. For example, evaluations that focus on the top 50 most common codes are an oversimplification, as there are thousands of codes used in practice. This position paper aims to align AI coding research more closely with practical challenges of clinical coding. Based on our analysis, we offer eight specific recommendations, suggesting ways to improve current evaluation methods. Additionally, we propose new AI-based methods beyond automated coding, suggesting alternative approaches to assist clinical coders in their workflows.", "date": "2024-12-23", "categories": []}
{"id": "2506.14900", "title": "Adverse Event Extraction from Discharge Summaries: A New Dataset, Annotation Scheme, and Initial Findings", "abstract": "In this work, we present a manually annotated corpus for Adverse Event (AE) extraction from discharge summaries of elderly patients, a population often underrepresented in clinical NLP resources. The dataset includes 14 clinically significant AEs-such as falls, delirium, and intracranial haemorrhage, along with contextual attributes like negation, diagnosis type, and in-hospital occurrence. Uniquely, the annotation schema supports both discontinuous and overlapping entities, addressing challenges rarely tackled in prior work. We evaluate multiple models using FlairNLP across three annotation granularities: fine-grained, coarse-grained, and coarse-grained with negation. While transformer-based models (e.g., BERT-cased) achieve strong performance on document-level coarse-grained extraction (F1 = 0.943), performance drops notably for fine-grained entity-level tasks (e.g., F1 = 0.675), particularly for rare events and complex attributes. These results demonstrate that despite high-level scores, significant challenges remain in detecting underrepresented AEs and capturing nuanced clinical language. Developed within a Trusted Research Environment (TRE), the dataset is available upon request via DataLoch and serves as a robust benchmark for evaluating AE extraction methods and supporting future cross-dataset generalisation.", "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": "2506.12674", "title": "Enhancing Clinical Models with Pseudo Data for De-identification", "abstract": "Many models are pretrained on redacted text for privacy reasons. Clinical foundation models are often trained on de-identified text, which uses special syntax (masked) text in place of protected health information. Even though these models have increased in popularity, there has been little effort in understanding the effects of training them on redacted text. In this work, we pretrain several encoder-only models on a dataset that contains redacted text and a version with replaced realistic pseudo text. We then fine-tuned models for the protected health information de-identification task and show how our methods significantly outperform previous baselines. The contributions of this work include: a) our novel, and yet surprising findings with training recommendations, b) redacted text replacements used to produce the pseudo dataset, c) pretrained embeddings and fine-tuned task specific models, and d) freely available pseudo training dataset generation and model source code used in our experiments.", "date": "2025-06-15", "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": "2506.14203", "title": "Intended Target Identification for Anomia Patients with Gradient-based Selective Augmentation", "abstract": "In this study, we investigate the potential of language models (LMs) in aiding patients experiencing anomia, a difficulty identifying the names of items. Identifying the intended target item from patient's circumlocution involves the two challenges of term failure and error: (1) The terms relevant to identifying the item remain unseen. (2) What makes the challenge unique is inherent perturbed terms by semantic paraphasia, which are not exactly related to the target item, hindering the identification process. To address each, we propose robustifying the model from semantically paraphasic errors and enhancing the model with unseen terms with gradient-based selective augmentation. Specifically, the gradient value controls augmented data quality amid semantic errors, while the gradient variance guides the inclusion of unseen but relevant terms. Due to limited domain-specific datasets, we evaluate the model on the Tip-of-the-Tongue dataset as an intermediary task and then apply our findings to real patient data from AphasiaBank. Our results demonstrate strong performance against baselines, aiding anomia patients by addressing the outlined challenges.", "date": "2025-06-17", "categories": []}
{"id": "2408.14568", "title": "Improving Clinical Note Generation from Complex Doctor-Patient Conversation", "abstract": "Writing clinical notes and documenting medical exams is a critical task for healthcare professionals, serving as a vital component of patient care documentation. However, manually writing these notes is time-consuming and can impact the amount of time clinicians can spend on direct patient interaction and other tasks. Consequently, the development of automated clinical note generation systems has emerged as a clinically meaningful area of research within AI for health. In this paper, we present three key contributions to the field of clinical note generation using large language models (LLMs). First, we introduce CliniKnote, a comprehensive dataset consisting of 1,200 complex doctor-patient conversations paired with their full clinical notes. This dataset, created and curated by medical experts with the help of modern neural networks, provides a valuable resource for training and evaluating models in clinical note generation tasks. Second, we propose the K-SOAP (Keyword, Subjective, Objective, Assessment, and Plan) note format, which enhances traditional SOAP~\\cite{podder2023soap} (Subjective, Objective, Assessment, and Plan) notes by adding a keyword section at the top, allowing for quick identification of essential information. Third, we develop an automatic pipeline to generate K-SOAP notes from doctor-patient conversations and benchmark various modern LLMs using various metrics. Our results demonstrate significant improvements in efficiency and performance compared to standard LLM finetuning methods.", "date": "2024-08-26", "categories": []}
{"id": "2506.12823", "title": "Medical Argument Mining: Exploitation of Scarce Data Using NLI Systems", "abstract": "This work presents an Argument Mining process that extracts argumentative entities from clinical texts and identifies their relationships using token classification and Natural Language Inference techniques. Compared to straightforward methods like text classification, this methodology demonstrates superior performance in data-scarce settings. By assessing the effectiveness of these methods in identifying argumentative structures that support or refute possible diagnoses, this research lays the groundwork for future tools that can provide evidence-based justifications for machine-generated clinical conclusions.", "date": "2025-06-15", "categories": []}
{"id": "2506.12936", "title": "CliniDial: A Naturally Occurring Multimodal Dialogue Dataset for Team Reflection in Action During Clinical Operation", "abstract": "In clinical operations, teamwork can be the crucial factor that determines the final outcome. Prior studies have shown that sufficient collaboration is the key factor that determines the outcome of an operation. To understand how the team practices teamwork during the operation, we collected CliniDial from simulations of medical operations. CliniDial includes the audio data and its transcriptions, the simulated physiology signals of the patient manikins, and how the team operates from two camera angles. We annotate behavior codes following an existing framework to understand the teamwork process for CliniDial. We pinpoint three main characteristics of our dataset, including its label imbalances, rich and natural interactions, and multiple modalities, and conduct experiments to test existing LLMs' capabilities on handling data with these characteristics. Experimental results show that CliniDial poses significant challenges to the existing models, inviting future effort on developing methods that can deal with real-world clinical data. We open-source the codebase at https://github.com/MichiganNLP/CliniDial", "date": "2025-06-15", "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": "2506.11478", "title": "ImmunoFOMO: Are Language Models missing what oncologists see?", "abstract": "Language models (LMs) capabilities have grown with a fast pace over the past decade leading researchers in various disciplines, such as biomedical research, to increasingly explore the utility of LMs in their day-to-day applications. Domain specific language models have already been in use for biomedical natural language processing (NLP) applications. Recently however, the interest has grown towards medical language models and their understanding capabilities. In this paper, we investigate the medical conceptual grounding of various language models against expert clinicians for identification of hallmarks of immunotherapy in breast cancer abstracts. Our results show that pre-trained language models have potential to outperform large language models in identifying very specific (low-level) concepts.", "date": "2025-06-13", "categories": []}
{"id": "2506.11666", "title": "Converting Annotated Clinical Cases into Structured Case Report Forms", "abstract": "Case Report Forms (CRFs) are largely used in medical research as they ensure accuracy, reliability, and validity of results in clinical studies. However, publicly available, wellannotated CRF datasets are scarce, limiting the development of CRF slot filling systems able to fill in a CRF from clinical notes. To mitigate the scarcity of CRF datasets, we propose to take advantage of available datasets annotated for information extraction tasks and to convert them into structured CRFs. We present a semi-automatic conversion methodology, which has been applied to the E3C dataset in two languages (English and Italian), resulting in a new, high-quality dataset for CRF slot filling. Through several experiments on the created dataset, we report that slot filling achieves 59.7% for Italian and 67.3% for English on a closed Large Language Models (zero-shot) and worse performances on three families of open-source models, showing that filling CRFs is challenging even for recent state-of-the-art LLMs. We release the datest at https://huggingface.co/collections/NLP-FBK/e3c-to-crf-67b9844065460cbe42f80166", "date": "2025-06-13", "categories": []}
{"id": "2506.10328", "title": "Towards Scalable SOAP Note Generation: A Weakly Supervised Multimodal Framework", "abstract": "Skin carcinoma is the most prevalent form of cancer globally, accounting for over $8 billion in annual healthcare expenditures. In clinical settings, physicians document patient visits using detailed SOAP (Subjective, Objective, Assessment, and Plan) notes. However, manually generating these notes is labor-intensive and contributes to clinician burnout. In this work, we propose a weakly supervised multimodal framework to generate clinically structured SOAP notes from limited inputs, including lesion images and sparse clinical text. Our approach reduces reliance on manual annotations, enabling scalable, clinically grounded documentation while alleviating clinician burden and reducing the need for large annotated data. Our method achieves performance comparable to GPT-4o, Claude, and DeepSeek Janus Pro across key clinical relevance metrics. To evaluate clinical quality, we introduce two novel metrics MedConceptEval and Clinical Coherence Score (CCS) which assess semantic alignment with expert medical concepts and input features, respectively.", "date": "2025-06-12", "categories": []}
{"id": "2506.10896", "title": "BioClinical ModernBERT: A State-of-the-Art Long-Context Encoder for Biomedical and Clinical NLP", "abstract": "Encoder-based transformer models are central to biomedical and clinical Natural Language Processing (NLP), as their bidirectional self-attention makes them well-suited for efficiently extracting structured information from unstructured text through discriminative tasks. However, encoders have seen slower development compared to decoder models, leading to limited domain adaptation in biomedical and clinical settings. We introduce BioClinical ModernBERT, a domain-adapted encoder that builds on the recent ModernBERT release, incorporating long-context processing and substantial improvements in speed and performance for biomedical and clinical NLP. BioClinical ModernBERT is developed through continued pretraining on the largest biomedical and clinical corpus to date, with over 53.5 billion tokens, and addresses a key limitation of prior clinical encoders by leveraging 20 datasets from diverse institutions, domains, and geographic regions, rather than relying on data from a single source. It outperforms existing biomedical and clinical encoders on four downstream tasks spanning a broad range of use cases. We release both base (150M parameters) and large (396M parameters) versions of BioClinical ModernBERT, along with training checkpoints to support further research.", "date": "2025-06-12", "categories": []}
{"id": "2506.09315", "title": "Alzheimer's Dementia Detection Using Perplexity from Paired Large Language Models", "abstract": "Alzheimer's dementia (AD) is a neurodegenerative disorder with cognitive decline that commonly impacts language ability. This work extends the paired perplexity approach to detecting AD by using a recent large language model (LLM), the instruction-following version of Mistral-7B. We improve accuracy by an average of 3.33% over the best current paired perplexity method and by 6.35% over the top-ranked method from the ADReSS 2020 challenge benchmark. Our further analysis demonstrates that the proposed approach can effectively detect AD with a clear and interpretable decision boundary in contrast to other methods that suffer from opaque decision-making processes. Finally, by prompting the fine-tuned LLMs and comparing the model-generated responses to human responses, we illustrate that the LLMs have learned the special language patterns of AD speakers, which opens up possibilities for novel methods of model interpretation and data augmentation.", "date": "2025-06-11", "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": "2506.08231", "title": "Ensuring Reliability of Curated EHR-Derived Data: The Validation of Accuracy for LLM/ML-Extracted Information and Data (VALID) Framework", "abstract": "Large language models (LLMs) are increasingly used to extract clinical data from electronic health records (EHRs), offering significant improvements in scalability and efficiency for real-world data (RWD) curation in oncology. However, the adoption of LLMs introduces new challenges in ensuring the reliability, accuracy, and fairness of extracted data, which are essential for research, regulatory, and clinical applications. Existing quality assurance frameworks for RWD and artificial intelligence do not fully address the unique error modes and complexities associated with LLM-extracted data. In this paper, we propose a comprehensive framework for evaluating the quality of clinical data extracted by LLMs. The framework integrates variable-level performance benchmarking against expert human abstraction, automated verification checks for internal consistency and plausibility, and replication analyses comparing LLM-extracted data to human-abstracted datasets or external standards. This multidimensional approach enables the identification of variables most in need of improvement, systematic detection of latent errors, and confirmation of dataset fitness-for-purpose in real-world research. Additionally, the framework supports bias assessment by stratifying metrics across demographic subgroups. By providing a rigorous and transparent method for assessing LLM-extracted RWD, this framework advances industry standards and supports the trustworthy use of AI-powered evidence generation in oncology research and practice.", "date": "2025-06-09", "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": "2506.04924", "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": "2506.05589", "title": "UTSA-NLP at ArchEHR-QA 2025: Improving EHR Question Answering via Self-Consistency Prompting", "abstract": "We describe our system for the ArchEHR-QA Shared Task on answering clinical questions using electronic health records (EHRs). Our approach uses large language models in two steps: first, to find sentences in the EHR relevant to a clinician's question, and second, to generate a short, citation-supported response based on those sentences. We use few-shot prompting, self-consistency, and thresholding to improve the sentence classification step to decide which sentences are essential. We compare several models and find that a smaller 8B model performs better than a larger 70B model for identifying relevant information. Our results show that accurate sentence selection is critical for generating high-quality responses and that self-consistency with thresholding helps make these decisions more reliable.", "date": "2025-06-05", "categories": []}
{"id": "2506.06208", "title": "Building Models of Neurological Language", "abstract": "This report documents the development and evaluation of domain-specific language models for neurology. Initially focused on building a bespoke model, the project adapted to rapid advances in open-source and commercial medical LLMs, shifting toward leveraging retrieval-augmented generation (RAG) and representational models for secure, local deployment. Key contributions include the creation of neurology-specific datasets (case reports, QA sets, textbook-derived data), tools for multi-word expression extraction, and graph-based analyses of medical terminology. The project also produced scripts and Docker containers for local hosting. Performance metrics and graph community results are reported, with future possible work open for multimodal models using open-source architectures like phi-4.", "date": "2025-06-06", "categories": []}
{"id": "2505.10360", "title": "FactsR: A Safer Method for Producing High Quality Healthcare Documentation", "abstract": "There are now a multitude of AI-scribing solutions for healthcare promising the utilization of large language models for ambient documentation. However, these AI scribes still rely on one-shot, or few-shot prompts for generating notes after the consultation has ended, employing little to no reasoning. This risks long notes with an increase in hallucinations, misrepresentation of the intent of the clinician, and reliance on the proofreading of the clinician to catch errors. A dangerous combination for patient safety if vigilance is compromised by workload and fatigue. In this paper, we introduce a method for extracting salient clinical information in real-time alongside the healthcare consultation, denoted Facts, and use that information recursively to generate the final note. The FactsR method results in more accurate and concise notes by placing the clinician-in-the-loop of note generation, while opening up new use cases within real-time decision support.", "date": "2025-05-15", "categories": []}
{"id": "2303.12892", "title": "Improving Transformer Performance for French Clinical Notes Classification Using Mixture of Experts on a Limited Dataset", "abstract": "Transformer-based models have shown outstanding results in natural language processing but face challenges in applications like classifying small-scale clinical texts, especially with constrained computational resources. This study presents a customized Mixture of Expert (MoE) Transformer models for classifying small-scale French clinical texts at CHU Sainte-Justine Hospital. The MoE-Transformer addresses the dual challenges of effective training with limited data and low-resource computation suitable for in-house hospital use. Despite the success of biomedical pre-trained models such as CamemBERT-bio, DrBERT, and AliBERT, their high computational demands make them impractical for many clinical settings. Our MoE-Transformer model not only outperforms DistillBERT, CamemBERT, FlauBERT, and Transformer models on the same dataset but also achieves impressive results: an accuracy of 87\\%, precision of 87\\%, recall of 85\\%, and F1-score of 86\\%. While the MoE-Transformer does not surpass the performance of biomedical pre-trained BERT models, it can be trained at least 190 times faster, offering a viable alternative for settings with limited data and computational resources. Although the MoE-Transformer addresses challenges of generalization gaps and sharp minima, demonstrating some limitations for efficient and accurate clinical text classification, this model still represents a significant advancement in the field. It is particularly valuable for classifying small French clinical narratives within the privacy and constraints of hospital-based computational resources.", "date": "2023-03-22", "categories": []}
{"id": "2407.21050", "title": "Cross-Institutional Dental EHR Entity Extraction via Generative AI and Synthetic Notes", "abstract": "This research addresses the issue of missing structured data in dental records by extracting diagnostic information from unstructured text. The updated periodontology classification system's complexity has increased incomplete or missing structured diagnoses. To tackle this, we use advanced AI and NLP methods, leveraging GPT-4 to generate synthetic notes for fine-tuning a RoBERTa model. This significantly enhances the model's ability to understand medical and dental language. We evaluated the model using 120 randomly selected clinical notes from two datasets, demonstrating its improved diagnostic extraction accuracy. The results showed high accuracy in diagnosing periodontal status, stage, and grade, with Site 1 scoring 0.99 and Site 2 scoring 0.98. In the subtype category, Site 2 achieved perfect scores, outperforming Site 1. This method enhances extraction accuracy and broadens its use across dental contexts. The study underscores AI and NLP's transformative impact on healthcare delivery and management. Integrating AI and NLP technologies enhances documentation and simplifies administrative tasks by precisely extracting complex clinical information. This approach effectively addresses challenges in dental diagnostics. Using synthetic training data from LLMs optimizes the training process, improving accuracy and efficiency in identifying periodontal diagnoses from clinical notes. This innovative method holds promise for broader healthcare applications, potentially improving patient care quality.", "date": "2024-07-23", "categories": []}
{"id": "2506.02924", "title": "INESC-ID @ eRisk 2025: Exploring Fine-Tuned, Similarity-Based, and Prompt-Based Approaches to Depression Symptom Identification", "abstract": "In this work, we describe our team's approach to eRisk's 2025 Task 1: Search for Symptoms of Depression. Given a set of sentences and the Beck's Depression Inventory - II (BDI) questionnaire, participants were tasked with submitting up to 1,000 sentences per depression symptom in the BDI, sorted by relevance. Participant submissions were evaluated according to standard Information Retrieval (IR) metrics, including Average Precision (AP) and R-Precision (R-PREC). The provided training data, however, consisted of sentences labeled as to whether a given sentence was relevant or not w.r.t. one of BDI's symptoms. Due to this labeling limitation, we framed our development as a binary classification task for each BDI symptom, and evaluated accordingly. To that end, we split the available labeled data into training and validation sets, and explored foundation model fine-tuning, sentence similarity, Large Language Model (LLM) prompting, and ensemble techniques. The validation results revealed that fine-tuning foundation models yielded the best performance, particularly when enhanced with synthetic data to mitigate class imbalance. We also observed that the optimal approach varied by symptom. Based on these insights, we devised five independent test runs, two of which used ensemble methods. These runs achieved the highest scores in the official IR evaluation, outperforming submissions from 16 other teams.", "date": "2025-06-03", "categories": []}
{"id": "2403.04024", "title": "Enhancing chest X-ray datasets with privacy-preserving large language models and multi-type annotations: a data-driven approach for improved classification", "abstract": "In chest X-ray (CXR) image analysis, rule-based systems are usually employed to extract labels from reports for dataset releases. However, there is still room for improvement in label quality. These labelers typically output only presence labels, sometimes with binary uncertainty indicators, which limits their usefulness. Supervised deep learning models have also been developed for report labeling but lack adaptability, similar to rule-based systems. In this work, we present MAPLEZ (Medical report Annotations with Privacy-preserving Large language model using Expeditious Zero shot answers), a novel approach leveraging a locally executable Large Language Model (LLM) to extract and enhance findings labels on CXR reports. MAPLEZ extracts not only binary labels indicating the presence or absence of a finding but also the location, severity, and radiologists' uncertainty about the finding. Over eight abnormalities from five test sets, we show that our method can extract these annotations with an increase of 3.6 percentage points (pp) in macro F1 score for categorical presence annotations and more than 20 pp increase in F1 score for the location annotations over competing labelers. Additionally, using the combination of improved annotations and multi-type annotations in classification supervision, we demonstrate substantial advancements in model quality, with an increase of 1.1 pp in AUROC over models trained with annotations from the best alternative approach. We share code and annotations.", "date": "2024-03-06", "categories": []}
{"id": "2506.00060", "title": "Comparative analysis of privacy-preserving open-source LLMs regarding extraction of diagnostic information from clinical CMR imaging reports", "abstract": "Purpose: We investigated the utilization of privacy-preserving, locally-deployed, open-source Large Language Models (LLMs) to extract diagnostic information from free-text cardiovascular magnetic resonance (CMR) reports. Materials and Methods: We evaluated nine open-source LLMs on their ability to identify diagnoses and classify patients into various cardiac diagnostic categories based on descriptive findings in 109 clinical CMR reports. Performance was quantified using standard classification metrics including accuracy, precision, recall, and F1 score. We also employed confusion matrices to examine patterns of misclassification across models. Results: Most open-source LLMs demonstrated exceptional performance in classifying reports into different diagnostic categories. Google's Gemma2 model achieved the highest average F1 score of 0.98, followed by Qwen2.5:32B and DeepseekR1-32B with F1 scores of 0.96 and 0.95, respectively. All other evaluated models attained average scores above 0.93, with Mistral and DeepseekR1-7B being the only exceptions. The top four LLMs outperformed our board-certified cardiologist (F1 score of 0.94) across all evaluation metrics in analyzing CMR reports. Conclusion: Our findings demonstrate the feasibility of implementing open-source, privacy-preserving LLMs in clinical settings for automated analysis of imaging reports, enabling accurate, fast and resource-efficient diagnostic categorization.", "date": "2025-05-29", "categories": []}
{"id": "2506.00134", "title": "Spurious Correlations and Beyond: Understanding and Mitigating Shortcut Learning in SDOH Extraction with Large Language Models", "abstract": "Social determinants of health (SDOH) extraction from clinical text is critical for downstream healthcare analytics. Although large language models (LLMs) have shown promise, they may rely on superficial cues leading to spurious predictions. Using the MIMIC portion of the SHAC (Social History Annotation Corpus) dataset and focusing on drug status extraction as a case study, we demonstrate that mentions of alcohol or smoking can falsely induce models to predict current/past drug use where none is present, while also uncovering concerning gender disparities in model performance. We further evaluate mitigation strategies - such as prompt engineering and chain-of-thought reasoning - to reduce these false positives, providing insights into enhancing LLM reliability in health domains.", "date": "2025-05-30", "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": "2505.24090", "title": "Searching Clinical Data Using Generative AI", "abstract": "Artificial Intelligence (AI) is making a major impact on healthcare, particularly through its application in natural language processing (NLP) and predictive analytics. The healthcare sector has increasingly adopted AI for tasks such as clinical data analysis and medical code assignment. However, searching for clinical information in large and often unorganized datasets remains a manual and error-prone process. Assisting this process with automations can help physicians improve their operational productivity significantly.\n  In this paper, we present a generative AI approach, coined SearchAI, to enhance the accuracy and efficiency of searching clinical data. Unlike traditional code assignment, which is a one-to-one problem, clinical data search is a one-to-many problem, i.e., a given search query can map to a family of codes. Healthcare professionals typically search for groups of related diseases, drugs, or conditions that map to many codes, and therefore, they need search tools that can handle keyword synonyms, semantic variants, and broad open-ended queries. SearchAI employs a hierarchical model that respects the coding hierarchy and improves the traversal of relationships from parent to child nodes. SearchAI navigates these hierarchies predictively and ensures that all paths are reachable without losing any relevant nodes.\n  To evaluate the effectiveness of SearchAI, we conducted a series of experiments using both public and production datasets. Our results show that SearchAI outperforms default hierarchical traversals across several metrics, including accuracy, robustness, performance, and scalability. SearchAI can help make clinical data more accessible, leading to streamlined workflows, reduced administrative burden, and enhanced coding and diagnostic accuracy.", "date": "2025-05-30", "categories": []}
{"id": "2505.24105", "title": "Training LLMs for EHR-Based Reasoning Tasks via Reinforcement Learning", "abstract": "We present EHRMIND, a practical recipe for adapting large language models (LLMs) to complex clinical reasoning tasks using reinforcement learning with verifiable rewards (RLVR). While RLVR has succeeded in mathematics and coding, its application to healthcare contexts presents unique challenges due to the specialized knowledge and reasoning required for electronic health record (EHR) interpretation. Our pilot study on the MEDCALC benchmark reveals two key failure modes: (1) misapplied knowledge, where models possess relevant medical knowledge but apply it incorrectly, and (2) missing knowledge, where models lack essential domain knowledge. To address these cases, EHRMIND applies a two-stage solution: a lightweight supervised fine-tuning (SFT) warm-up that injects missing domain knowledge, stabilizes subsequent training, and encourages structured, interpretable outputs; followed by RLVR, which reinforces outcome correctness and refines the model's decision-making. We demonstrate the effectiveness of our method across diverse clinical applications, including medical calculations (MEDCALC), patient-trial matching (TREC CLINICAL TRIALS), and disease diagnosis (EHRSHOT). EHRMIND delivers consistent gains in accuracy, interpretability, and cross-task generalization. These findings offer practical guidance for applying RLVR to enhance LLM capabilities in healthcare settings.", "date": "2025-05-30", "categories": []}
{"id": "2505.24619", "title": "Interpretable phenotyping of Heart Failure patients with Dutch discharge letters", "abstract": "Objective: Heart failure (HF) patients present with diverse phenotypes affecting treatment and prognosis. This study evaluates models for phenotyping HF patients based on left ventricular ejection fraction (LVEF) classes, using structured and unstructured data, assessing performance and interpretability.\n  Materials and Methods: The study analyzes all HF hospitalizations at both Amsterdam UMC hospitals (AMC and VUmc) from 2015 to 2023 (33,105 hospitalizations, 16,334 patients). Data from AMC were used for model training, and from VUmc for external validation. The dataset was unlabelled and included tabular clinical measurements and discharge letters. Silver labels for LVEF classes were generated by combining diagnosis codes, echocardiography results, and textual mentions. Gold labels were manually annotated for 300 patients for testing. Multiple Transformer-based (black-box) and Aug-Linear (white-box) models were trained and compared with baselines on structured and unstructured data. To evaluate interpretability, two clinicians annotated 20 discharge letters by highlighting information they considered relevant for LVEF classification. These were compared to SHAP and LIME explanations from black-box models and the inherent explanations of Aug-Linear models.\n  Results: BERT-based and Aug-Linear models, using discharge letters alone, achieved the highest classification results (AUC=0.84 for BERT, 0.81 for Aug-Linear on external validation), outperforming baselines. Aug-Linear explanations aligned more closely with clinicians' explanations than post-hoc explanations on black-box models.\n  Conclusions: Discharge letters emerged as the most informative source for phenotyping HF patients. Aug-Linear models matched black-box performance while providing clinician-aligned interpretability, supporting their use in transparent clinical decision-making.", "date": "2025-05-30", "categories": []}
{"id": "2503.13330", "title": "LEAVS: An LLM-based Labeler for Abdominal CT Supervision", "abstract": "Extracting structured labels from radiology reports has been employed to create vision models to simultaneously detect several types of abnormalities. However, existing works focus mainly on the chest region. Few works have been investigated on abdominal radiology reports due to more complex anatomy and a wider range of pathologies in the abdomen. We propose LEAVS (Large language model Extractor for Abdominal Vision Supervision). This labeler can annotate the certainty of presence and the urgency of seven types of abnormalities for nine abdominal organs on CT radiology reports. To ensure broad coverage, we chose abnormalities that encompass most of the finding types from CT reports. Our approach employs a specialized chain-of-thought prompting strategy for a locally-run LLM using sentence extraction and multiple-choice questions in a tree-based decision system. We demonstrate that the LLM can extract several abnormality types across abdominal organs with an average F1 score of 0.89, significantly outperforming competing labelers and humans. Additionally, we show that extraction of urgency labels achieved performance comparable to human annotations. Finally, we demonstrate that the abnormality labels contain valuable information for training a single vision model that classifies several organs as normal or abnormal. We release our code and structured annotations for a public CT dataset containing over 1,000 CT volumes.", "date": "2025-03-17", "categories": []}
{"id": "2505.14107", "title": "DiagnosisArena: Benchmarking Diagnostic Reasoning for Large Language Models", "abstract": "The emergence of groundbreaking large language models capable of performing complex reasoning tasks holds significant promise for addressing various scientific challenges, including those arising in complex clinical scenarios. To enable their safe and effective deployment in real-world healthcare settings, it is urgently necessary to benchmark the diagnostic capabilities of current models systematically. Given the limitations of existing medical benchmarks in evaluating advanced diagnostic reasoning, we present DiagnosisArena, a comprehensive and challenging benchmark designed to rigorously assess professional-level diagnostic competence. DiagnosisArena consists of 1,113 pairs of segmented patient cases and corresponding diagnoses, spanning 28 medical specialties, deriving from clinical case reports published in 10 top-tier medical journals. The benchmark is developed through a meticulous construction pipeline, involving multiple rounds of screening and review by both AI systems and human experts, with thorough checks conducted to prevent data leakage. Our study reveals that even the most advanced reasoning models, o3, o1, and DeepSeek-R1, achieve only 51.12%, 31.09%, and 17.79% accuracy, respectively. This finding highlights a significant generalization bottleneck in current large language models when faced with clinical diagnostic reasoning challenges. Through DiagnosisArena, we aim to drive further advancements in AI's diagnostic reasoning capabilities, enabling more effective solutions for real-world clinical diagnostic challenges. We provide the benchmark and evaluation tools for further research and development https://github.com/SPIRAL-MED/DiagnosisArena.", "date": "2025-05-20", "categories": []}
{"id": "2505.23030", "title": "Can Modern NLP Systems Reliably Annotate Chest Radiography Exams? A Pre-Purchase Evaluation and Comparative Study of Solutions from AWS, Google, Azure, John Snow Labs, and Open-Source Models on an Independent Pediatric Dataset", "abstract": "General-purpose clinical natural language processing (NLP) tools are increasingly used for the automatic labeling of clinical reports. However, independent evaluations for specific tasks, such as pediatric chest radiograph (CXR) report labeling, are limited. This study compares four commercial clinical NLP systems - Amazon Comprehend Medical (AWS), Google Healthcare NLP (GC), Azure Clinical NLP (AZ), and SparkNLP (SP) - for entity extraction and assertion detection in pediatric CXR reports. Additionally, CheXpert and CheXbert, two dedicated chest radiograph report labelers, were evaluated on the same task using CheXpert-defined labels. We analyzed 95,008 pediatric CXR reports from a large academic pediatric hospital. Entities and assertion statuses (positive, negative, uncertain) from the findings and impression sections were extracted by the NLP systems, with impression section entities mapped to 12 disease categories and a No Findings category. CheXpert and CheXbert extracted the same 13 categories. Outputs were compared using Fleiss Kappa and accuracy against a consensus pseudo-ground truth. Significant differences were found in the number of extracted entities and assertion distributions across NLP systems. SP extracted 49,688 unique entities, GC 16,477, AZ 31,543, and AWS 27,216. Assertion accuracy across models averaged around 62%, with SP highest (76%) and AWS lowest (50%). CheXpert and CheXbert achieved 56% accuracy. Considerable variability in performance highlights the need for careful validation and review before deploying NLP tools for clinical report labeling.", "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": "2505.21757", "title": "BehaviorSFT: Behavioral Token Conditioning for Clinical Agents Across the Proactivity Spectrum", "abstract": "Large Language Models (LLMs) as clinical agents require careful behavioral adaptation. While adept at reactive tasks (e.g., diagnosis reasoning), LLMs often struggle with proactive engagement, like unprompted identification of critical missing information or risks. We introduce BehaviorBench, a comprehensive dataset to evaluate agent behaviors across a clinical assistance spectrum, ranging from reactive query responses to proactive interventions (e.g., clarifying ambiguities, flagging overlooked critical data). Our BehaviorBench experiments reveal LLMs' inconsistent proactivity. To address this, we propose BehaviorSFT, a novel training strategy using behavioral tokens to explicitly condition LLMs for dynamic behavioral selection along this spectrum. BehaviorSFT boosts performance, achieving up to 97.3% overall Macro F1 on BehaviorBench and improving proactive task scores (e.g., from 95.0% to 96.5% for Qwen2.5-7B-Ins). Crucially, blind clinician evaluations confirmed BehaviorSFT-trained agents exhibit more realistic clinical behavior, striking a superior balance between helpful proactivity (e.g., timely, relevant suggestions) and necessary restraint (e.g., avoiding over-intervention) versus standard fine-tuning or explicit instructed agents.", "date": "2025-05-27", "categories": []}
{"id": "2505.22280", "title": "Natural Language Processing in Support of Evidence-based Medicine: A Scoping Review", "abstract": "Evidence-based medicine (EBM) is at the forefront of modern healthcare, emphasizing the use of the best available scientific evidence to guide clinical decisions. Due to the sheer volume and rapid growth of medical literature and the high cost of curation, there is a critical need to investigate Natural Language Processing (NLP) methods to identify, appraise, synthesize, summarize, and disseminate evidence in EBM. This survey presents an in-depth review of 129 research studies on leveraging NLP for EBM, illustrating its pivotal role in enhancing clinical decision-making processes. The paper systematically explores how NLP supports the five fundamental steps of EBM -- Ask, Acquire, Appraise, Apply, and Assess. The review not only identifies current limitations within the field but also proposes directions for future research, emphasizing the potential for NLP to revolutionize EBM by refining evidence extraction, evidence synthesis, appraisal, summarization, enhancing data comprehensibility, and facilitating a more efficient clinical workflow.", "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": "2505.20320", "title": "Less Context, Same Performance: A RAG Framework for Resource-Efficient LLM-Based Clinical NLP", "abstract": "Long text classification is challenging for Large Language Models (LLMs) due to token limits and high computational costs. This study explores whether a Retrieval Augmented Generation (RAG) approach using only the most relevant text segments can match the performance of processing entire clinical notes with large context LLMs. We begin by splitting clinical documents into smaller chunks, converting them into vector embeddings, and storing these in a FAISS index. We then retrieve the top 4,000 words most pertinent to the classification query and feed these consolidated segments into an LLM. We evaluated three LLMs (GPT4o, LLaMA, and Mistral) on a surgical complication identification task. Metrics such as AUC ROC, precision, recall, and F1 showed no statistically significant differences between the RAG based approach and whole-text processing (p > 0.05p > 0.05). These findings indicate that RAG can significantly reduce token usage without sacrificing classification accuracy, providing a scalable and cost effective solution for analyzing lengthy clinical documents.", "date": "2025-05-23", "categories": []}
{"id": "2505.21324", "title": "Leveraging large language models and traditional machine learning ensembles for ADHD detection from narrative transcripts", "abstract": "Despite rapid advances in large language models (LLMs), their integration with traditional supervised machine learning (ML) techniques that have proven applicability to medical data remains underexplored. This is particularly true for psychiatric applications, where narrative data often exhibit nuanced linguistic and contextual complexity, and can benefit from the combination of multiple models with differing characteristics. In this study, we introduce an ensemble framework for automatically classifying Attention-Deficit/Hyperactivity Disorder (ADHD) diagnosis (binary) using narrative transcripts. Our approach integrates three complementary models: LLaMA3, an open-source LLM that captures long-range semantic structure; RoBERTa, a pre-trained transformer model fine-tuned on labeled clinical narratives; and a Support Vector Machine (SVM) classifier trained using TF-IDF-based lexical features. These models are aggregated through a majority voting mechanism to enhance predictive robustness. The dataset includes 441 instances, including 352 for training and 89 for validation. Empirical results show that the ensemble outperforms individual models, achieving an F$_1$ score of 0.71 (95\\% CI: [0.60-0.80]). Compared to the best-performing individual model (SVM), the ensemble improved recall while maintaining competitive precision. This indicates the strong sensitivity of the ensemble in identifying ADHD-related linguistic cues. These findings demonstrate the promise of hybrid architectures that leverage the semantic richness of LLMs alongside the interpretability and pattern recognition capabilities of traditional supervised ML, offering a new direction for robust and generalizable psychiatric text classification.", "date": "2025-05-27", "categories": []}
{"id": "2505.18530", "title": "MRGAgents: A Multi-Agent Framework for Improved Medical Report Generation with Med-LVLMs", "abstract": "Medical Large Vision-Language Models (Med-LVLMs) have been widely adopted for medical report generation. Despite Med-LVLMs producing state-of-the-art performance, they exhibit a bias toward predicting all findings as normal, leading to reports that overlook critical abnormalities. Furthermore, these models often fail to provide comprehensive descriptions of radiologically relevant regions necessary for accurate diagnosis. To address these challenges, we proposeMedical Report Generation Agents (MRGAgents), a novel multi-agent framework that fine-tunes specialized agents for different disease categories. By curating subsets of the IU X-ray and MIMIC-CXR datasets to train disease-specific agents, MRGAgents generates reports that more effectively balance normal and abnormal findings while ensuring a comprehensive description of clinically relevant regions. Our experiments demonstrate that MRGAgents outperformed the state-of-the-art, improving both report comprehensiveness and diagnostic utility.", "date": "2025-05-24", "categories": []}
{"id": "2505.18708", "title": "A General Knowledge Injection Framework for ICD Coding", "abstract": "ICD Coding aims to assign a wide range of medical codes to a medical text document, which is a popular and challenging task in the healthcare domain. To alleviate the problems of long-tail distribution and the lack of annotations of code-specific evidence, many previous works have proposed incorporating code knowledge to improve coding performance. However, existing methods often focus on a single type of knowledge and design specialized modules that are complex and incompatible with each other, thereby limiting their scalability and effectiveness. To address this issue, we propose GKI-ICD, a novel, general knowledge injection framework that integrates three key types of knowledge, namely ICD Description, ICD Synonym, and ICD Hierarchy, without specialized design of additional modules. The comprehensive utilization of the above knowledge, which exhibits both differences and complementarity, can effectively enhance the ICD coding performance. Extensive experiments on existing popular ICD coding benchmarks demonstrate the effectiveness of GKI-ICD, which achieves the state-of-the-art performance on most evaluation metrics. Code is available at https://github.com/xuzhang0112/GKI-ICD.", "date": "2025-05-24", "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": "2503.20556", "title": "A Retrieval-Based Approach to Medical Procedure Matching in Romanian", "abstract": "Accurately mapping medical procedure names from healthcare providers to standardized terminology used by insurance companies is a crucial yet complex task. Inconsistencies in naming conventions lead to missclasified procedures, causing administrative inefficiencies and insurance claim problems in private healthcare settings. Many companies still use human resources for manual mapping, while there is a clear opportunity for automation. This paper proposes a retrieval-based architecture leveraging sentence embeddings for medical name matching in the Romanian healthcare system. This challenge is significantly more difficult in underrepresented languages such as Romanian, where existing pretrained language models lack domain-specific adaptation to medical text. We evaluate multiple embedding models, including Romanian, multilingual, and medical-domain-specific representations, to identify the most effective solution for this task. Our findings contribute to the broader field of medical NLP for low-resource languages such as Romanian.", "date": "2025-03-26", "categories": []}
{"id": "2505.15024", "title": "Diagnosing our datasets: How does my language model learn clinical information?", "abstract": "Large language models (LLMs) have performed well across various clinical natural language processing tasks, despite not being directly trained on electronic health record (EHR) data. In this work, we examine how popular open-source LLMs learn clinical information from large mined corpora through two crucial but understudied lenses: (1) their interpretation of clinical jargon, a foundational ability for understanding real-world clinical notes, and (2) their responses to unsupported medical claims. For both use cases, we investigate the frequency of relevant clinical information in their corresponding pretraining corpora, the relationship between pretraining data composition and model outputs, and the sources underlying this data. To isolate clinical jargon understanding, we evaluate LLMs on a new dataset MedLingo. Unsurprisingly, we find that the frequency of clinical jargon mentions across major pretraining corpora correlates with model performance. However, jargon frequently appearing in clinical notes often rarely appears in pretraining corpora, revealing a mismatch between available data and real-world usage. Similarly, we find that a non-negligible portion of documents support disputed claims that can then be parroted by models. Finally, we classified and analyzed the types of online sources in which clinical jargon and unsupported medical claims appear, with implications for future dataset composition.", "date": "2025-05-21", "categories": []}
{"id": "2505.17047", "title": "Assessing the Quality of AI-Generated Clinical Notes: A Validated Evaluation of a Large Language Model Scribe", "abstract": "In medical practices across the United States, physicians have begun implementing generative artificial intelligence (AI) tools to perform the function of scribes in order to reduce the burden of documenting clinical encounters. Despite their widespread use, no established methods exist to gauge the quality of AI scribes. To address this gap, we developed a blinded study comparing the relative performance of large language model (LLM) generated clinical notes with those from field experts based on audio-recorded clinical encounters. Quantitative metrics from the Physician Documentation Quality Instrument (PDQI9) provided a framework to measure note quality, which we adapted to assess relative performance of AI generated notes. Clinical experts spanning 5 medical specialties used the PDQI9 tool to evaluate specialist-drafted Gold notes and LLM authored Ambient notes. Two evaluators from each specialty scored notes drafted from a total of 97 patient visits. We found uniformly high inter rater agreement (RWG greater than 0.7) between evaluators in general medicine, orthopedics, and obstetrics and gynecology, and moderate (RWG 0.5 to 0.7) to high inter rater agreement in pediatrics and cardiology. We found a modest yet significant difference in the overall note quality, wherein Gold notes achieved a score of 4.25 out of 5 and Ambient notes scored 4.20 out of 5 (p = 0.04). Our findings support the use of the PDQI9 instrument as a practical method to gauge the quality of LLM authored notes, as compared to human-authored notes.", "date": "2025-05-15", "categories": []}
{"id": "2505.17065", "title": "Decoding Rarity: Large Language Models in the Diagnosis of Rare Diseases", "abstract": "Recent advances in artificial intelligence, particularly large language models LLMs, have shown promising capabilities in transforming rare disease research. This survey paper explores the integration of LLMs in the analysis of rare diseases, highlighting significant strides and pivotal studies that leverage textual data to uncover insights and patterns critical for diagnosis, treatment, and patient care. While current research predominantly employs textual data, the potential for multimodal data integration combining genetic, imaging, and electronic health records stands as a promising frontier. We review foundational papers that demonstrate the application of LLMs in identifying and extracting relevant medical information, simulating intelligent conversational agents for patient interaction, and enabling the formulation of accurate and timely diagnoses. Furthermore, this paper discusses the challenges and ethical considerations inherent in deploying LLMs, including data privacy, model transparency, and the need for robust, inclusive data sets. As part of this exploration, we present a section on experimentation that utilizes multiple LLMs alongside structured questionnaires, specifically designed for diagnostic purposes in the context of different diseases. We conclude with future perspectives on the evolution of LLMs towards truly multimodal platforms, which would integrate diverse data types to provide a more comprehensive understanding of rare diseases, ultimately fostering better outcomes in clinical settings.", "date": "2025-05-18", "categories": []}
{"id": "2505.17167", "title": "CRG Score: A Distribution-Aware Clinical Metric for Radiology Report Generation", "abstract": "Evaluating long-context radiology report generation is challenging. NLG metrics fail to capture clinical correctness, while LLM-based metrics often lack generalizability. Clinical accuracy metrics are more relevant but are sensitive to class imbalance, frequently favoring trivial predictions. We propose the CRG Score, a distribution-aware and adaptable metric that evaluates only clinically relevant abnormalities explicitly described in reference reports. CRG supports both binary and structured labels (e.g., type, location) and can be paired with any LLM for feature extraction. By balancing penalties based on label distribution, it enables fairer, more robust evaluation and serves as a clinically aligned reward function.", "date": "2025-05-22", "categories": []}
{"id": "2505.17265", "title": "CaseReportBench: An LLM Benchmark Dataset for Dense Information Extraction in Clinical Case Reports", "abstract": "Rare diseases, including Inborn Errors of Metabolism (IEM), pose significant diagnostic challenges. Case reports serve as key but computationally underutilized resources to inform diagnosis. Clinical dense information extraction refers to organizing medical information into structured predefined categories. Large Language Models (LLMs) may enable scalable information extraction from case reports but are rarely evaluated for this task. We introduce CaseReportBench, an expert-annotated dataset for dense information extraction of case reports, focusing on IEMs. Using this dataset, we assess various models and prompting strategies, introducing novel approaches such as category-specific prompting and subheading-filtered data integration. Zero-shot chain-of-thought prompting offers little advantage over standard zero-shot prompting. Category-specific prompting improves alignment with the benchmark. The open-source model Qwen2.5-7B outperforms GPT-4o for this task. Our clinician evaluations show that LLMs can extract clinically relevant details from case reports, supporting rare disease diagnosis and management. We also highlight areas for improvement, such as LLMs' limitations in recognizing negative findings important for differential diagnosis. This work advances LLM-driven clinical natural language processing and paves the way for scalable medical AI applications.", "date": "2025-05-22", "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": "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": "2505.11733", "title": "MedCaseReasoning: Evaluating and learning diagnostic reasoning from clinical case reports", "abstract": "Doctors and patients alike increasingly use Large Language Models (LLMs) to diagnose clinical cases. However, unlike domains such as math or coding, where correctness can be objectively defined by the final answer, medical diagnosis requires both the outcome and the reasoning process to be accurate. Currently, widely used medical benchmarks like MedQA and MMLU assess only accuracy in the final answer, overlooking the quality and faithfulness of the clinical reasoning process. To address this limitation, we introduce MedCaseReasoning, the first open-access dataset for evaluating LLMs on their ability to align with clinician-authored diagnostic reasoning. The dataset includes 14,489 diagnostic question-and-answer cases, each paired with detailed reasoning statements derived from open-access medical case reports. We evaluate state-of-the-art reasoning LLMs on MedCaseReasoning and find significant shortcomings in their diagnoses and reasoning: for instance, the top-performing open-source model, DeepSeek-R1, achieves only 48% 10-shot diagnostic accuracy and mentions only 64% of the clinician reasoning statements (recall). However, we demonstrate that fine-tuning LLMs on the reasoning traces derived from MedCaseReasoning significantly improves diagnostic accuracy and clinical reasoning recall by an average relative gain of 29% and 41%, respectively. The open-source dataset, code, and models are available at https://github.com/kevinwu23/Stanford-MedCaseReasoning.", "date": "2025-05-16", "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": "2408.12249", "title": "LLMs are not Zero-Shot Reasoners for Biomedical Information Extraction", "abstract": "Large Language Models (LLMs) are increasingly adopted for applications in healthcare, reaching the performance of domain experts on tasks such as question answering and document summarisation. Despite their success on these tasks, it is unclear how well LLMs perform on tasks that are traditionally pursued in the biomedical domain, such as structured information extraction. To bridge this gap, in this paper, we systematically benchmark LLM performance in Medical Classification and Named Entity Recognition (NER) tasks. We aim to disentangle the contribution of different factors to the performance, particularly the impact of LLMs' task knowledge and reasoning capabilities, their (parametric) domain knowledge, and addition of external knowledge. To this end, we evaluate various open LLMs - including BioMistral and Llama-2 models - on a diverse set of biomedical datasets, using standard prompting, Chain of-Thought (CoT) and Self Consistency based reasoning as well as Retrieval-Augmented Generation (RAG) with PubMed and Wikipedia corpora. Counter intuitively, our results reveal that standard prompting consistently outperforms more complex techniques across both tasks, laying bare the limitations in the current application of CoT, self-consistency and RAG in the biomedical domain. Our findings suggest that advanced prompting methods developed for knowledge- or reasoning-intensive tasks, such as CoT or RAG, are not easily portable to biomedical tasks where precise structured outputs are required. This highlights the need for more effective integration of external knowledge and reasoning mechanisms in LLMs to enhance their performance in real-world biomedical applications.", "date": "2024-08-22", "categories": []}
{"id": "2503.03261", "title": "Can Frontier LLMs Replace Annotators in Biomedical Text Mining? Analyzing Challenges and Exploring Solutions", "abstract": "Multiple previous studies have reported suboptimal performance of LLMs in biomedical text mining. By analyzing failure patterns in these evaluations, we identified three primary challenges for LLMs in biomedical corpora: (1) LLMs fail to learn implicit dataset-specific nuances from supervised data, (2) The common formatting requirements of discriminative tasks limit the reasoning capabilities of LLMs particularly for LLMs that lack test-time compute, and (3) LLMs struggle to adhere to annotation guidelines and match exact schemas, which hinders their ability to understand detailed annotation requirements which is essential in biomedical annotation workflow. We experimented with prompt engineering techniques targeted to the above issues, and developed a pipeline that dynamically extracts instructions from annotation guidelines. Our results show that frontier LLMs can approach or surpass the performance of SOTA BERT-based models with minimal reliance on manually annotated data and without fine-tuning. Furthermore, we performed model distillation on a closed-source LLM, demonstrating that a BERT model trained exclusively on synthetic data annotated by LLMs can also achieve a practical performance. Based on these findings, we explored the feasibility of partially replacing manual annotation with LLMs in production scenarios for biomedical text mining.", "date": "2025-03-05", "categories": []}
{"id": "2505.13418", "title": "Dementia Through Different Eyes: Explainable Modeling of Human and LLM Perceptions for Early Awareness", "abstract": "Cognitive decline often surfaces in language years before diagnosis. It is frequently non-experts, such as those closest to the patient, who first sense a change and raise concern. As LLMs become integrated into daily communication and used over prolonged periods, it may even be an LLM that notices something is off. But what exactly do they notice--and should be noticing--when making that judgment? This paper investigates how dementia is perceived through language by non-experts. We presented transcribed picture descriptions to non-expert humans and LLMs, asking them to intuitively judge whether each text was produced by someone healthy or with dementia. We introduce an explainable method that uses LLMs to extract high-level, expert-guided features representing these picture descriptions, and use logistic regression to model human and LLM perceptions and compare with clinical diagnoses. Our analysis reveals that human perception of dementia is inconsistent and relies on a narrow, and sometimes misleading, set of cues. LLMs, by contrast, draw on a richer, more nuanced feature set that aligns more closely with clinical patterns. Still, both groups show a tendency toward false negatives, frequently overlooking dementia cases. Through our interpretable framework and the insights it provides, we hope to help non-experts better recognize the linguistic signs that matter.", "date": "2025-05-19", "categories": []}
{"id": "2505.10780", "title": "SECRET: Semi-supervised Clinical Trial Document Similarity Search", "abstract": "Clinical trials are vital for evaluation of safety and efficacy of new treatments. However, clinical trials are resource-intensive, time-consuming and expensive to conduct, where errors in trial design, reduced efficacy, and safety events can result in significant delays, financial losses, and damage to reputation. These risks underline the importance of informed and strategic decisions in trial design to mitigate these risks and improve the chances of a successful trial. Identifying similar historical trials is critical as these trials can provide an important reference for potential pitfalls and challenges including serious adverse events, dosage inaccuracies, recruitment difficulties, patient adherence issues, etc. Addressing these challenges in trial design can lead to development of more effective study protocols with optimized patient safety and trial efficiency. In this paper, we present a novel method to identify similar historical trials by summarizing clinical trial protocols and searching for similar trials based on a query trial's protocol. Our approach significantly outperforms all baselines, achieving up to a 78% improvement in recall@1 and a 53% improvement in precision@1 over the best baseline. We also show that our method outperforms all other baselines in partial trial similarity search and zero-shot patient-trial matching, highlighting its superior utility in these tasks.", "date": "2025-05-16", "categories": []}
{"id": "2505.09794", "title": "Automated Detection of Clinical Entities in Lung and Breast Cancer Reports Using NLP Techniques", "abstract": "Research projects, including those focused on cancer, rely on the manual extraction of information from clinical reports. This process is time-consuming and prone to errors, limiting the efficiency of data-driven approaches in healthcare. To address these challenges, Natural Language Processing (NLP) offers an alternative for automating the extraction of relevant data from electronic health records (EHRs). In this study, we focus on lung and breast cancer due to their high incidence and the significant impact they have on public health. Early detection and effective data management in both types of cancer are crucial for improving patient outcomes. To enhance the accuracy and efficiency of data extraction, we utilized GMV's NLP tool uQuery, which excels at identifying relevant entities in clinical texts and converting them into standardized formats such as SNOMED and OMOP. uQuery not only detects and classifies entities but also associates them with contextual information, including negated entities, temporal aspects, and patient-related details. In this work, we explore the use of NLP techniques, specifically Named Entity Recognition (NER), to automatically identify and extract key clinical information from EHRs related to these two cancers. A dataset from Health Research Institute Hospital La Fe (IIS La Fe), comprising 200 annotated breast cancer and 400 lung cancer reports, was used, with eight clinical entities manually labeled using the Doccano platform. To perform NER, we fine-tuned the bsc-bio-ehr-en3 model, a RoBERTa-based biomedical linguistic model pre-trained in Spanish. Fine-tuning was performed using the Transformers architecture, enabling accurate recognition of clinical entities in these cancer types. Our results demonstrate strong overall performance, particularly in identifying entities like MET and PAT, although challenges remain with less frequent entities like EVOL.", "date": "2025-05-14", "categories": []}
{"id": "2505.10261", "title": "The Evolving Landscape of Generative Large Language Models and Traditional Natural Language Processing in Medicine", "abstract": "Natural language processing (NLP) has been traditionally applied to medicine, and generative large language models (LLMs) have become prominent recently. However, the differences between them across different medical tasks remain underexplored. We analyzed 19,123 studies, finding that generative LLMs demonstrate advantages in open-ended tasks, while traditional NLP dominates in information extraction and analysis tasks. As these technologies advance, ethical use of them is essential to ensure their potential in medical applications.", "date": "2025-05-15", "categories": []}
{"id": "2505.10409", "title": "Are LLM-generated plain language summaries truly understandable? A large-scale crowdsourced evaluation", "abstract": "Plain language summaries (PLSs) are essential for facilitating effective communication between clinicians and patients by making complex medical information easier for laypeople to understand and act upon. Large language models (LLMs) have recently shown promise in automating PLS generation, but their effectiveness in supporting health information comprehension remains unclear. Prior evaluations have generally relied on automated scores that do not measure understandability directly, or subjective Likert-scale ratings from convenience samples with limited generalizability. To address these gaps, we conducted a large-scale crowdsourced evaluation of LLM-generated PLSs using Amazon Mechanical Turk with 150 participants. We assessed PLS quality through subjective Likert-scale ratings focusing on simplicity, informativeness, coherence, and faithfulness; and objective multiple-choice comprehension and recall measures of reader understanding. Additionally, we examined the alignment between 10 automated evaluation metrics and human judgments. Our findings indicate that while LLMs can generate PLSs that appear indistinguishable from human-written ones in subjective evaluations, human-written PLSs lead to significantly better comprehension. Furthermore, automated evaluation metrics fail to reflect human judgment, calling into question their suitability for evaluating PLSs. This is the first study to systematically evaluate LLM-generated PLSs based on both reader preferences and comprehension outcomes. Our findings highlight the need for evaluation frameworks that move beyond surface-level quality and for generation methods that explicitly optimize for layperson comprehension.", "date": "2025-05-15", "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": "2505.06912", "title": "Building a Human-Verified Clinical Reasoning Dataset via a Human LLM Hybrid Pipeline for Trustworthy Medical AI", "abstract": "Despite strong performance in medical question-answering, the clinical adoption of Large Language Models (LLMs) is critically hampered by their opaque 'black-box' reasoning, limiting clinician trust. This challenge is compounded by the predominant reliance of current medical LLMs on corpora from scientific literature or synthetic data, which often lack the granular expert validation and high clinical relevance essential for advancing their specialized medical capabilities. To address these critical gaps, we introduce a highly clinically relevant dataset with 31,247 medical question-answer pairs, each accompanied by expert-validated chain-of-thought (CoT) explanations. This resource, spanning multiple clinical domains, was curated via a scalable human-LLM hybrid pipeline: LLM-generated rationales were iteratively reviewed, scored, and refined by medical experts against a structured rubric, with substandard outputs revised through human effort or guided LLM regeneration until expert consensus. This publicly available dataset provides a vital source for the development of medical LLMs that capable of transparent and verifiable reasoning, thereby advancing safer and more interpretable AI in medicine.", "date": "2025-05-11", "categories": []}
{"id": "2505.07162", "title": "KDH-MLTC: Knowledge Distillation for Healthcare Multi-Label Text Classification", "abstract": "The increasing volume of healthcare textual data requires computationally efficient, yet highly accurate classification approaches able to handle the nuanced and complex nature of medical terminology. This research presents Knowledge Distillation for Healthcare Multi-Label Text Classification (KDH-MLTC), a framework leveraging model compression and Large Language Models (LLMs). The proposed approach addresses conventional healthcare Multi-Label Text Classification (MLTC) challenges by integrating knowledge distillation and sequential fine-tuning, subsequently optimized through Particle Swarm Optimization (PSO) for hyperparameter tuning. KDH-MLTC transfers knowledge from a more complex teacher LLM (i.e., BERT) to a lighter student LLM (i.e., DistilBERT) through sequential training adapted to MLTC that preserves the teacher's learned information while significantly reducing computational requirements. As a result, the classification is enabled to be conducted locally, making it suitable for healthcare textual data characterized by sensitivity and, therefore, ensuring HIPAA compliance. The experiments conducted on three medical literature datasets of different sizes, sampled from the Hallmark of Cancer (HoC) dataset, demonstrate that KDH-MLTC achieves superior performance compared to existing approaches, particularly for the largest dataset, reaching an F1 score of 82.70%. Additionally, statistical validation and an ablation study are carried out, proving the robustness of KDH-MLTC. Furthermore, the PSO-based hyperparameter optimization process allowed the identification of optimal configurations. The proposed approach contributes to healthcare text classification research, balancing efficiency requirements in resource-constrained healthcare settings with satisfactory accuracy demands.", "date": "2025-05-12", "categories": []}
{"id": "2505.06151", "title": "Estimating Quality in Therapeutic Conversations: A Multi-Dimensional Natural Language Processing Framework", "abstract": "Engagement between client and therapist is a critical determinant of therapeutic success. We propose a multi-dimensional natural language processing (NLP) framework that objectively classifies engagement quality in counseling sessions based on textual transcripts. Using 253 motivational interviewing transcripts (150 high-quality, 103 low-quality), we extracted 42 features across four domains: conversational dynamics, semantic similarity as topic alignment, sentiment classification, and question detection. Classifiers, including Random Forest (RF), Cat-Boost, and Support Vector Machines (SVM), were hyperparameter tuned and trained using a stratified 5-fold cross-validation and evaluated on a holdout test set. On balanced (non-augmented) data, RF achieved the highest classification accuracy (76.7%), and SVM achieved the highest AUC (85.4%). After SMOTE-Tomek augmentation, performance improved significantly: RF achieved up to 88.9% accuracy, 90.0% F1-score, and 94.6% AUC, while SVM reached 81.1% accuracy, 83.1% F1-score, and 93.6% AUC. The augmented data results reflect the potential of the framework in future larger-scale applications. Feature contribution revealed conversational dynamics and semantic similarity between clients and therapists were among the top contributors, led by words uttered by the client (mean and standard deviation). The framework was robust across the original and augmented datasets and demonstrated consistent improvements in F1 scores and recall. While currently text-based, the framework supports future multimodal extensions (e.g., vocal tone, facial affect) for more holistic assessments. This work introduces a scalable, data-driven method for evaluating engagement quality of the therapy session, offering clinicians real-time feedback to enhance the quality of both virtual and in-person therapeutic interactions.", "date": "2025-05-09", "categories": []}
{"id": "2503.15169", "title": "Benchmarking Open-Source Large Language Models on Healthcare Text\n  Classification Tasks", "abstract": "  The application of large language models (LLMs) to healthcare information\nextraction has emerged as a promising approach. This study evaluates the\nclassification performance of five open-source LLMs: GEMMA-3-27B-IT,\nLLAMA3-70B, LLAMA4-109B, DEEPSEEK-R1-DISTILL-LLAMA-70B, and\nDEEPSEEK-V3-0324-UD-Q2_K_XL, across six healthcare-related classification tasks\ninvolving both social media data (breast cancer, changes in medication regimen,\nadverse pregnancy outcomes, potential COVID-19 cases) and clinical data (stigma\nlabeling, medication change discussion). We report precision, recall, and F1\nscores with 95% confidence intervals for all model-task combinations. Our\nfindings reveal significant performance variability between LLMs, with\nDeepSeekV3 emerging as the strongest overall performer, achieving the highest\nF1 scores in four tasks. Notably, models generally performed better on social\nmedia tasks compared to clinical data tasks, suggesting potential\ndomain-specific challenges. GEMMA-3-27B-IT demonstrated exceptionally high\nrecall despite its smaller parameter count, while LLAMA4-109B showed\nsurprisingly underwhelming performance compared to its predecessor LLAMA3-70B,\nindicating that larger parameter counts do not guarantee improved\nclassification results. We observed distinct precision-recall trade-offs across\nmodels, with some favoring sensitivity over specificity and vice versa. These\nfindings highlight the importance of task-specific model selection for\nhealthcare applications, considering the particular data domain and\nprecision-recall requirements rather than model size alone. As healthcare\nincreasingly integrates AI-driven text classification tools, this comprehensive\nbenchmarking provides valuable guidance for model selection and implementation\nwhile underscoring the need for continued evaluation and domain adaptation of\nLLMs in healthcare contexts.\n", "date": "2025-03-19", "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": "2505.04073", "title": "Natural Language Generation in Healthcare: A Review of Methods and\n  Applications", "abstract": "  Natural language generation (NLG) is the key technology to achieve generative\nartificial intelligence (AI). With the breakthroughs in large language models\n(LLMs), NLG has been widely used in various medical applications, demonstrating\nthe potential to enhance clinical workflows, support clinical decision-making,\nand improve clinical documentation. Heterogeneous and diverse medical data\nmodalities, such as medical text, images, and knowledge bases, are utilized in\nNLG. Researchers have proposed many generative models and applied them in a\nnumber of healthcare applications. There is a need for a comprehensive review\nof NLG methods and applications in the medical domain. In this study, we\nsystematically reviewed 113 scientific publications from a total of 3,988\nNLG-related articles identified using a literature search, focusing on data\nmodality, model architecture, clinical applications, and evaluation methods.\nFollowing PRISMA (Preferred Reporting Items for Systematic reviews and\nMeta-Analyses) guidelines, we categorize key methods, identify clinical\napplications, and assess their capabilities, limitations, and emerging\nchallenges. This timely review covers the key NLG technologies and medical\napplications and provides valuable insights for future studies to leverage NLG\nto transform medical discovery and healthcare.\n", "date": "2025-05-07", "categories": []}
{"id": "2505.04284", "title": "GASCADE: Grouped Summarization of Adverse Drug Event for Enhanced Cancer\n  Pharmacovigilance", "abstract": "  In the realm of cancer treatment, summarizing adverse drug events (ADEs)\nreported by patients using prescribed drugs is crucial for enhancing\npharmacovigilance practices and improving drug-related decision-making. While\nthe volume and complexity of pharmacovigilance data have increased, existing\nresearch in this field has predominantly focused on general diseases rather\nthan specifically addressing cancer. This work introduces the task of grouped\nsummarization of adverse drug events reported by multiple patients using the\nsame drug for cancer treatment. To address the challenge of limited resources\nin cancer pharmacovigilance, we present the MultiLabeled Cancer Adverse Drug\nReaction and Summarization (MCADRS) dataset. This dataset includes\npharmacovigilance posts detailing patient concerns regarding drug efficacy and\nadverse effects, along with extracted labels for drug names, adverse drug\nevents, severity, and adversity of reactions, as well as summaries of ADEs for\neach drug. Additionally, we propose the Grouping and Abstractive Summarization\nof Cancer Adverse Drug events (GASCADE) framework, a novel pipeline that\ncombines the information extraction capabilities of Large Language Models\n(LLMs) with the summarization power of the encoder-decoder T5 model. Our work\nis the first to apply alignment techniques, including advanced algorithms like\nDirect Preference Optimization, to encoder-decoder models using synthetic\ndatasets for summarization tasks. Through extensive experiments, we demonstrate\nthe superior performance of GASCADE across various metrics, validated through\nboth automated assessments and human evaluations. This multitasking approach\nenhances drug-related decision-making and fosters a deeper understanding of\npatient concerns, paving the way for advancements in personalized and\nresponsive cancer care. The code and dataset used in this work are publicly\navailable.\n", "date": "2025-05-07", "categories": []}
{"id": "2505.03467", "title": "Uncertainty-Aware Large Language Models for Explainable Disease\n  Diagnosis", "abstract": "  Explainable disease diagnosis, which leverages patient information (e.g.,\nsigns and symptoms) and computational models to generate probable diagnoses and\nreasonings, offers clear clinical values. However, when clinical notes\nencompass insufficient evidence for a definite diagnosis, such as the absence\nof definitive symptoms, diagnostic uncertainty usually arises, increasing the\nrisk of misdiagnosis and adverse outcomes. Although explicitly identifying and\nexplaining diagnostic uncertainties is essential for trustworthy diagnostic\nsystems, it remains under-explored. To fill this gap, we introduce ConfiDx, an\nuncertainty-aware large language model (LLM) created by fine-tuning open-source\nLLMs with diagnostic criteria. We formalized the task and assembled richly\nannotated datasets that capture varying degrees of diagnostic ambiguity.\nEvaluating ConfiDx on real-world datasets demonstrated that it excelled in\nidentifying diagnostic uncertainties, achieving superior diagnostic\nperformance, and generating trustworthy explanations for diagnoses and\nuncertainties. To our knowledge, this is the first study to jointly address\ndiagnostic uncertainty recognition and explanation, substantially enhancing the\nreliability of automatic diagnostic systems.\n", "date": "2025-05-06", "categories": []}
{"id": "2505.01693", "title": "High-Fidelity Pseudo-label Generation by Large Language Models for\n  Training Robust Radiology Report Classifiers", "abstract": "  Automated labeling of chest X-ray reports is essential for enabling\ndownstream tasks such as training image-based diagnostic models, population\nhealth studies, and clinical decision support. However, the high variability,\ncomplexity, and prevalence of negation and uncertainty in these free-text\nreports pose significant challenges for traditional Natural Language Processing\nmethods. While large language models (LLMs) demonstrate strong text\nunderstanding, their direct application for large-scale, efficient labeling is\nlimited by computational cost and speed. This paper introduces DeBERTa-RAD, a\nnovel two-stage framework that combines the power of state-of-the-art LLM\npseudo-labeling with efficient DeBERTa-based knowledge distillation for\naccurate and fast chest X-ray report labeling. We leverage an advanced LLM to\ngenerate high-quality pseudo-labels, including certainty statuses, for a large\ncorpus of reports. Subsequently, a DeBERTa-Base model is trained on this\npseudo-labeled data using a tailored knowledge distillation strategy. Evaluated\non the expert-annotated MIMIC-500 benchmark, DeBERTa-RAD achieves a\nstate-of-the-art Macro F1 score of 0.9120, significantly outperforming\nestablished rule-based systems, fine-tuned transformer models, and direct LLM\ninference, while maintaining a practical inference speed suitable for\nhigh-throughput applications. Our analysis shows particular strength in\nhandling uncertain findings. This work demonstrates a promising path to\novercome data annotation bottlenecks and achieve high-performance medical text\nprocessing through the strategic combination of LLM capabilities and efficient\nstudent models trained via distillation.\n", "date": "2025-05-03", "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": "2504.21851", "title": "TRUST: An LLM-Based Dialogue System for Trauma Understanding and\n  Structured Assessments", "abstract": "  Objectives: While Large Language Models (LLMs) have been widely used to\nassist clinicians and support patients, no existing work has explored dialogue\nsystems for standard diagnostic interviews and assessments. This study aims to\nbridge the gap in mental healthcare accessibility by developing an LLM-powered\ndialogue system that replicates clinician behavior. Materials and Methods: We\nintroduce TRUST, a framework of cooperative LLM modules capable of conducting\nformal diagnostic interviews and assessments for Post-Traumatic Stress Disorder\n(PTSD). To guide the generation of appropriate clinical responses, we propose a\nDialogue Acts schema specifically designed for clinical interviews.\nAdditionally, we develop a patient simulation approach based on real-life\ninterview transcripts to replace time-consuming and costly manual testing by\nclinicians. Results: A comprehensive set of evaluation metrics is designed to\nassess the dialogue system from both the agent and patient simulation\nperspectives. Expert evaluations by conversation and clinical specialists show\nthat TRUST performs comparably to real-life clinical interviews. Discussion:\nOur system performs at the level of average clinicians, with room for future\nenhancements in communication styles and response appropriateness. Conclusions:\nOur TRUST framework shows its potential to facilitate mental healthcare\navailability.\n", "date": "2025-04-30", "categories": []}
{"id": "2504.20220", "title": "A Multimodal Pipeline for Clinical Data Extraction: Applying\n  Vision-Language Models to Scans of Transfusion Reaction Reports", "abstract": "  Despite the growing adoption of electronic health records, many processes\nstill rely on paper documents, reflecting the heterogeneous real-world\nconditions in which healthcare is delivered. The manual transcription process\nis time-consuming and prone to errors when transferring paper-based data to\ndigital formats. To streamline this workflow, this study presents an\nopen-source pipeline that extracts and categorizes checkbox data from scanned\ndocuments. Demonstrated on transfusion reaction reports, the design supports\nadaptation to other checkbox-rich document types. The proposed method\nintegrates checkbox detection, multilingual optical character recognition (OCR)\nand multilingual vision-language models (VLMs). The pipeline achieves high\nprecision and recall compared against annually compiled gold-standards from\n2017 to 2024. The result is a reduction in administrative workload and accurate\nregulatory reporting. The open-source availability of this pipeline encourages\nself-hosted parsing of checkbox forms.\n", "date": "2025-04-28", "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": "2305.16326", "title": "Benchmarking large language models for biomedical natural language\n  processing applications and recommendations", "abstract": "  The rapid growth of biomedical literature poses challenges for manual\nknowledge curation and synthesis. Biomedical Natural Language Processing\n(BioNLP) automates the process. While Large Language Models (LLMs) have shown\npromise in general domains, their effectiveness in BioNLP tasks remains unclear\ndue to limited benchmarks and practical guidelines.\n  We perform a systematic evaluation of four LLMs, GPT and LLaMA\nrepresentatives on 12 BioNLP benchmarks across six applications. We compare\ntheir zero-shot, few-shot, and fine-tuning performance with traditional\nfine-tuning of BERT or BART models. We examine inconsistencies, missing\ninformation, hallucinations, and perform cost analysis. Here we show that\ntraditional fine-tuning outperforms zero or few shot LLMs in most tasks.\nHowever, closed-source LLMs like GPT-4 excel in reasoning-related tasks such as\nmedical question answering. Open source LLMs still require fine-tuning to close\nperformance gaps. We find issues like missing information and hallucinations in\nLLM outputs. These results offer practical insights for applying LLMs in\nBioNLP.\n", "date": "2023-05-10", "categories": []}
{"id": "2504.18569", "title": "Large Language Model Empowered Privacy-Protected Framework for PHI\n  Annotation in Clinical Notes", "abstract": "  The de-identification of private information in medical data is a crucial\nprocess to mitigate the risk of confidentiality breaches, particularly when\npatient personal details are not adequately removed before the release of\nmedical records. Although rule-based and learning-based methods have been\nproposed, they often struggle with limited generalizability and require\nsubstantial amounts of annotated data for effective performance. Recent\nadvancements in large language models (LLMs) have shown significant promise in\naddressing these issues due to their superior language comprehension\ncapabilities. However, LLMs present challenges, including potential privacy\nrisks when using commercial LLM APIs and high computational costs for deploying\nopen-source LLMs locally. In this work, we introduce LPPA, an LLM-empowered\nPrivacy-Protected PHI Annotation framework for clinical notes, targeting the\nEnglish language. By fine-tuning LLMs locally with synthetic notes, LPPA\nensures strong privacy protection and high PHI annotation accuracy. Extensive\nexperiments demonstrate LPPA's effectiveness in accurately de-identifying\nprivate information, offering a scalable and efficient solution for enhancing\npatient privacy protection.\n", "date": "2025-04-22", "categories": []}
{"id": "2402.11908", "title": "Semantic Textual Similarity Assessment in Chest X-ray Reports Using a\n  Domain-Specific Cosine-Based Metric", "abstract": "  Medical language processing and deep learning techniques have emerged as\ncritical tools for improving healthcare, particularly in the analysis of\nmedical imaging and medical text data. These multimodal data fusion techniques\nhelp to improve the interpretation of medical imaging and lead to increased\ndiagnostic accuracy, informed clinical decisions, and improved patient\noutcomes. The success of these models relies on the ability to extract and\nconsolidate semantic information from clinical text. This paper addresses the\nneed for more robust methods to evaluate the semantic content of medical\nreports. Conventional natural language processing approaches and metrics are\ninitially designed for considering the semantic context in the natural language\ndomain and machine translation, often failing to capture the complex semantic\nmeanings inherent in medical content. In this study, we introduce a novel\napproach designed specifically for assessing the semantic similarity between\ngenerated medical reports and the ground truth. Our approach is validated,\ndemonstrating its efficiency in assessing domain-specific semantic similarity\nwithin medical contexts. By applying our metric to state-of-the-art Chest X-ray\nreport generation models, we obtain results that not only align with\nconventional metrics but also provide more contextually meaningful scores in\nthe considered medical domain.\n", "date": "2024-02-19", "categories": []}
{"id": "2504.17119", "title": "The Rise of Small Language Models in Healthcare: A Comprehensive Survey", "abstract": "  Despite substantial progress in healthcare applications driven by large\nlanguage models (LLMs), growing concerns around data privacy, and limited\nresources; the small language models (SLMs) offer a scalable and clinically\nviable solution for efficient performance in resource-constrained environments\nfor next-generation healthcare informatics. Our comprehensive survey presents a\ntaxonomic framework to identify and categorize them for healthcare\nprofessionals and informaticians. The timeline of healthcare SLM contributions\nestablishes a foundational framework for analyzing models across three\ndimensions: NLP tasks, stakeholder roles, and the continuum of care. We present\na taxonomic framework to identify the architectural foundations for building\nmodels from scratch; adapting SLMs to clinical precision through prompting,\ninstruction fine-tuning, and reasoning; and accessibility and sustainability\nthrough compression techniques. Our primary objective is to offer a\ncomprehensive survey for healthcare professionals, introducing recent\ninnovations in model optimization and equipping them with curated resources to\nsupport future research and development in the field. Aiming to showcase the\ngroundbreaking advancements in SLMs for healthcare, we present a comprehensive\ncompilation of experimental results across widely studied NLP tasks in\nhealthcare to highlight the transformative potential of SLMs in healthcare. The\nupdated repository is available at Github\n", "date": "2025-04-23", "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": "2409.18986", "title": "Lab-AI: Using Retrieval Augmentation to Enhance Language Models for\n  Personalized Lab Test Interpretation in Clinical Medicine", "abstract": "  Accurate interpretation of lab results is crucial in clinical medicine, yet\nmost patient portals use universal normal ranges, ignoring conditional factors\nlike age and gender. This study introduces Lab-AI, an interactive system that\noffers personalized normal ranges using retrieval-augmented generation (RAG)\nfrom credible health sources. Lab-AI has two modules: factor retrieval and\nnormal range retrieval. We tested these on 122 lab tests: 40 with conditional\nfactors and 82 without. For tests with factors, normal ranges depend on\npatient-specific information. Our results show GPT-4-turbo with RAG achieved a\n0.948 F1 score for factor retrieval and 0.995 accuracy for normal range\nretrieval. GPT-4-turbo with RAG outperformed the best non-RAG system by 33.5%\nin factor retrieval and showed 132% and 100% improvements in question-level and\nlab-level performance, respectively, for normal range retrieval. These findings\nhighlight Lab-AI's potential to enhance patient understanding of lab results.\n", "date": "2024-09-16", "categories": []}
{"id": "2403.05720", "title": "A dataset and benchmark for hospital course summarization with adapted\n  large language models", "abstract": "  Brief hospital course (BHC) summaries are clinical documents that summarize a\npatient's hospital stay. While large language models (LLMs) depict remarkable\ncapabilities in automating real-world tasks, their capabilities for healthcare\napplications such as synthesizing BHCs from clinical notes have not been shown.\nWe introduce a novel pre-processed dataset, the MIMIC-IV-BHC, encapsulating\nclinical note and brief hospital course (BHC) pairs to adapt LLMs for BHC\nsynthesis. Furthermore, we introduce a benchmark of the summarization\nperformance of two general-purpose LLMs and three healthcare-adapted LLMs.\nUsing clinical notes as input, we apply prompting-based (using in-context\nlearning) and fine-tuning-based adaptation strategies to three open-source LLMs\n(Clinical-T5-Large, Llama2-13B, FLAN-UL2) and two proprietary LLMs (GPT-3.5,\nGPT-4). We evaluate these LLMs across multiple context-length inputs using\nnatural language similarity metrics. We further conduct a clinical study with\nfive clinicians, comparing clinician-written and LLM-generated BHCs across 30\nsamples, focusing on their potential to enhance clinical decision-making\nthrough improved summary quality. We observe that the Llama2-13B fine-tuned LLM\noutperforms other domain-adapted models given quantitative evaluation metrics\nof BLEU and BERT-Score. GPT-4 with in-context learning shows more robustness to\nincreasing context lengths of clinical note inputs than fine-tuned Llama2-13B.\nDespite comparable quantitative metrics, the reader study depicts a significant\npreference for summaries generated by GPT-4 with in-context learning compared\nto both Llama2-13B fine-tuned summaries and the original summaries,\nhighlighting the need for qualitative clinical evaluation.\n", "date": "2024-03-08", "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": "2504.16271", "title": "The Language of Attachment: Modeling Attachment Dynamics in\n  Psychotherapy", "abstract": "  The delivery of mental healthcare through psychotherapy stands to benefit\nimmensely from developments within Natural Language Processing (NLP), in\nparticular through the automatic identification of patient specific qualities,\nsuch as attachment style. Currently, the assessment of attachment style is\nperformed manually using the Patient Attachment Coding System (PACS; Talia et\nal., 2017), which is complex, resource-consuming and requires extensive\ntraining. To enable wide and scalable adoption of attachment informed treatment\nand research, we propose the first exploratory analysis into automatically\nassessing patient attachment style from psychotherapy transcripts using NLP\nclassification models. We further analyze the results and discuss the\nimplications of using automated tools for this purpose -- e.g., confusing\n`preoccupied' patients with `avoidant' likely has a more negative impact on\ntherapy outcomes with respect to other mislabeling. Our work opens an avenue of\nresearch enabling more personalized psychotherapy and more targeted research\ninto the mechanisms of psychotherapy through advancements in NLP.\n", "date": "2025-04-22", "categories": []}
{"id": "2504.16448", "title": "EMRModel: A Large Language Model for Extracting Medical Consultation\n  Dialogues into Structured Medical Records", "abstract": "  Medical consultation dialogues contain critical clinical information, yet\ntheir unstructured nature hinders effective utilization in diagnosis and\ntreatment. Traditional methods, relying on rule-based or shallow machine\nlearning techniques, struggle to capture deep and implicit semantics. Recently,\nlarge pre-trained language models and Low-Rank Adaptation (LoRA), a lightweight\nfine-tuning method, have shown promise for structured information extraction.\nWe propose EMRModel, a novel approach that integrates LoRA-based fine-tuning\nwith code-style prompt design, aiming to efficiently convert medical\nconsultation dialogues into structured electronic medical records (EMRs).\nAdditionally, we construct a high-quality, realistically grounded dataset of\nmedical consultation dialogues with detailed annotations. Furthermore, we\nintroduce a fine-grained evaluation benchmark for medical consultation\ninformation extraction and provide a systematic evaluation methodology,\nadvancing the optimization of medical natural language processing (NLP) models.\nExperimental results show EMRModel achieves an F1 score of 88.1%, improving\nby49.5% over standard pre-trained models. Compared to traditional LoRA\nfine-tuning methods, our model shows superior performance, highlighting its\neffectiveness in structured medical record extraction tasks.\n", "date": "2025-04-23", "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": "2504.12350", "title": "A Large-Language Model Framework for Relative Timeline Extraction from\n  PubMed Case Reports", "abstract": "  Timing of clinical events is central to characterization of patient\ntrajectories, enabling analyses such as process tracing, forecasting, and\ncausal reasoning. However, structured electronic health records capture few\ndata elements critical to these tasks, while clinical reports lack temporal\nlocalization of events in structured form. We present a system that transforms\ncase reports into textual time series-structured pairs of textual events and\ntimestamps. We contrast manual and large language model (LLM) annotations\n(n=320 and n=390 respectively) of ten randomly-sampled PubMed open-access\n(PMOA) case reports (N=152,974) and assess inter-LLM agreement (n=3,103; N=93).\nWe find that the LLM models have moderate event recall(O1-preview: 0.80) but\nhigh temporal concordance among identified events (O1-preview: 0.95). By\nestablishing the task, annotation, and assessment systems, and by demonstrating\nhigh concordance, this work may serve as a benchmark for leveraging the PMOA\ncorpus for temporal analytics.\n", "date": "2025-04-15", "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": "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": "2504.03786", "title": "Do \"New Snow Tablets\" Contain Snow? Large Language Models Over-Rely on\n  Names to Identify Ingredients of Chinese Drugs", "abstract": "  Traditional Chinese Medicine (TCM) has seen increasing adoption in\nhealthcare, with specialized Large Language Models (LLMs) emerging to support\nclinical applications. A fundamental requirement for these models is accurate\nidentification of TCM drug ingredients. In this paper, we evaluate how general\nand TCM-specialized LLMs perform when identifying ingredients of Chinese drugs.\nOur systematic analysis reveals consistent failure patterns: models often\ninterpret drug names literally, overuse common herbs regardless of relevance,\nand exhibit erratic behaviors when faced with unfamiliar formulations. LLMs\nalso fail to understand the verification task. These findings demonstrate that\ncurrent LLMs rely primarily on drug names rather than possessing systematic\npharmacological knowledge. To address these limitations, we propose a Retrieval\nAugmented Generation (RAG) approach focused on ingredient names. Experiments\nacross 220 TCM formulations show our method significantly improves accuracy\nfrom approximately 50% to 82% in ingredient verification tasks. Our work\nhighlights critical weaknesses in current TCM-specific LLMs and offers a\npractical solution for enhancing their clinical reliability.\n", "date": "2025-04-03", "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": "2409.16395", "title": "HELIOT: LLM-Based CDSS for Adverse Drug Reaction Management", "abstract": "  Medication errors significantly threaten patient safety, leading to adverse\ndrug events and substantial economic burdens on healthcare systems. Clinical\nDecision Support Systems (CDSSs) aimed at mitigating these errors often face\nlimitations when processing unstructured clinical data, including reliance on\nstatic databases and rule-based algorithms, frequently generating excessive\nalerts that lead to alert fatigue among healthcare providers. This paper\nintroduces HELIOT, an innovative CDSS for adverse drug reaction management that\nprocesses free-text clinical information using Large Language Models (LLMs)\nintegrated with a comprehensive pharmaceutical data repository. HELIOT\nleverages advanced natural language processing capabilities to interpret\nmedical narratives, extract relevant drug reaction information from\nunstructured clinical notes, and learn from past patient-specific medication\ntolerances to reduce false alerts, enabling more nuanced and contextual adverse\ndrug event warnings across primary care, specialist consultations, and hospital\nsettings. An initial evaluation using a synthetic dataset of clinical\nnarratives and expert-verified ground truth shows promising results. HELIOT\nachieves high accuracy in a controlled setting. In addition, by intelligently\nanalyzing previous medication tolerance documented in clinical notes and\ndistinguishing between cases requiring different alert types, HELIOT can\npotentially reduce interruptive alerts by over 50% compared to traditional\nCDSSs. While these preliminary findings are encouraging, real-world validation\nwill be essential to confirm these benefits in clinical practice.\n", "date": "2024-09-24", "categories": []}
{"id": "2504.10418", "title": "CliniChat: A Multi-Source Knowledge-Driven Framework for Clinical\n  Interview Dialogue Reconstruction and Evaluation", "abstract": "  Large language models (LLMs) hold great promise for assisting clinical\ninterviews due to their fluent interactive capabilities and extensive medical\nknowledge. However, the lack of high-quality interview dialogue data and widely\naccepted evaluation methods has significantly impeded this process. So we\npropose CliniChat, a framework that integrates multi-source knowledge to enable\nLLMs to simulate real-world clinical interviews. It consists of two modules:\nClini-Recon and Clini-Eval, each responsible for reconstructing and evaluating\ninterview dialogues, respectively. By incorporating three sources of knowledge,\nClini-Recon transforms clinical notes into systematic, professional, and\nempathetic interview dialogues. Clini-Eval combines a comprehensive evaluation\nmetric system with a two-phase automatic evaluation approach, enabling LLMs to\nassess interview performance like experts. We contribute MedQA-Dialog, a\nhigh-quality synthetic interview dialogue dataset, and CliniChatGLM, a model\nspecialized for clinical interviews. Experimental results demonstrate that\nCliniChatGLM's interview capabilities undergo a comprehensive upgrade,\nparticularly in history-taking, achieving state-of-the-art performance.\n", "date": "2025-04-14", "categories": []}
{"id": "2307.11170", "title": "UMLS-KGI-BERT: Data-Centric Knowledge Integration in Transformers for\n  Biomedical Entity Recognition", "abstract": "  Pre-trained transformer language models (LMs) have in recent years become the\ndominant paradigm in applied NLP. These models have achieved state-of-the-art\nperformance on tasks such as information extraction, question answering,\nsentiment analysis, document classification and many others. In the biomedical\ndomain, significant progress has been made in adapting this paradigm to NLP\ntasks that require the integration of domain-specific knowledge as well as\nstatistical modelling of language. In particular, research in this area has\nfocused on the question of how best to construct LMs that take into account not\nonly the patterns of token distribution in medical text, but also the wealth of\nstructured information contained in terminology resources such as the UMLS.\nThis work contributes a data-centric paradigm for enriching the language\nrepresentations of biomedical transformer-encoder LMs by extracting text\nsequences from the UMLS. This allows for graph-based learning objectives to be\ncombined with masked-language pre-training. Preliminary results from\nexperiments in the extension of pre-trained LMs as well as training from\nscratch show that this framework improves downstream performance on multiple\nbiomedical and clinical Named Entity Recognition (NER) tasks.\n", "date": "2023-07-20", "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": "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": "2504.03932", "title": "YaleNLP @ PerAnsSumm 2025: Multi-Perspective Integration via\n  Mixture-of-Agents for Enhanced Healthcare QA Summarization", "abstract": "  Automated summarization of healthcare community question-answering forums is\nchallenging due to diverse perspectives presented across multiple user\nresponses to each question. The PerAnsSumm Shared Task was therefore proposed\nto tackle this challenge by identifying perspectives from different answers and\nthen generating a comprehensive answer to the question. In this study, we\naddress the PerAnsSumm Shared Task using two complementary paradigms: (i) a\ntraining-based approach through QLoRA fine-tuning of LLaMA-3.3-70B-Instruct,\nand (ii) agentic approaches including zero- and few-shot prompting with\nfrontier LLMs (LLaMA-3.3-70B-Instruct and GPT-4o) and a Mixture-of-Agents (MoA)\nframework that leverages a diverse set of LLMs by combining outputs from\nmulti-layer feedback aggregation. For perspective span\nidentification/classification, GPT-4o zero-shot achieves an overall score of\n0.57, substantially outperforming the 0.40 score of the LLaMA baseline. With a\n2-layer MoA configuration, we were able to improve LLaMA performance up by 28\npercent to 0.51. For perspective-based summarization, GPT-4o zero-shot attains\nan overall score of 0.42 compared to 0.28 for the best LLaMA zero-shot, and our\n2-layer MoA approach boosts LLaMA performance by 32 percent to 0.37.\nFurthermore, in few-shot setting, our results show that the\nsentence-transformer embedding-based exemplar selection provides more gain than\nmanually selected exemplars on LLaMA models, although the few-shot prompting is\nnot always helpful for GPT-4o. The YaleNLP team's approach ranked the overall\nsecond place in the shared task.\n", "date": "2025-04-04", "categories": []}
{"id": "2504.03964", "title": "Clinical ModernBERT: An efficient and long context encoder for\n  biomedical text", "abstract": "  We introduce Clinical ModernBERT, a transformer based encoder pretrained on\nlarge scale biomedical literature, clinical notes, and medical ontologies,\nincorporating PubMed abstracts, MIMIC IV clinical data, and medical codes with\ntheir textual descriptions. Building on ModernBERT the current state of the art\nnatural language text encoder featuring architectural upgrades such as rotary\npositional embeddings (RoPE), Flash Attention, and extended context length up\nto 8,192 tokens our model adapts these innovations specifically for biomedical\nand clinical domains. Clinical ModernBERT excels at producing semantically rich\nrepresentations tailored for long context tasks. We validate this both by\nanalyzing its pretrained weights and through empirical evaluation on a\ncomprehensive suite of clinical NLP benchmarks.\n", "date": "2025-04-04", "categories": []}
{"id": "2504.04336", "title": "Generative Large Language Models Trained for Detecting Errors in\n  Radiology Reports", "abstract": "  In this retrospective study, a dataset was constructed with two parts. The\nfirst part included 1,656 synthetic chest radiology reports generated by GPT-4\nusing specified prompts, with 828 being error-free synthetic reports and 828\ncontaining errors. The second part included 614 reports: 307 error-free reports\nbetween 2011 and 2016 from the MIMIC-CXR database and 307 corresponding\nsynthetic reports with errors generated by GPT-4 on the basis of these\nMIMIC-CXR reports and specified prompts. All errors were categorized into four\ntypes: negation, left/right, interval change, and transcription errors. Then,\nseveral models, including Llama-3, GPT-4, and BiomedBERT, were refined using\nzero-shot prompting, few-shot prompting, or fine-tuning strategies. Finally,\nthe performance of these models was evaluated using the F1 score, 95\\%\nconfidence interval (CI) and paired-sample t-tests on our constructed dataset,\nwith the prediction results further assessed by radiologists. Using zero-shot\nprompting, the fine-tuned Llama-3-70B-Instruct model achieved the best\nperformance with the following F1 scores: 0.769 for negation errors, 0.772 for\nleft/right errors, 0.750 for interval change errors, 0.828 for transcription\nerrors, and 0.780 overall. In the real-world evaluation phase, two radiologists\nreviewed 200 randomly selected reports output by the model. Of these, 99 were\nconfirmed to contain errors detected by the models by both radiologists, and\n163 were confirmed to contain model-detected errors by at least one\nradiologist. Generative LLMs, fine-tuned on synthetic and MIMIC-CXR radiology\nreports, greatly enhanced error detection in radiology reports.\n", "date": "2025-04-06", "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": "2406.15888", "title": "Real-time Speech Summarization for Medical Conversations", "abstract": "  In doctor-patient conversations, identifying medically relevant information\nis crucial, posing the need for conversation summarization. In this work, we\npropose the first deployable real-time speech summarization system for\nreal-world applications in industry, which generates a local summary after\nevery N speech utterances within a conversation and a global summary after the\nend of a conversation. Our system could enhance user experience from a business\nstandpoint, while also reducing computational costs from a technical\nperspective. Secondly, we present VietMed-Sum which, to our knowledge, is the\nfirst speech summarization dataset for medical conversations. Thirdly, we are\nthe first to utilize LLM and human annotators collaboratively to create gold\nstandard and synthetic summaries for medical conversation summarization.\nFinally, we present baseline results of state-of-the-art models on VietMed-Sum.\nAll code, data (English-translated and Vietnamese) and models are available\nonline: https://github.com/leduckhai/MultiMed/tree/master/VietMed-Sum\n", "date": "2024-06-22", "categories": []}
{"id": "2504.02871", "title": "Synthesized Annotation Guidelines are Knowledge-Lite Boosters for\n  Clinical Information Extraction", "abstract": "  Generative information extraction using large language models, particularly\nthrough few-shot learning, has become a popular method. Recent studies indicate\nthat providing a detailed, human-readable guideline-similar to the annotation\nguidelines traditionally used for training human annotators can significantly\nimprove performance. However, constructing these guidelines is both labor- and\nknowledge-intensive. Additionally, the definitions are often tailored to meet\nspecific needs, making them highly task-specific and often non-reusable.\nHandling these subtle differences requires considerable effort and attention to\ndetail. In this study, we propose a self-improving method that harvests the\nknowledge summarization and text generation capacity of LLMs to synthesize\nannotation guidelines while requiring virtually no human input. Our zero-shot\nexperiments on the clinical named entity recognition benchmarks, 2012 i2b2\nEVENT, 2012 i2b2 TIMEX, 2014 i2b2, and 2018 n2c2 showed 25.86%, 4.36%, 0.20%,\nand 7.75% improvements in strict F1 scores from the no-guideline baseline. The\nLLM-synthesized guidelines showed equivalent or better performance compared to\nhuman-written guidelines by 1.15% to 4.14% in most tasks. In conclusion, this\nstudy proposes a novel LLM self-improving method that requires minimal\nknowledge and human input and is applicable to multiple biomedical domains.\n", "date": "2025-04-01", "categories": []}
{"id": "2504.02917", "title": "Bias in Large Language Models Across Clinical Applications: A Systematic\n  Review", "abstract": "  Background: Large language models (LLMs) are rapidly being integrated into\nhealthcare, promising to enhance various clinical tasks. However, concerns\nexist regarding their potential for bias, which could compromise patient care\nand exacerbate health inequities. This systematic review investigates the\nprevalence, sources, manifestations, and clinical implications of bias in LLMs.\nMethods: We conducted a systematic search of PubMed, OVID, and EMBASE from\ndatabase inception through 2025, for studies evaluating bias in LLMs applied to\nclinical tasks. We extracted data on LLM type, bias source, bias manifestation,\naffected attributes, clinical task, evaluation methods, and outcomes. Risk of\nbias was assessed using a modified ROBINS-I tool. Results: Thirty-eight studies\nmet inclusion criteria, revealing pervasive bias across various LLMs and\nclinical applications. Both data-related bias (from biased training data) and\nmodel-related bias (from model training) were significant contributors. Biases\nmanifested as: allocative harm (e.g., differential treatment recommendations);\nrepresentational harm (e.g., stereotypical associations, biased image\ngeneration); and performance disparities (e.g., variable output quality). These\nbiases affected multiple attributes, most frequently race/ethnicity and gender,\nbut also age, disability, and language. Conclusions: Bias in clinical LLMs is a\npervasive and systemic issue, with a potential to lead to misdiagnosis and\ninappropriate treatment, particularly for marginalized patient populations.\nRigorous evaluation of the model is crucial. Furthermore, the development and\nimplementation of effective mitigation strategies, coupled with continuous\nmonitoring in real-world clinical settings, are essential to ensure the safe,\nequitable, and trustworthy deployment of LLMs in healthcare.\n", "date": "2025-04-03", "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": "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": "2406.10459", "title": "CancerLLM: A Large Language Model in Cancer Domain", "abstract": "  Medical Large Language Models (LLMs) have demonstrated impressive performance\non a wide variety of medical NLP tasks; however, there still lacks a LLM\nspecifically designed for phenotyping identification and diagnosis in cancer\ndomain. Moreover, these LLMs typically have several billions of parameters,\nmaking them computationally expensive for healthcare systems. Thus, in this\nstudy, we propose CancerLLM, a model with 7 billion parameters and a\nMistral-style architecture, pre-trained on nearly 2.7M clinical notes and over\n515K pathology reports covering 17 cancer types, followed by fine-tuning on two\ncancer-relevant tasks, including cancer phenotypes extraction and cancer\ndiagnosis generation. Our evaluation demonstrated that the CancerLLM achieves\nstate-of-the-art results with F1 score of 91.78% on phenotyping extraction and\n86.81% on disganois generation. It outperformed existing LLMs, with an average\nF1 score improvement of 9.23%. Additionally, the CancerLLM demonstrated its\nefficiency on time and GPU usage, and robustness comparing with other LLMs. We\ndemonstrated that CancerLLM can potentially provide an effective and robust\nsolution to advance clinical research and practice in cancer domain\n", "date": "2024-06-15", "categories": []}
{"id": "2504.00664", "title": "Do LLMs Surpass Encoders for Biomedical NER?", "abstract": "  Recognizing spans of biomedical concepts and their types (e.g., drug or gene)\nin free text, often called biomedical named entity recognition (NER), is a\nbasic component of information extraction (IE) pipelines. Without a strong NER\ncomponent, other applications, such as knowledge discovery and information\nretrieval, are not practical. State-of-the-art in NER shifted from traditional\nML models to deep neural networks with transformer-based encoder models (e.g.,\nBERT) emerging as the current standard. However, decoder models (also called\nlarge language models or LLMs) are gaining traction in IE. But LLM-driven NER\noften ignores positional information due to the generative nature of decoder\nmodels. Furthermore, they are computationally very expensive (both in inference\ntime and hardware needs). Hence, it is worth exploring if they actually excel\nat biomedical NER and assess any associated trade-offs (performance vs\nefficiency). This is exactly what we do in this effort employing the same BIO\nentity tagging scheme (that retains positional information) using five\ndifferent datasets with varying proportions of longer entities. Our results\nshow that the LLMs chosen (Mistral and Llama: 8B range) often outperform best\nencoder models (BERT-(un)cased, BiomedBERT, and DeBERTav3: 300M range) by 2-8%\nin F-scores except for one dataset, where they equal encoder performance. This\ngain is more prominent among longer entities of length >= 3 tokens. However,\nLLMs are one to two orders of magnitude more expensive at inference time and\nmay need cost prohibitive hardware. Thus, when performance differences are\nsmall or real time user feedback is needed, encoder models might still be more\nsuitable than LLMs.\n", "date": "2025-04-01", "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": "2503.23281", "title": "Extracting Patient History from Clinical Text: A Comparative Study of\n  Clinical Large Language Models", "abstract": "  Extracting medical history entities (MHEs) related to a patient's chief\ncomplaint (CC), history of present illness (HPI), and past, family, and social\nhistory (PFSH) helps structure free-text clinical notes into standardized EHRs,\nstreamlining downstream tasks like continuity of care, medical coding, and\nquality metrics. Fine-tuned clinical large language models (cLLMs) can assist\nin this process while ensuring the protection of sensitive data via on-premises\ndeployment. This study evaluates the performance of cLLMs in recognizing\nCC/HPI/PFSH-related MHEs and examines how note characteristics impact model\naccuracy. We annotated 1,449 MHEs across 61 outpatient-related clinical notes\nfrom the MTSamples repository. To recognize these entities, we fine-tuned seven\nstate-of-the-art cLLMs. Additionally, we assessed the models' performance when\nenhanced by integrating, problems, tests, treatments, and other basic medical\nentities (BMEs). We compared the performance of these models against GPT-4o in\na zero-shot setting. To further understand the textual characteristics\naffecting model accuracy, we conducted an error analysis focused on note\nlength, entity length, and segmentation. The cLLMs showed potential in reducing\nthe time required for extracting MHEs by over 20%. However, detecting many\ntypes of MHEs remained challenging due to their polysemous nature and the\nfrequent involvement of non-medical vocabulary. Fine-tuned GatorTron and\nGatorTronS, two of the most extensively trained cLLMs, demonstrated the highest\nperformance. Integrating pre-identified BME information improved model\nperformance for certain entities. Regarding the impact of textual\ncharacteristics on model performance, we found that longer entities were harder\nto identify, note length did not correlate with a higher error rate, and\nwell-organized segments with headings are beneficial for the extraction.\n", "date": "2025-03-30", "categories": []}
{"id": "2503.23673", "title": "WHERE and WHICH: Iterative Debate for Biomedical Synthetic Data\n  Augmentation", "abstract": "  In Biomedical Natural Language Processing (BioNLP) tasks, such as Relation\nExtraction, Named Entity Recognition, and Text Classification, the scarcity of\nhigh-quality data remains a significant challenge. This limitation poisons\nlarge language models to correctly understand relationships between biological\nentities, such as molecules and diseases, or drug interactions, and further\nresults in potential misinterpretation of biomedical documents. To address this\nissue, current approaches generally adopt the Synthetic Data Augmentation\nmethod which involves similarity computation followed by word replacement, but\ncounterfactual data are usually generated. As a result, these methods disrupt\nmeaningful word sets or produce sentences with meanings that deviate\nsubstantially from the original context, rendering them ineffective in\nimproving model performance. To this end, this paper proposes a\nbiomedical-dedicated rationale-based synthetic data augmentation method. Beyond\nthe naive lexicon similarity, specific bio-relation similarity is measured to\nhold the augmented instance having a strong correlation with bio-relation\ninstead of simply increasing the diversity of augmented data. Moreover, a\nmulti-agents-involved reflection mechanism helps the model iteratively\ndistinguish different usage of similar entities to escape falling into the\nmis-replace trap. We evaluate our method on the BLURB and BigBIO benchmark,\nwhich includes 9 common datasets spanning four major BioNLP tasks. Our\nexperimental results demonstrate consistent performance improvements across all\ntasks, highlighting the effectiveness of our approach in addressing the\nchallenges associated with data scarcity and enhancing the overall performance\nof biomedical NLP models.\n", "date": "2025-03-31", "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": "2503.24116", "title": "Multi-Task Learning for Extracting Menstrual Characteristics from\n  Clinical Notes", "abstract": "  Menstrual health is a critical yet often overlooked aspect of women's\nhealthcare. Despite its clinical relevance, detailed data on menstrual\ncharacteristics is rarely available in structured medical records. To address\nthis gap, we propose a novel Natural Language Processing pipeline to extract\nkey menstrual cycle attributes -- dysmenorrhea, regularity, flow volume, and\nintermenstrual bleeding. Our approach utilizes the GatorTron model with\nMulti-Task Prompt-based Learning, enhanced by a hybrid retrieval preprocessing\nstep to identify relevant text segments. It out- performs baseline methods,\nachieving an average F1-score of 90% across all menstrual characteristics,\ndespite being trained on fewer than 100 annotated clinical notes. The retrieval\nstep consistently improves performance across all approaches, allowing the\nmodel to focus on the most relevant segments of lengthy clinical notes. These\nresults show that combining multi-task learning with retrieval improves\ngeneralization and performance across menstrual charac- teristics, advancing\nautomated extraction from clinical notes and supporting women's health\nresearch.\n", "date": "2025-03-31", "categories": []}
{"id": "2503.21911", "title": "AutoPsyC: Automatic Recognition of Psychodynamic Conflicts from\n  Semi-structured Interviews with Large Language Models", "abstract": "  Psychodynamic conflicts are persistent, often unconscious themes that shape a\nperson's behaviour and experiences. Accurate diagnosis of psychodynamic\nconflicts is crucial for effective patient treatment and is commonly done via\nlong, manually scored semi-structured interviews. Existing automated solutions\nfor psychiatric diagnosis tend to focus on the recognition of broad disorder\ncategories such as depression, and it is unclear to what extent psychodynamic\nconflicts which even the patient themselves may not have conscious access to\ncould be automatically recognised from conversation. In this paper, we propose\nAutoPsyC, the first method for recognising the presence and significance of\npsychodynamic conflicts from full-length Operationalized Psychodynamic\nDiagnostics (OPD) interviews using Large Language Models (LLMs). Our approach\ncombines recent advances in parameter-efficient fine-tuning and\nRetrieval-Augmented Generation (RAG) with a summarisation strategy to\neffectively process entire 90 minute long conversations. In evaluations on a\ndataset of 141 diagnostic interviews we show that AutoPsyC consistently\noutperforms all baselines and ablation conditions on the recognition of four\nhighly relevant psychodynamic conflicts.\n", "date": "2025-03-27", "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": "2503.20639", "title": "PVLens: Enhancing Pharmacovigilance Through Automated Label Extraction", "abstract": "  Reliable drug safety reference databases are essential for pharmacovigilance,\nyet existing resources like SIDER are outdated and static. We introduce PVLens,\nan automated system that extracts labeled safety information from FDA\nStructured Product Labels (SPLs) and maps terms to MedDRA. PVLens integrates\nautomation with expert oversight through a web-based review tool. In validation\nagainst 97 drug labels, PVLens achieved an F1 score of 0.882, with high recall\n(0.983) and moderate precision (0.799). By offering a scalable, more accurate\nand continuously updated alternative to SIDER, PVLens enhances real-time\npharamcovigilance with improved accuracy and contemporaneous insights.\n", "date": "2025-03-26", "categories": []}
{"id": "2503.21349", "title": "Fine-Tuning LLMs on Small Medical Datasets: Text Classification and\n  Normalization Effectiveness on Cardiology reports and Discharge records", "abstract": "  We investigate the effectiveness of fine-tuning large language models (LLMs)\non small medical datasets for text classification and named entity recognition\ntasks. Using a German cardiology report dataset and the i2b2 Smoking Challenge\ndataset, we demonstrate that fine-tuning small LLMs locally on limited training\ndata can improve performance achieving comparable results to larger models. Our\nexperiments show that fine-tuning improves performance on both tasks, with\nnotable gains observed with as few as 200-300 training examples. Overall, the\nstudy highlights the potential of task-specific fine-tuning of LLMs for\nautomating clinical workflows and efficiently extracting structured data from\nunstructured medical text.\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": "2503.20648", "title": "TN-Eval: Rubric and Evaluation Protocols for Measuring the Quality of\n  Behavioral Therapy Notes", "abstract": "  Behavioral therapy notes are important for both legal compliance and patient\ncare. Unlike progress notes in physical health, quality standards for\nbehavioral therapy notes remain underdeveloped. To address this gap, we\ncollaborated with licensed therapists to design a comprehensive rubric for\nevaluating therapy notes across key dimensions: completeness, conciseness, and\nfaithfulness. Further, we extend a public dataset of behavioral health\nconversations with therapist-written notes and LLM-generated notes, and apply\nour evaluation framework to measure their quality. We find that: (1) A\nrubric-based manual evaluation protocol offers more reliable and interpretable\nresults than traditional Likert-scale annotations. (2) LLMs can mimic human\nevaluators in assessing completeness and conciseness but struggle with\nfaithfulness. (3) Therapist-written notes often lack completeness and\nconciseness, while LLM-generated notes contain hallucination. Surprisingly, in\na blind test, therapists prefer and judge LLM-generated notes to be superior to\ntherapist-written notes.\n", "date": "2025-03-26", "categories": []}
{"id": "2503.20666", "title": "TAMA: A Human-AI Collaborative Thematic Analysis Framework Using\n  Multi-Agent LLMs for Clinical Interviews", "abstract": "  Thematic analysis (TA) is a widely used qualitative approach for uncovering\nlatent meanings in unstructured text data. TA provides valuable insights in\nhealthcare but is resource-intensive. Large Language Models (LLMs) have been\nintroduced to perform TA, yet their applications in healthcare remain\nunexplored. Here, we propose TAMA: A Human-AI Collaborative Thematic Analysis\nframework using Multi-Agent LLMs for clinical interviews. We leverage the\nscalability and coherence of multi-agent systems through structured\nconversations between agents and coordinate the expertise of cardiac experts in\nTA. Using interview transcripts from parents of children with Anomalous Aortic\nOrigin of a Coronary Artery (AAOCA), a rare congenital heart disease, we\ndemonstrate that TAMA outperforms existing LLM-assisted TA approaches,\nachieving higher thematic hit rate, coverage, and distinctiveness. TAMA\ndemonstrates strong potential for automated TA in clinical settings by\nleveraging multi-agent LLM systems with human-in-the-loop integration by\nenhancing quality while significantly reducing manual workload.\n", "date": "2025-03-26", "categories": []}
{"id": "2503.19394", "title": "Quantifying Symptom Causality in Clinical Decision Making: An\n  Exploration Using CausaLM", "abstract": "  Current machine learning approaches to medical diagnosis often rely on\ncorrelational patterns between symptoms and diseases, risking misdiagnoses when\nsymptoms are ambiguous or common across multiple conditions. In this work, we\nmove beyond correlation to investigate the causal influence of key\nsymptoms-specifically \"chest pain\" on diagnostic predictions. Leveraging the\nCausaLM framework, we generate counterfactual text representations in which\ntarget concepts are effectively \"forgotten\" enabling a principled estimation of\nthe causal effect of that concept on a model's predicted disease distribution.\nBy employing Textual Representation-based Average Treatment Effect (TReATE), we\nquantify how the presence or absence of a symptom shapes the model's diagnostic\noutcomes, and contrast these findings against correlation-based baselines such\nas CONEXP. Our results offer deeper insight into the decision-making behavior\nof clinical NLP models and have the potential to inform more trustworthy,\ninterpretable, and causally-grounded decision support tools in medical\npractice.\n", "date": "2025-03-25", "categories": []}
{"id": "2411.00173", "title": "Beyond Label Attention: Transparency in Language Models for Automated\n  Medical Coding via Dictionary Learning", "abstract": "  Medical coding, the translation of unstructured clinical text into\nstandardized medical codes, is a crucial but time-consuming healthcare\npractice. Though large language models (LLM) could automate the coding process\nand improve the efficiency of such tasks, interpretability remains paramount\nfor maintaining patient trust. Current efforts in interpretability of medical\ncoding applications rely heavily on label attention mechanisms, which often\nleads to the highlighting of extraneous tokens irrelevant to the ICD code. To\nfacilitate accurate interpretability in medical language models, this paper\nleverages dictionary learning that can efficiently extract sparsely activated\nrepresentations from dense language model embeddings in superposition. Compared\nwith common label attention mechanisms, our model goes beyond token-level\nrepresentations by building an interpretable dictionary which enhances the\nmechanistic-based explanations for each ICD code prediction, even when the\nhighlighted tokens are medically irrelevant. We show that dictionary features\ncan steer model behavior, elucidate the hidden meanings of upwards of 90% of\nmedically irrelevant tokens, and are human interpretable.\n", "date": "2024-10-31", "categories": []}
{"id": "2503.17425", "title": "Beyond Negation Detection: Comprehensive Assertion Detection Models for\n  Clinical NLP", "abstract": "  Assertion status detection is a critical yet often overlooked component of\nclinical NLP, essential for accurately attributing extracted medical facts.\nPast studies have narrowly focused on negation detection, leading to\nunderperforming commercial solutions such as AWS Medical Comprehend, Azure AI\nText Analytics, and GPT-4o due to their limited domain adaptation. To address\nthis gap, we developed state-of-the-art assertion detection models, including\nfine-tuned LLMs, transformer-based classifiers, few-shot classifiers, and deep\nlearning (DL) approaches. We evaluated these models against cloud-based\ncommercial API solutions, the legacy rule-based NegEx approach, and GPT-4o. Our\nfine-tuned LLM achieves the highest overall accuracy (0.962), outperforming\nGPT-4o (0.901) and commercial APIs by a notable margin, particularly excelling\nin Present (+4.2%), Absent (+8.4%), and Hypothetical (+23.4%) assertions. Our\nDL-based models surpass commercial solutions in Conditional (+5.3%) and\nAssociated-with-Someone-Else (+10.1%) categories, while the few-shot classifier\noffers a lightweight yet highly competitive alternative (0.929), making it\nideal for resource-constrained environments. Integrated within Spark NLP, our\nmodels consistently outperform black-box commercial solutions while enabling\nscalable inference and seamless integration with medical NER, Relation\nExtraction, and Terminology Resolution. These results reinforce the importance\nof domain-adapted, transparent, and customizable clinical NLP solutions over\ngeneral-purpose LLMs and proprietary APIs.\n", "date": "2025-03-21", "categories": []}
{"id": "2503.17509", "title": "Follow-up Question Generation For Enhanced Patient-Provider\n  Conversations", "abstract": "  Follow-up question generation is an essential feature of dialogue systems as\nit can reduce conversational ambiguity and enhance modeling complex\ninteractions. Conversational contexts often pose core NLP challenges such as\n(i) extracting relevant information buried in fragmented data sources, and (ii)\nmodeling parallel thought processes. These two challenges occur frequently in\nmedical dialogue as a doctor asks questions based not only on patient\nutterances but also their prior EHR data and current diagnostic hypotheses.\nAsking medical questions in asynchronous conversations compounds these issues\nas doctors can only rely on static EHR information to motivate follow-up\nquestions.\n  To address these challenges, we introduce FollowupQ, a novel framework for\nenhancing asynchronous medical conversation. FollowupQ is a multi-agent\nframework that processes patient messages and EHR data to generate personalized\nfollow-up questions, clarifying patient-reported medical conditions. FollowupQ\nreduces requisite provider follow-up communications by 34%. It also improves\nperformance by 17% and 5% on real and synthetic data, respectively. We also\nrelease the first public dataset of asynchronous medical messages with linked\nEHR data alongside 2,300 follow-up questions written by clinical experts for\nthe wider NLP research community.\n", "date": "2025-03-21", "categories": []}
{"id": "2503.18539", "title": "Natural Language Processing for Electronic Health Records in\n  Scandinavian Languages: Norwegian, Swedish, and Danish", "abstract": "  Background: Clinical natural language processing (NLP) refers to the use of\ncomputational methods for extracting, processing, and analyzing unstructured\nclinical text data, and holds a huge potential to transform healthcare in\nvarious clinical tasks. Objective: The study aims to perform a systematic\nreview to comprehensively assess and analyze the state-of-the-art NLP methods\nfor the mainland Scandinavian clinical text. Method: A literature search was\nconducted in various online databases including PubMed, ScienceDirect, Google\nScholar, ACM digital library, and IEEE Xplore between December 2022 and\nFebruary 2024. Further, relevant references to the included articles were also\nused to solidify our search. The final pool includes articles that conducted\nclinical NLP in the mainland Scandinavian languages and were published in\nEnglish between 2010 and 2024. Results: Out of the 113 articles, 18% (n=21)\nfocus on Norwegian clinical text, 64% (n=72) on Swedish, 10% (n=11) on Danish,\nand 8% (n=9) focus on more than one language. Generally, the review identified\npositive developments across the region despite some observable gaps and\ndisparities between the languages. There are substantial disparities in the\nlevel of adoption of transformer-based models. In essential tasks such as\nde-identification, there is significantly less research activity focusing on\nNorwegian and Danish compared to Swedish text. Further, the review identified a\nlow level of sharing resources such as data, experimentation code, pre-trained\nmodels, and rate of adaptation and transfer learning in the region. Conclusion:\nThe review presented a comprehensive assessment of the state-of-the-art\nClinical NLP for electronic health records (EHR) text in mainland Scandinavian\nlanguages and, highlighted the potential barriers and challenges that hinder\nthe rapid advancement of the field in the region.\n", "date": "2025-03-24", "categories": []}
{"id": "2503.18594", "title": "ClinText-SP and RigoBERTa Clinical: a new set of open resources for\n  Spanish Clinical NLP", "abstract": "  We present a novel contribution to Spanish clinical natural language\nprocessing by introducing the largest publicly available clinical corpus,\nClinText-SP, along with a state-of-the-art clinical encoder language model,\nRigoBERTa Clinical. Our corpus was meticulously curated from diverse open\nsources, including clinical cases from medical journals and annotated corpora\nfrom shared tasks, providing a rich and diverse dataset that was previously\ndifficult to access. RigoBERTa Clinical, developed through domain-adaptive\npretraining on this comprehensive dataset, significantly outperforms existing\nmodels on multiple clinical NLP benchmarks. By publicly releasing both the\ndataset and the model, we aim to empower the research community with robust\nresources that can drive further advancements in clinical NLP and ultimately\ncontribute to improved healthcare applications.\n", "date": "2025-03-24", "categories": []}
{"id": "2503.16513", "title": "Medifact at PerAnsSumm 2025: Leveraging Lightweight Models for\n  Perspective-Specific Summarization of Clinical Q&A Forums", "abstract": "  The PerAnsSumm 2025 challenge focuses on perspective-aware healthcare answer\nsummarization (Agarwal et al., 2025). This work proposes a few-shot learning\nframework using a Snorkel-BART-SVM pipeline for classifying and summarizing\nopen-ended healthcare community question-answering (CQA). An SVM model is\ntrained with weak supervision via Snorkel, enhancing zero-shot learning.\nExtractive classification identifies perspective-relevant sentences, which are\nthen summarized using a pretrained BART-CNN model. The approach achieved 12th\nplace among 100 teams in the shared task, demonstrating computational\nefficiency and contextual accuracy. By leveraging pretrained summarization\nmodels, this work advances medical CQA research and contributes to clinical\ndecision support systems.\n", "date": "2025-03-15", "categories": []}
{"id": "2503.16533", "title": "From Patient Consultations to Graphs: Leveraging LLMs for Patient\n  Journey Knowledge Graph Construction", "abstract": "  The transition towards patient-centric healthcare necessitates a\ncomprehensive understanding of patient journeys, which encompass all healthcare\nexperiences and interactions across the care spectrum. Existing healthcare data\nsystems are often fragmented and lack a holistic representation of patient\ntrajectories, creating challenges for coordinated care and personalized\ninterventions. Patient Journey Knowledge Graphs (PJKGs) represent a novel\napproach to addressing the challenge of fragmented healthcare data by\nintegrating diverse patient information into a unified, structured\nrepresentation. This paper presents a methodology for constructing PJKGs using\nLarge Language Models (LLMs) to process and structure both formal clinical\ndocumentation and unstructured patient-provider conversations. These graphs\nencapsulate temporal and causal relationships among clinical encounters,\ndiagnoses, treatments, and outcomes, enabling advanced temporal reasoning and\npersonalized care insights. The research evaluates four different LLMs, such as\nClaude 3.5, Mistral, Llama 3.1, and Chatgpt4o, in their ability to generate\naccurate and computationally efficient knowledge graphs. Results demonstrate\nthat while all models achieved perfect structural compliance, they exhibited\nvariations in medical entity processing and computational efficiency. The paper\nconcludes by identifying key challenges and future research directions. This\nwork contributes to advancing patient-centric healthcare through the\ndevelopment of comprehensive, actionable knowledge graphs that support improved\ncare coordination and outcome prediction.\n", "date": "2025-03-18", "categories": []}
{"id": "2503.15718", "title": "Am I eligible? Natural Language Inference for Clinical Trial Patient\n  Recruitment: the Patient's Point of View", "abstract": "  Recruiting patients to participate in clinical trials can be challenging and\ntime-consuming. Usually, participation in a clinical trial is initiated by a\nhealthcare professional and proposed to the patient. Promoting clinical trials\ndirectly to patients via online recruitment might help to reach them more\nefficiently. In this study, we address the case where a patient is initiating\ntheir own recruitment process and wants to determine whether they are eligible\nfor a given clinical trial, using their own language to describe their medical\nprofile. To study whether this creates difficulties in the patient trial\nmatching process, we design a new dataset and task, Natural Language Inference\nfor Patient Recruitment (NLI4PR), in which patient language profiles must be\nmatched to clinical trials. We create it by adapting the TREC 2022 Clinical\nTrial Track dataset, which provides patients' medical profiles, and rephrasing\nthem manually using patient language. We also use the associated clinical trial\nreports where the patients are either eligible or excluded. We prompt several\nopen-source Large Language Models on our task and achieve from 56.5 to 71.8 of\nF1 score using patient language, against 64.7 to 73.1 for the same task using\nmedical language. When using patient language, we observe only a small loss in\nperformance for the best model, suggesting that having the patient as a\nstarting point could be adopted to help recruit patients for clinical trials.\nThe corpus and code bases are all freely available on our Github and\nHuggingFace repositories.\n", "date": "2025-03-19", "categories": []}
{"id": "2503.14671", "title": "Generating Medically-Informed Explanations for Depression Detection\n  using LLMs", "abstract": "  Early detection of depression from social media data offers a valuable\nopportunity for timely intervention. However, this task poses significant\nchallenges, requiring both professional medical knowledge and the development\nof accurate and explainable models. In this paper, we propose LLM-MTD (Large\nLanguage Model for Multi-Task Depression Detection), a novel approach that\nleverages a pre-trained large language model to simultaneously classify social\nmedia posts for depression and generate textual explanations grounded in\nmedical diagnostic criteria. We train our model using a multi-task learning\nframework with a combined loss function that optimizes both classification\naccuracy and explanation quality. We evaluate LLM-MTD on the benchmark Reddit\nSelf-Reported Depression Dataset (RSDD) and compare its performance against\nseveral competitive baseline methods, including traditional machine learning\nand fine-tuned BERT. Our experimental results demonstrate that LLM-MTD achieves\nstate-of-the-art performance in depression detection, showing significant\nimprovements in AUPRC and other key metrics. Furthermore, human evaluation of\nthe generated explanations reveals their relevance, completeness, and medical\naccuracy, highlighting the enhanced interpretability of our approach. This work\ncontributes a novel methodology for depression detection that combines the\npower of large language models with the crucial aspect of explainability.\n", "date": "2025-03-18", "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": "2503.11384", "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": "2411.13163", "title": "Unlocking Historical Clinical Trial Data with ALIGN: A Compositional\n  Large Language Model System for Medical Coding", "abstract": "  The reuse of historical clinical trial data has significant potential to\naccelerate medical research and drug development. However, interoperability\nchallenges, particularly with missing medical codes, hinders effective data\nintegration across studies. While Large Language Models (LLMs) offer a\npromising solution for automated coding without labeled data, current\napproaches face challenges on complex coding tasks. We introduce ALIGN, a novel\ncompositional LLM-based system for automated, zero-shot medical coding. ALIGN\nfollows a three-step process: (1) diverse candidate code generation; (2)\nself-evaluation of codes and (3) confidence scoring and uncertainty estimation\nenabling human deferral to ensure reliability. We evaluate ALIGN on harmonizing\nmedication terms into Anatomical Therapeutic Chemical (ATC) and medical history\nterms into Medical Dictionary for Regulatory Activities (MedDRA) codes\nextracted from 22 immunology trials. ALIGN outperformed the LLM baselines,\nwhile also providing capabilities for trustworthy deployment. For MedDRA\ncoding, ALIGN achieved high accuracy across all levels, matching RAG and\nexcelling at the most specific levels (87-90% for HLGT). For ATC coding, ALIGN\ndemonstrated superior performance, particularly at lower hierarchy levels (ATC\nLevel 4), with 72-73% overall accuracy and 86-89% accuracy for common\nmedications, outperforming baselines by 7-22%. ALIGN's uncertainty-based\ndeferral improved accuracy by 17% to 90% accuracy with 30% deferral, notably\nenhancing performance on uncommon medications. ALIGN achieves this\ncost-efficiently at \\$0.0007 and \\$0.02 per code for GPT-4o-mini and GPT-4o,\nreducing barriers to clinical adoption. ALIGN advances automated medical coding\nfor clinical trial data, contributing to enhanced data interoperability and\nreusability, positioning it as a promising tool to improve clinical research\nand accelerate drug development.\n", "date": "2024-11-20", "categories": []}
{"id": "2503.09896", "title": "A Rule Based Solution to Co-reference Resolution in Clinical Text", "abstract": "  Objective: The aim of this study was to build an effective co-reference\nresolution system tailored for the biomedical domain. Materials and Methods:\nExperiment materials used in this study is provided by the 2011 i2b2 Natural\nLanguage Processing Challenge. The 2011 i2b2 challenge involves coreference\nresolution in medical documents. Concept mentions have been annotated in\nclinical texts, and the mentions that co-refer in each document are to be\nlinked by coreference chains. Normally, there are two ways of constructing a\nsystem to automatically discover co-referent links. One is to manually build\nrules for co-reference resolution, and the other category of approaches is to\nuse machine learning systems to learn automatically from training datasets and\nthen perform the resolution task on testing datasets. Results: Experiments show\nthe existing co-reference resolution systems are able to find some of the\nco-referent links, and our rule based system performs well finding the majority\nof the co-referent links. Our system achieved 89.6% overall performance on\nmultiple medical datasets. Conclusion: The experiment results show that\nmanually crafted rules based on observation of training data is a valid way to\naccomplish high performance in this coreference resolution task for the\ncritical biomedical domain.\n", "date": "2025-03-12", "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": "2503.09358", "title": "RetSTA: An LLM-Based Approach for Standardizing Clinical Fundus Image\n  Reports", "abstract": "  Standardization of clinical reports is crucial for improving the quality of\nhealthcare and facilitating data integration. The lack of unified standards,\nincluding format, terminology, and style, is a great challenge in clinical\nfundus diagnostic reports, which increases the difficulty for large language\nmodels (LLMs) to understand the data. To address this, we construct a bilingual\nstandard terminology, containing fundus clinical terms and commonly used\ndescriptions in clinical diagnosis. Then, we establish two models,\nRetSTA-7B-Zero and RetSTA-7B. RetSTA-7B-Zero, fine-tuned on an augmented\ndataset simulating clinical scenarios, demonstrates powerful standardization\nbehaviors. However, it encounters a challenge of limitation to cover a wider\nrange of diseases. To further enhance standardization performance, we build\nRetSTA-7B, which integrates a substantial amount of standardized data generated\nby RetSTA-7B-Zero along with corresponding English data, covering diverse\ncomplex clinical scenarios and achieving report-level standardization for the\nfirst time. Experimental results demonstrate that RetSTA-7B outperforms other\ncompared LLMs in bilingual standardization task, which validates its superior\nperformance and generalizability. The checkpoints are available at\nhttps://github.com/AB-Story/RetSTA-7B.\n", "date": "2025-03-12", "categories": []}
{"id": "2503.08323", "title": "Towards Scalable and Cross-Lingual Specialist Language Models for\n  Oncology", "abstract": "  Clinical oncology generates vast, unstructured data that often contain\ninconsistencies, missing information, and ambiguities, making it difficult to\nextract reliable insights for data-driven decision-making. General-purpose\nlarge language models (LLMs) struggle with these challenges due to their lack\nof domain-specific reasoning, including specialized clinical terminology,\ncontext-dependent interpretations, and multi-modal data integration. We address\nthese issues with an oncology-specialized, efficient, and adaptable NLP\nframework that combines instruction tuning, retrieval-augmented generation\n(RAG), and graph-based knowledge integration. Our lightweight models prove\neffective at oncology-specific tasks, such as named entity recognition (e.g.,\nidentifying cancer diagnoses), entity linking (e.g., linking entities to\nstandardized ontologies), TNM staging, document classification (e.g., cancer\nsubtype classification from pathology reports), and treatment response\nprediction. Our framework emphasizes adaptability and resource efficiency. We\ninclude minimal German instructions, collected at the University Hospital\nZurich (USZ), to test whether small amounts of non-English language data can\neffectively transfer knowledge across languages. This approach mirrors our\nmotivation for lightweight models, which balance strong performance with\nreduced computational costs, making them suitable for resource-limited\nhealthcare settings. We validated our models on oncology datasets,\ndemonstrating strong results in named entity recognition, relation extraction,\nand document classification.\n", "date": "2025-03-11", "categories": []}
{"id": "2410.18955", "title": "BioMistral-NLU: Towards More Generalizable Medical Language\n  Understanding through Instruction Tuning", "abstract": "  Large language models (LLMs) such as ChatGPT are fine-tuned on large and\ndiverse instruction-following corpora, and can generalize to new tasks.\nHowever, those instruction-tuned LLMs often perform poorly in specialized\nmedical natural language understanding (NLU) tasks that require domain\nknowledge, granular text comprehension, and structured data extraction. To\nbridge the gap, we: (1) propose a unified prompting format for 7 important NLU\ntasks, (2) curate an instruction-tuning dataset, MNLU-Instruct, utilizing\ndiverse existing open-source medical NLU corpora, and (3) develop\nBioMistral-NLU, a generalizable medical NLU model, through fine-tuning\nBioMistral on MNLU-Instruct. We evaluate BioMistral-NLU in a zero-shot setting,\nacross 6 important NLU tasks, from two widely adopted medical NLU benchmarks:\nBLUE and BLURB. Our experiments show that our BioMistral-NLU outperforms the\noriginal BioMistral, as well as the proprietary LLMs - ChatGPT and GPT-4. Our\ndataset-agnostic prompting strategy and instruction tuning step over diverse\nNLU tasks enhance LLMs' generalizability across diverse medical NLU tasks. Our\nablation experiments show that instruction-tuning on a wider variety of tasks,\neven when the total number of training instances remains constant, enhances\ndownstream zero-shot generalization.\n", "date": "2024-10-24", "categories": []}
{"id": "2503.04691", "title": "Quantifying the Reasoning Abilities of LLMs on Real-world Clinical Cases", "abstract": "  Recent advancements in reasoning-enhanced large language models (LLMs), such\nas DeepSeek-R1 and OpenAI-o3, have demonstrated significant progress. However,\ntheir application in professional medical contexts remains underexplored,\nparticularly in evaluating the quality of their reasoning processes alongside\nfinal outputs. Here, we introduce MedR-Bench, a benchmarking dataset of 1,453\nstructured patient cases, annotated with reasoning references derived from\nclinical case reports. Spanning 13 body systems and 10 specialties, it includes\nboth common and rare diseases. To comprehensively evaluate LLM performance, we\npropose a framework encompassing three critical examination recommendation,\ndiagnostic decision-making, and treatment planning, simulating the entire\npatient care journey. To assess reasoning quality, we present the Reasoning\nEvaluator, a novel automated system that objectively scores free-text reasoning\nresponses based on efficiency, actuality, and completeness using dynamic\ncross-referencing and evidence checks. Using this benchmark, we evaluate five\nstate-of-the-art reasoning LLMs, including DeepSeek-R1, OpenAI-o3-mini, and\nGemini-2.0-Flash Thinking, etc. Our results show that current LLMs achieve over\n85% accuracy in relatively simple diagnostic tasks when provided with\nsufficient examination results. However, performance declines in more complex\ntasks, such as examination recommendation and treatment planning. While\nreasoning outputs are generally reliable, with factuality scores exceeding 90%,\ncritical reasoning steps are frequently missed. These findings underscore both\nthe progress and limitations of clinical LLMs. Notably, open-source models like\nDeepSeek-R1 are narrowing the gap with proprietary systems, highlighting their\npotential to drive accessible and equitable advancements in healthcare.\n", "date": "2025-03-06", "categories": []}
{"id": "2503.05701", "title": "OPTIC: Optimizing Patient-Provider Triaging & Improving Communications\n  in Clinical Operations using GPT-4 Data Labeling and Model Distillation", "abstract": "  The COVID-19 pandemic has accelerated the adoption of telemedicine and\npatient messaging through electronic medical portals (patient medical advice\nrequests, or PMARs). While these platforms enhance patient access to\nhealthcare, they have also increased the burden on healthcare providers due to\nthe surge in PMARs. This study seeks to develop an efficient tool for message\ntriaging to reduce physician workload and improve patient-provider\ncommunication. We developed OPTIC (Optimizing Patient-Provider Triaging &\nImproving Communications in Clinical Operations), a powerful message triaging\ntool that utilizes GPT-4 for data labeling and BERT for model distillation. The\nstudy used a dataset of 405,487 patient messaging encounters from Johns Hopkins\nMedicine between January and June 2020. High-quality labeled data was generated\nthrough GPT-4-based prompt engineering, which was then used to train a BERT\nmodel to classify messages as \"Admin\" or \"Clinical.\" The BERT model achieved\n88.85% accuracy on the test set validated by GPT-4 labeling, with a sensitivity\nof 88.29%, specificity of 89.38%, and an F1 score of 0.8842. BERTopic analysis\nidentified 81 distinct topics within the test data, with over 80% accuracy in\nclassifying 58 topics. The system was successfully deployed through Epic's\nNebula Cloud Platform, demonstrating its practical effectiveness in healthcare\nsettings.\n", "date": "2025-02-05", "categories": []}
{"id": "2503.06204", "title": "CUPCase: Clinically Uncommon Patient Cases and Diagnoses Dataset", "abstract": "  Medical benchmark datasets significantly contribute to developing Large\nLanguage Models (LLMs) for medical knowledge extraction, diagnosis,\nsummarization, and other uses. Yet, current benchmarks are mainly derived from\nexam questions given to medical students or cases described in the medical\nliterature, lacking the complexity of real-world patient cases that deviate\nfrom classic textbook abstractions. These include rare diseases, uncommon\npresentations of common diseases, and unexpected treatment responses. Here, we\nconstruct Clinically Uncommon Patient Cases and Diagnosis Dataset (CUPCase)\nbased on 3,562 real-world case reports from BMC, including diagnoses in\nopen-ended textual format and as multiple-choice options with distractors.\nUsing this dataset, we evaluate the ability of state-of-the-art LLMs, including\nboth general-purpose and Clinical LLMs, to identify and correctly diagnose a\npatient case, and test models' performance when only partial information about\ncases is available. Our findings show that general-purpose GPT-4o attains the\nbest performance in both the multiple-choice task (average accuracy of 87.9%)\nand the open-ended task (BERTScore F1 of 0.764), outperforming several LLMs\nwith a focus on the medical domain such as Meditron-70B and MedLM-Large.\nMoreover, GPT-4o was able to maintain 87% and 88% of its performance with only\nthe first 20% of tokens of the case presentation in multiple-choice and free\ntext, respectively, highlighting the potential of LLMs to aid in early\ndiagnosis in real-world cases. CUPCase expands our ability to evaluate LLMs for\nclinical decision support in an open and reproducible manner.\n", "date": "2025-03-08", "categories": []}
{"id": "2412.09651", "title": "Assisted morbidity coding: the SISCO.web use case for identifying the\n  main diagnosis in Hospital Discharge Records", "abstract": "  Coding morbidity data using international standard diagnostic classifications\nis increasingly important and still challenging. Clinical coders and physicians\nassign codes to patient episodes based on their interpretation of case notes or\nelectronic patient records. Therefore, accurate coding relies on the legibility\nof case notes and the coders' understanding of medical terminology. During the\nlast ten years, many studies have shown poor reproducibility of clinical\ncoding, even recently, with the application of Artificial Intelligence-based\nmodels. Given this context, the paper aims to present the SISCO.web approach\ndesigned to support physicians in filling in Hospital Discharge Records with\nproper diagnoses and procedures codes using the International Classification of\nDiseases (9th and 10th), and, above all, in identifying the main pathological\ncondition. The web service leverages NLP algorithms, specific coding rules, as\nwell as ad hoc decision trees to identify the main condition, showing promising\nresults in providing accurate ICD coding suggestions.\n", "date": "2024-12-11", "categories": []}
{"id": "2503.05373", "title": "Leveraging Semantic Type Dependencies for Clinical Named Entity\n  Recognition", "abstract": "  Previous work on clinical relation extraction from free-text sentences\nleveraged information about semantic types from clinical knowledge bases as a\npart of entity representations. In this paper, we exploit additional evidence\nby also making use of domain-specific semantic type dependencies. We encode the\nrelation between a span of tokens matching a Unified Medical Language System\n(UMLS) concept and other tokens in the sentence. We implement our method and\ncompare against different named entity recognition (NER) architectures (i.e.,\nBiLSTM-CRF and BiLSTM-GCN-CRF) using different pre-trained clinical embeddings\n(i.e., BERT, BioBERT, UMLSBert). Our experimental results on clinical datasets\nshow that in some cases NER effectiveness can be significantly improved by\nmaking use of domain-specific semantic type dependencies. Our work is also the\nfirst study generating a matrix encoding to make use of more than three\ndependencies in one pass for the NER task.\n", "date": "2025-03-07", "categories": []}
{"id": "2503.02365", "title": "EchoQA: A Large Collection of Instruction Tuning Data for Echocardiogram\n  Reports", "abstract": "  We introduce a novel question-answering (QA) dataset using echocardiogram\nreports sourced from the Medical Information Mart for Intensive Care database.\nThis dataset is specifically designed to enhance QA systems in cardiology,\nconsisting of 771,244 QA pairs addressing a wide array of cardiac abnormalities\nand their severity. We compare large language models (LLMs), including\nopen-source and biomedical-specific models for zero-shot evaluation, and\nclosed-source models for zero-shot and three-shot evaluation. Our results show\nthat fine-tuning LLMs improves performance across various QA metrics,\nvalidating the value of our dataset. Clinicians also qualitatively evaluate the\nbest-performing model to assess the LLM responses for correctness. Further, we\nconduct fine-grained fairness audits to assess the bias-performance trade-off\nof LLMs across various social determinants of health. Our objective is to\npropel the field forward by establishing a benchmark for LLM AI agents aimed at\nsupporting clinicians with cardiac differential diagnoses, thereby reducing the\ndocumentation burden that contributes to clinician burnout and enabling\nhealthcare professionals to focus more on patient care.\n", "date": "2025-03-04", "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": "2405.18448", "title": "Multi-objective Representation for Numbers in Clinical Narratives: A\n  CamemBERT-Bio-Based Alternative to Large-Scale LLMs", "abstract": "  The processing of numerical values is a rapidly developing area in the field\nof Language Models (LLMs). Despite numerous advancements achieved by previous\nresearch, significant challenges persist, particularly within the healthcare\ndomain. This paper investigates the limitations of Transformer models in\nunderstanding numerical values. \\textit{Objective:} this research aims to\ncategorize numerical values extracted from medical documents into eight\nspecific physiological categories using CamemBERT-bio. \\textit{Methods:} In a\ncontext where scalable methods and Large Language Models (LLMs) are emphasized,\nwe explore lifting the limitations of transformer-based models. We examine two\nstrategies: fine-tuning CamemBERT-bio on a small medical dataset, integrating\nLabel Embedding for Self-Attention (LESA), and combining LESA with additional\nenhancement techniques such as Xval. Given that CamemBERT-bio is already\npre-trained on a large medical dataset, the first approach aims to update its\nencoder with the newly added label embeddings technique. In contrast, the\nsecond approach seeks to develop multiple representations of numbers\n(contextual and magnitude-based) to achieve more robust number embeddings.\n\\textit{Results:} As anticipated, fine-tuning the standard CamemBERT-bio on our\nsmall medical dataset did not improve F1 scores. However, significant\nimprovements were observed with CamemBERT-bio + LESA, resulting in an over 13\\%\nincrease. Similar enhancements were noted when combining LESA with Xval,\noutperforming conventional methods and giving comparable results to GPT-4\n\\textit{Conclusions and Novelty:} This study introduces two innovative\ntechniques for handling numerical data, which are also applicable to other\nmodalities. We illustrate how these techniques can improve the performance of\nTransformer-based models, achieving more reliable classification results even\nwith small datasets.\n", "date": "2024-05-28", "categories": []}
{"id": "2411.15700", "title": "RAMIE: Retrieval-Augmented Multi-task Information Extraction with Large\n  Language Models on Dietary Supplements", "abstract": "  \\textbf{Objective:} We aimed to develop an advanced multi-task large language\nmodel (LLM) framework to extract multiple types of information about dietary\nsupplements (DS) from clinical records.\n  \\textbf{Methods:} We used four core DS information extraction tasks - namely,\nnamed entity recognition (NER: 2,949 clinical sentences), relation extraction\n(RE: 4,892 sentences), triple extraction (TE: 2,949 sentences), and usage\nclassification (UC: 2,460 sentences) as our multitasks. We introduced a novel\nRetrieval-Augmented Multi-task Information Extraction (RAMIE) Framework,\nincluding: 1) employed instruction fine-tuning techniques with task-specific\nprompts, 2) trained LLMs for multiple tasks with improved storage efficiency\nand lower training costs, and 3) incorporated retrieval augmentation generation\n(RAG) techniques by retrieving similar examples from the training set. We\ncompared RAMIE's performance to LLMs with instruction fine-tuning alone and\nconducted an ablation study to assess the contributions of multi-task learning\nand RAG to improved multitasking performance.\n  \\textbf{Results:} With the aid of the RAMIE framework, Llama2-13B achieved an\nF1 score of 87.39 (3.51\\% improvement) on the NER task and demonstrated\noutstanding performance on the RE task with an F1 score of 93.74 (1.15\\%\nimprovement). For the TE task, Llama2-7B scored 79.45 (14.26\\% improvement),\nand MedAlpaca-7B achieved the highest F1 score of 93.45 (0.94\\% improvement) on\nthe UC task. The ablation study revealed that while MTL increased efficiency\nwith a slight trade-off in performance, RAG significantly boosted overall\naccuracy.\n  \\textbf{Conclusion:} This study presents a novel RAMIE framework that\ndemonstrates substantial improvements in multi-task information extraction for\nDS-related data from clinical records. Our framework can potentially be applied\nto other domains.\n", "date": "2024-11-24", "categories": []}
{"id": "2502.14189", "title": "QUAD-LLM-MLTC: Large Language Models Ensemble Learning for Healthcare\n  Text Multi-Label Classification", "abstract": "  The escalating volume of collected healthcare textual data presents a unique\nchallenge for automated Multi-Label Text Classification (MLTC), which is\nprimarily due to the scarcity of annotated texts for training and their nuanced\nnature. Traditional machine learning models often fail to fully capture the\narray of expressed topics. However, Large Language Models (LLMs) have\ndemonstrated remarkable effectiveness across numerous Natural Language\nProcessing (NLP) tasks in various domains, which show impressive computational\nefficiency and suitability for unsupervised learning through prompt\nengineering. Consequently, these LLMs promise an effective MLTC of medical\nnarratives. However, when dealing with various labels, different prompts can be\nrelevant depending on the topic. To address these challenges, the proposed\napproach, QUAD-LLM-MLTC, leverages the strengths of four LLMs: GPT-4o, BERT,\nPEGASUS, and BART. QUAD-LLM-MLTC operates in a sequential pipeline in which\nBERT extracts key tokens, PEGASUS augments textual data, GPT-4o classifies, and\nBART provides topics' assignment probabilities, which results in four\nclassifications, all in a 0-shot setting. The outputs are then combined using\nensemble learning and processed through a meta-classifier to produce the final\nMLTC result. The approach is evaluated using three samples of annotated texts,\nwhich contrast it with traditional and single-model methods. The results show\nsignificant improvements across the majority of the topics in the\nclassification's F1 score and consistency (F1 and Micro-F1 scores of 78.17% and\n80.16% with standard deviations of 0.025 and 0.011, respectively). This\nresearch advances MLTC using LLMs and provides an efficient and scalable\nsolution to rapidly categorize healthcare-related text data without further\ntraining.\n", "date": "2025-02-20", "categories": []}
{"id": "2503.00624", "title": "An evaluation of DeepSeek Models in Biomedical Natural Language\n  Processing", "abstract": "  The advancement of Large Language Models (LLMs) has significantly impacted\nbiomedical Natural Language Processing (NLP), enhancing tasks such as named\nentity recognition, relation extraction, event extraction, and text\nclassification. In this context, the DeepSeek series of models have shown\npromising potential in general NLP tasks, yet their capabilities in the\nbiomedical domain remain underexplored. This study evaluates multiple DeepSeek\nmodels (Distilled-DeepSeek-R1 series and Deepseek-LLMs) across four key\nbiomedical NLP tasks using 12 datasets, benchmarking them against\nstate-of-the-art alternatives (Llama3-8B, Qwen2.5-7B, Mistral-7B, Phi-4-14B,\nGemma-2-9B). Our results reveal that while DeepSeek models perform\ncompetitively in named entity recognition and text classification, challenges\npersist in event and relation extraction due to precision-recall trade-offs. We\nprovide task-specific model recommendations and highlight future research\ndirections. This evaluation underscores the strengths and limitations of\nDeepSeek models in biomedical NLP, guiding their future deployment and\noptimization.\n", "date": "2025-03-01", "categories": []}
{"id": "2503.00863", "title": "Systematic Literature Review on Clinical Trial Eligibility Matching", "abstract": "  Clinical trial eligibility matching is a critical yet often labor-intensive\nand error-prone step in medical research, as it ensures that participants meet\nprecise criteria for safe and reliable study outcomes. Recent advances in\nNatural Language Processing (NLP) have shown promise in automating and\nimproving this process by rapidly analyzing large volumes of unstructured\nclinical text and structured electronic health record (EHR) data. In this\npaper, we present a systematic overview of current NLP methodologies applied to\nclinical trial eligibility screening, focusing on data sources, annotation\npractices, machine learning approaches, and real-world implementation\nchallenges. A comprehensive literature search (spanning Google Scholar,\nMendeley, and PubMed from 2015 to 2024) yielded high-quality studies, each\ndemonstrating the potential of techniques such as rule-based systems, named\nentity recognition, contextual embeddings, and ontology-based normalization to\nenhance patient matching accuracy. While results indicate substantial\nimprovements in screening efficiency and precision, limitations persist\nregarding data completeness, annotation consistency, and model scalability\nacross diverse clinical domains. The review highlights how explainable AI and\nstandardized ontologies can bolster clinician trust and broaden adoption.\nLooking ahead, further research into advanced semantic and temporal\nrepresentations, expanded data integration, and rigorous prospective\nevaluations is necessary to fully realize the transformative potential of NLP\nin clinical trial recruitment.\n", "date": "2025-03-02", "categories": []}
{"id": "2503.01194", "title": "Cancer Type, Stage and Prognosis Assessment from Pathology Reports using\n  LLMs", "abstract": "  Large Language Models (LLMs) have shown significant promise across various\nnatural language processing tasks. However, their application in the field of\npathology, particularly for extracting meaningful insights from unstructured\nmedical texts such as pathology reports, remains underexplored and not well\nquantified. In this project, we leverage state-of-the-art language models,\nincluding the GPT family, Mistral models, and the open-source Llama models, to\nevaluate their performance in comprehensively analyzing pathology reports.\nSpecifically, we assess their performance in cancer type identification, AJCC\nstage determination, and prognosis assessment, encompassing both information\nextraction and higher-order reasoning tasks. Based on a detailed analysis of\ntheir performance metrics in a zero-shot setting, we developed two\ninstruction-tuned models: Path-llama3.1-8B and Path-GPT-4o-mini-FT. These\nmodels demonstrated superior performance in zero-shot cancer type\nidentification, staging, and prognosis assessment compared to the other models\nevaluated.\n", "date": "2025-03-03", "categories": []}
{"id": "2503.01315", "title": "Explainable Depression Detection in Clinical Interviews with\n  Personalized Retrieval-Augmented Generation", "abstract": "  Depression is a widespread mental health disorder, and clinical interviews\nare the gold standard for assessment. However, their reliance on scarce\nprofessionals highlights the need for automated detection. Current systems\nmainly employ black-box neural networks, which lack interpretability, which is\ncrucial in mental health contexts. Some attempts to improve interpretability\nuse post-hoc LLM generation but suffer from hallucination. To address these\nlimitations, we propose RED, a Retrieval-augmented generation framework for\nExplainable depression Detection. RED retrieves evidence from clinical\ninterview transcripts, providing explanations for predictions. Traditional\nquery-based retrieval systems use a one-size-fits-all approach, which may not\nbe optimal for depression detection, as user backgrounds and situations vary.\nWe introduce a personalized query generation module that combines standard\nqueries with user-specific background inferred by LLMs, tailoring retrieval to\nindividual contexts. Additionally, to enhance LLM performance in social\nintelligence, we augment LLMs by retrieving relevant knowledge from a social\nintelligence datastore using an event-centric retriever. Experimental results\non the real-world benchmark demonstrate RED's effectiveness compared to neural\nnetworks and LLM-based baselines.\n", "date": "2025-03-03", "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": "2502.19208", "title": "MultiConAD: A Unified Multilingual Conversational Dataset for Early\n  Alzheimer's Detection", "abstract": "  Dementia is a progressive cognitive syndrome with Alzheimer's disease (AD) as\nthe leading cause. Conversation-based AD detection offers a cost-effective\nalternative to clinical methods, as language dysfunction is an early biomarker\nof AD. However, most prior research has framed AD detection as a binary\nclassification problem, limiting the ability to identify Mild Cognitive\nImpairment (MCI)-a crucial stage for early intervention. Also, studies\nprimarily rely on single-language datasets, mainly in English, restricting\ncross-language generalizability. To address this gap, we make three key\ncontributions. First, we introduce a novel, multilingual dataset for AD\ndetection by unifying 16 publicly available dementia-related conversational\ndatasets. This corpus spans English, Spanish, Chinese, and Greek and\nincorporates both audio and text data derived from a variety of cognitive\nassessment tasks. Second, we perform finer-grained classification, including\nMCI, and evaluate various classifiers using sparse and dense text\nrepresentations. Third, we conduct experiments in monolingual and multilingual\nsettings, finding that some languages benefit from multilingual training while\nothers perform better independently. This study highlights the challenges in\nmultilingual AD detection and enables future research on both language-specific\napproaches and techniques aimed at improving model generalization and\nrobustness.\n", "date": "2025-02-26", "categories": []}
{"id": "2308.14321", "title": "Leveraging Medical Knowledge Graphs Into Large Language Models for\n  Diagnosis Prediction: Design and Application Study", "abstract": "  Electronic Health Records (EHRs) and routine documentation practices play a\nvital role in patients' daily care, providing a holistic record of health,\ndiagnoses, and treatment. However, complex and verbose EHR narratives overload\nhealthcare providers, risking diagnostic inaccuracies. While Large Language\nModels (LLMs) have showcased their potential in diverse language tasks, their\napplication in the healthcare arena needs to ensure the minimization of\ndiagnostic errors and the prevention of patient harm. In this paper, we outline\nan innovative approach for augmenting the proficiency of LLMs in the realm of\nautomated diagnosis generation, achieved through the incorporation of a medical\nknowledge graph (KG) and a novel graph model: Dr.Knows, inspired by the\nclinical diagnostic reasoning process. We derive the KG from the National\nLibrary of Medicine's Unified Medical Language System (UMLS), a robust\nrepository of biomedical knowledge. Our method negates the need for\npre-training and instead leverages the KG as an auxiliary instrument aiding in\nthe interpretation and summarization of complex medical concepts. Using\nreal-world hospital datasets, our experimental results demonstrate that the\nproposed approach of combining LLMs with KG has the potential to improve the\naccuracy of automated diagnosis generation. More importantly, our approach\noffers an explainable diagnostic pathway, edging us closer to the realization\nof AI-augmented diagnostic decision support systems.\n", "date": "2023-08-28", "categories": []}
{"id": "2410.03521", "title": "Building a Chinese Medical Dialogue System: Integrating Large-scale\n  Corpora and Novel Models", "abstract": "  The global COVID-19 pandemic underscored major deficiencies in traditional\nhealthcare systems, hastening the advancement of online medical services,\nespecially in medical triage and consultation. However, existing studies face\ntwo main challenges. First, the scarcity of large-scale, publicly available,\ndomain-specific medical datasets due to privacy concerns, with current datasets\nbeing small and limited to a few diseases, limiting the effectiveness of triage\nmethods based on Pre-trained Language Models (PLMs). Second, existing methods\nlack medical knowledge and struggle to accurately understand professional terms\nand expressions in patient-doctor consultations. To overcome these obstacles,\nwe construct the Large-scale Chinese Medical Dialogue Corpora (LCMDC), thereby\naddressing the data shortage in this field. Moreover, we further propose a\nnovel triage system that combines BERT-based supervised learning with prompt\nlearning, as well as a GPT-based medical consultation model. To enhance domain\nknowledge acquisition, we pre-trained PLMs using our self-constructed\nbackground corpus. Experimental results on the LCMDC demonstrate the efficacy\nof our proposed systems.\n", "date": "2024-09-27", "categories": []}
{"id": "2502.17571", "title": "Towards Conditioning Clinical Text Generation for User Control", "abstract": "  Deploying natural language generation systems in clinical settings remains\nchallenging despite advances in Large Language Models (LLMs), which continue to\nexhibit hallucinations and factual inconsistencies, necessitating human\noversight. This paper explores automated dataset augmentation using LLMs as\nhuman proxies to condition LLMs for clinician control without increasing\ncognitive workload. On the BioNLP ACL'24 Discharge Me! Shared Task, we achieve\nnew state-of-the-art results with simpler methods than prior submissions\nthrough more efficient training, yielding a 9\\% relative improvement without\naugmented training and up to 34\\% with dataset augmentation. Preliminary human\nevaluation further supports the effectiveness of our approach, highlighting the\npotential of augmenting clinical text generation for control to enhance\nrelevance, accuracy, and factual consistency.\n", "date": "2025-02-24", "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": "2501.12538", "title": "Academic case reports lack diversity: Assessing the presence and\n  diversity of sociodemographic and behavioral factors related to Post COVID-19\n  Condition", "abstract": "  Understanding the prevalence, disparities, and symptom variations of Post\nCOVID-19 Condition (PCC) for vulnerable populations is crucial to improving\ncare and addressing intersecting inequities. This study aims to develop a\ncomprehensive framework for integrating social determinants of health (SDOH)\ninto PCC research by leveraging NLP techniques to analyze disparities and\nvariations in SDOH representation within PCC case reports. Following\nconstruction of a PCC Case Report Corpus, comprising over 7,000 case reports\nfrom the LitCOVID repository, a subset of 709 reports were annotated with 26\ncore SDOH-related entity types using pre-trained named entity recognition (NER)\nmodels, human review, and data augmentation to improve quality, diversity and\nrepresentation of entity types. An NLP pipeline integrating NER, natural\nlanguage inference (NLI), trigram and frequency analyses was developed to\nextract and analyze these entities. Both encoder-only transformer models and\nRNN-based models were assessed for the NER objective.\n  Fine-tuned encoder-only BERT models outperformed traditional RNN-based models\nin generalizability to distinct sentence structures and greater class sparsity.\nExploratory analysis revealed variability in entity richness, with prevalent\nentities like condition, age, and access to care, and underrepresentation of\nsensitive categories like race and housing status. Trigram analysis highlighted\nfrequent co-occurrences among entities, including age, gender, and condition.\nThe NLI objective (entailment and contradiction analysis) showed attributes\nlike \"Experienced violence or abuse\" and \"Has medical insurance\" had high\nentailment rates (82.4%-80.3%), while attributes such as \"Is\nfemale-identifying,\" \"Is married,\" and \"Has a terminal condition\" exhibited\nhigh contradiction rates (70.8%-98.5%).\n", "date": "2025-01-21", "categories": []}
{"id": "2407.04629", "title": "Entity Decomposition with Filtering: A Zero-Shot Clinical Named Entity\n  Recognition Framework", "abstract": "  Clinical named entity recognition (NER) aims to retrieve important entities\nwithin clinical narratives. Recent works have demonstrated that large language\nmodels (LLMs) can achieve strong performance in this task. While previous works\nfocus on proprietary LLMs, we investigate how open NER LLMs, trained\nspecifically for entity recognition, perform in clinical NER. Our initial\nexperiment reveals significant contrast in performance for some clinical\nentities and how a simple exploitment on entity types can alleviate this issue.\nIn this paper, we introduce a novel framework, entity decomposition with\nfiltering, or EDF. Our key idea is to decompose the entity recognition task\ninto several retrievals of entity sub-types and then filter them. Our\nexperimental results demonstrate the efficacies of our framework and the\nimprovements across all metrics, models, datasets, and entity types. Our\nanalysis also reveals substantial improvement in recognizing previously missed\nentities using entity decomposition. We further provide a comprehensive\nevaluation of our framework and an in-depth error analysis to pave future\nworks.\n", "date": "2024-07-05", "categories": []}
{"id": "2410.23528", "title": "Large Language Models for Patient Comments Multi-Label Classification", "abstract": "  Patient experience and care quality are crucial for a hospital's\nsustainability and reputation. The analysis of patient feedback offers valuable\ninsight into patient satisfaction and outcomes. However, the unstructured\nnature of these comments poses challenges for traditional machine learning\nmethods following a supervised learning paradigm. This is due to the\nunavailability of labeled data and the nuances these texts encompass. This\nresearch explores leveraging Large Language Models (LLMs) in conducting\nMulti-label Text Classification (MLTC) of inpatient comments shared after a\nstay in the hospital. GPT-4 Turbo was leveraged to conduct the classification.\nHowever, given the sensitive nature of patients' comments, a security layer is\nintroduced before feeding the data to the LLM through a Protected Health\nInformation (PHI) detection framework, which ensures patients'\nde-identification. Additionally, using the prompt engineering framework,\nzero-shot learning, in-context learning, and chain-of-thought prompting were\nexperimented with. Results demonstrate that GPT-4 Turbo, whether following a\nzero-shot or few-shot setting, outperforms traditional methods and Pre-trained\nLanguage Models (PLMs) and achieves the highest overall performance with an\nF1-score of 76.12% and a weighted F1-score of 73.61% followed closely by the\nfew-shot learning results. Subsequently, the results' association with other\npatient experience structured variables (e.g., rating) was conducted. The study\nenhances MLTC through the application of LLMs, offering healthcare\npractitioners an efficient method to gain deeper insights into patient feedback\nand deliver prompt, appropriate responses.\n", "date": "2024-10-31", "categories": []}
{"id": "2502.06666", "title": "Automatic Evaluation of Healthcare LLMs Beyond Question-Answering", "abstract": "  Current Large Language Models (LLMs) benchmarks are often based on open-ended\nor close-ended QA evaluations, avoiding the requirement of human labor.\nClose-ended measurements evaluate the factuality of responses but lack\nexpressiveness. Open-ended capture the model's capacity to produce discourse\nresponses but are harder to assess for correctness. These two approaches are\ncommonly used, either independently or together, though their relationship\nremains poorly understood. This work is focused on the healthcare domain, where\nboth factuality and discourse matter greatly. It introduces a comprehensive,\nmulti-axis suite for healthcare LLM evaluation, exploring correlations between\nopen and close benchmarks and metrics. Findings include blind spots and\noverlaps in current methodologies. As an updated sanity check, we release a new\nmedical benchmark --CareQA-- with both open and closed variants. Finally, we\npropose a novel metric for open-ended evaluations -- Relaxed Perplexity -- to\nmitigate the identified limitations.\n", "date": "2025-02-10", "categories": []}
{"id": "2405.14766", "title": "Evaluating Large Language Models for Public Health Classification and\n  Extraction Tasks", "abstract": "  Advances in Large Language Models (LLMs) have led to significant interest in\ntheir potential to support human experts across a range of domains, including\npublic health. In this work we present automated evaluations of LLMs for public\nhealth tasks involving the classification and extraction of free text. We\ncombine six externally annotated datasets with seven new internally annotated\ndatasets to evaluate LLMs for processing text related to: health burden,\nepidemiological risk factors, and public health interventions. We evaluate\neleven open-weight LLMs (7-123 billion parameters) across all tasks using\nzero-shot in-context learning. We find that Llama-3.3-70B-Instruct is the\nhighest performing model, achieving the best results on 8/16 tasks (using\nmicro-F1 scores). We see significant variation across tasks with all\nopen-weight LLMs scoring below 60% micro-F1 on some challenging tasks, such as\nContact Classification, while all LLMs achieve greater than 80% micro-F1 on\nothers, such as GI Illness Classification. For a subset of 11 tasks, we also\nevaluate three GPT-4 and GPT-4o series models and find comparable results to\nLlama-3.3-70B-Instruct. Overall, based on these initial results we find\npromising signs that LLMs may be useful tools for public health experts to\nextract information from a wide variety of free text sources, and support\npublic health surveillance, research, and interventions.\n", "date": "2024-05-23", "categories": []}
{"id": "2412.00230", "title": "Clinical Document Corpora -- Real Ones, Translated and Synthetic\n  Substitutes, and Assorted Domain Proxies: A Survey of Diversity in Corpus\n  Design, with Focus on German Text Data", "abstract": "  We survey clinical document corpora, with focus on German textual data. Due\nto rigid data privacy legislation in Germany these resources, with only few\nexceptions, are stored in safe clinical data spaces and locked against\nclinic-external researchers. This situation stands in stark contrast with\nestablished workflows in the field of natural language processing where easy\naccessibility and reuse of data collections are common practice. Hence,\nalternative corpus designs have been examined to escape from this data poverty.\nBesides machine translation of English clinical datasets and the generation of\nsynthetic corpora with fictitious clinical contents, several other types of\ndomain proxies have come up as substitutes for clinical documents. Common\ninstances of close proxies are medical journal publications, therapy\nguidelines, drug labels, etc., more distant proxies include online encyclopedic\nmedical articles or medical contents from social media channels. After\nPRISM-conformant identification of 362 hits from 4 bibliographic systems, 78\nrelevant documents were finally selected for this review. They contained\noverall 92 different published versions of corpora from which 71 were truly\nunique in terms of their underlying document sets. Out of these, the majority\nwere clinical corpora -- 46 real ones, 5 translated ones, and 6 synthetic ones.\nAs to domain proxies, we identified 18 close and 17 distant ones. There is a\nclear divide between the large number of non-accessible authentic clinical\nGerman-language corpora and their publicly accessible substitutes: translated\nor synthetic, close or more distant proxies. So on first sight, the data\nbottleneck seems broken. Yet differences in genre-specific writing style,\nwording and medical domain expertise in this typological space are also\nobvious. This raises the question how valid alternative corpus designs really\nare.\n", "date": "2024-11-29", "categories": []}
{"id": "2502.13342", "title": "Beyond De-Identification: A Structured Approach for Defining and\n  Detecting Indirect Identifiers in Medical Texts", "abstract": "  Sharing sensitive texts for scientific purposes requires appropriate\ntechniques to protect the privacy of patients and healthcare personnel.\nAnonymizing textual data is particularly challenging due to the presence of\ndiverse unstructured direct and indirect identifiers. To mitigate the risk of\nre-identification, this work introduces a schema of nine categories of indirect\nidentifiers designed to account for different potential adversaries, including\nacquaintances, family members and medical staff. Using this schema, we annotate\n100 MIMIC-III discharge summaries and propose baseline models for identifying\nindirect identifiers. We will release the annotation guidelines, annotation\nspans (6,199 annotations in total) and the corresponding MIMIC-III document IDs\nto support further research in this area.\n", "date": "2025-02-18", "categories": []}
{"id": "2311.15054", "title": "Detection of developmental language disorder in Cypriot Greek children\n  using a neural network algorithm", "abstract": "  Children with developmental language disorder (DLD) encounter difficulties in\nacquiring various language structures. Early identification and intervention\nare crucial to prevent negative long-term outcomes impacting the academic,\nsocial, and emotional development of children. The study aims to develop an\nautomated method for the identification of DLD using artificial intelligence,\nspecifically a neural network machine learning algorithm. This protocol is\napplied for the first time in a Cypriot Greek child population with DLD. The\nneural network model was trained using perceptual and production data elicited\nfrom 15 children with DLD and 15 healthy controls in the age range of 7;10\nuntil 10;4. The k-fold technique was used to crossvalidate the algorithm. The\nperformance of the model was evaluated using metrics such as accuracy,\nprecision, recall, F1 score, and ROC/AUC curve to assess its ability to make\naccurate predictions on a set of unseen data. The results demonstrated high\nclassification values for all metrics, indicating the high accuracy of the\nneural model in classifying children with DLD. Additionally, the variable\nimportance analysis revealed that the language production skills of children\nhad a more significant impact on the performance of the model compared to\nperception skills. Machine learning paradigms provide effective discrimination\nbetween children with DLD and those with TD, with the potential to enhance\nclinical assessment and facilitate earlier and more efficient detection of the\ndisorder.\n", "date": "2023-11-25", "categories": []}
{"id": "2502.12362", "title": "Classifiers of Data Sharing Statements in Clinical Trial Records", "abstract": "  Digital individual participant data (IPD) from clinical trials are\nincreasingly distributed for potential scientific reuse. The identification of\navailable IPD, however, requires interpretations of textual data-sharing\nstatements (DSS) in large databases. Recent advancements in computational\nlinguistics include pre-trained language models that promise to simplify the\nimplementation of effective classifiers based on textual inputs. In a subset of\n5,000 textual DSS from ClinicalTrials.gov, we evaluate how well classifiers\nbased on domain-specific pre-trained language models reproduce original\navailability categories as well as manually annotated labels. Typical metrics\nindicate that classifiers that predicted manual annotations outperformed those\nthat learned to output the original availability categories. This suggests that\nthe textual DSS descriptions contain applicable information that the\navailability categories do not, and that such classifiers could thus aid the\nautomatic identification of available IPD in large trial databases.\n", "date": "2025-02-17", "categories": []}
{"id": "2405.20582", "title": "The Point of View of a Sentiment: Towards Clinician Bias Detection in\n  Psychiatric Notes", "abstract": "  Negative patient descriptions and stigmatizing language can contribute to\ngenerating healthcare disparities in two ways: (1) read by patients, they can\nharm their trust and engagement with the medical center; (2) read by\nphysicians, they may negatively influence their perspective of a future\npatient. In psychiatry, the patient-clinician therapeutic alliance is a major\ndeterminant of clinical outcomes. Therefore, language usage in psychiatric\nclinical notes may not only create healthcare disparities, but also perpetuate\nthem. Recent advances in NLP systems have facilitated the efforts to detect\ndiscriminatory language in healthcare. However, such attempts have only focused\non the perspectives of the medical center and its physicians. Considering both\nphysicians and non-physicians' point of view is a more translatable approach to\nidentifying potentially harmful language in clinical notes. By leveraging\npre-trained and large language models (PLMs and LLMs), this work aims to\ncharacterize potentially harmful language usage in psychiatric notes by\nidentifying the sentiment expressed in sentences describing patients based on\nthe reader's point of view. Extracting 39 sentences from the Mount Sinai Health\nSystem containing psychiatric lexicon, we fine-tuned three PLMs (RoBERTa,\nGatorTron, and GatorTron + Task Adaptation) and implemented zero-shot and\nfew-shot ICL approaches for three LLMs (GPT-3.5, Llama-3.1, and Mistral) to\nclassify the sentiment of the sentences according to the physician or\nnon-physician point of view. Results showed that GPT-3.5 aligned best to\nphysician point of view and Mistral aligned best to non-physician point of\nview. These results underline the importance of recognizing the reader's point\nof view, not only for improving the note writing process, but also for the\nquantification, identification, and reduction of bias in computational systems\nfor downstream analyses.\n", "date": "2024-05-31", "categories": []}
{"id": "2406.14326", "title": "medIKAL: Integrating Knowledge Graphs as Assistants of LLMs for Enhanced\n  Clinical Diagnosis on EMRs", "abstract": "  Electronic Medical Records (EMRs), while integral to modern healthcare,\npresent challenges for clinical reasoning and diagnosis due to their complexity\nand information redundancy. To address this, we proposed medIKAL (Integrating\nKnowledge Graphs as Assistants of LLMs), a framework that combines Large\nLanguage Models (LLMs) with knowledge graphs (KGs) to enhance diagnostic\ncapabilities. medIKAL assigns weighted importance to entities in medical\nrecords based on their type, enabling precise localization of candidate\ndiseases within KGs. It innovatively employs a residual network-like approach,\nallowing initial diagnosis by the LLM to be merged into KG search results.\nThrough a path-based reranking algorithm and a fill-in-the-blank style prompt\ntemplate, it further refined the diagnostic process. We validated medIKAL's\neffectiveness through extensive experiments on a newly introduced open-sourced\nChinese EMR dataset, demonstrating its potential to improve clinical diagnosis\nin real-world settings.\n", "date": "2024-06-20", "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": "2502.09715", "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": "2405.14445", "title": "Exploring the use of a Large Language Model for data extraction in\n  systematic reviews: a rapid feasibility study", "abstract": "  This paper describes a rapid feasibility study of using GPT-4, a large\nlanguage model (LLM), to (semi)automate data extraction in systematic reviews.\nDespite the recent surge of interest in LLMs there is still a lack of\nunderstanding of how to design LLM-based automation tools and how to robustly\nevaluate their performance. During the 2023 Evidence Synthesis Hackathon we\nconducted two feasibility studies. Firstly, to automatically extract study\ncharacteristics from human clinical, animal, and social science domain studies.\nWe used two studies from each category for prompt-development; and ten for\nevaluation. Secondly, we used the LLM to predict Participants, Interventions,\nControls and Outcomes (PICOs) labelled within 100 abstracts in the EBM-NLP\ndataset. Overall, results indicated an accuracy of around 80%, with some\nvariability between domains (82% for human clinical, 80% for animal, and 72%\nfor studies of human social sciences). Causal inference methods and study\ndesign were the data extraction items with the most errors. In the PICO study,\nparticipants and intervention/control showed high accuracy (>80%), outcomes\nwere more challenging. Evaluation was done manually; scoring methods such as\nBLEU and ROUGE showed limited value. We observed variability in the LLMs\npredictions and changes in response quality. This paper presents a template for\nfuture evaluations of LLMs in the context of data extraction for systematic\nreview automation. Our results show that there might be value in using LLMs,\nfor example as second or third reviewers. However, caution is advised when\nintegrating models such as GPT-4 into tools. Further research on stability and\nreliability in practical settings is warranted for each type of data that is\nprocessed by the LLM.\n", "date": "2024-05-23", "categories": []}
{"id": "2502.09247", "title": "The Joint Entity-Relation Extraction Model Based on Span and Interactive\n  Fusion Representation for Chinese Medical Texts with Complex Semantics", "abstract": "  Joint entity-relation extraction is a critical task in transforming\nunstructured or semi-structured text into triplets, facilitating the\nconstruction of large-scale knowledge graphs, and supporting various downstream\napplications. Despite its importance, research on Chinese text, particularly\nwith complex semantics in specialized domains like medicine, remains limited.\nTo address this gap, we introduce the CH-DDI, a Chinese drug-drug interactions\ndataset designed to capture the intricacies of medical text. Leveraging the\nstrengths of attention mechanisms in capturing long-range dependencies, we\npropose the SEA module, which enhances the extraction of complex contextual\nsemantic information, thereby improving entity recognition and relation\nextraction. Additionally, to address the inefficiencies of existing methods in\nfacilitating information exchange between entity recognition and relation\nextraction, we present an interactive fusion representation module. This module\nemploys Cross Attention for bidirectional information exchange between the\ntasks and further refines feature extraction through BiLSTM. Experimental\nresults on both our CH-DDI dataset and public CoNLL04 dataset demonstrate that\nour model exhibits strong generalization capabilities. On the CH-DDI dataset,\nour model achieves an F1-score of 96.73% for entity recognition and 78.43% for\nrelation extraction. On the CoNLL04 dataset, it attains an entity recognition\nprecision of 89.54% and a relation extraction accuracy of 71.64%.\n", "date": "2025-02-13", "categories": []}
{"id": "2405.18553", "title": "FAIIR: Building Toward A Conversational AI Agent Assistant for Youth\n  Mental Health Service Provision", "abstract": "  The world's healthcare systems and mental health agencies face both a growing\ndemand for youth mental health services, alongside a simultaneous challenge of\nlimited resources. Here, we focus on frontline crisis support, where Crisis\nResponders (CRs) engage in conversations for youth mental health support and\nassign an issue tag to each conversation. In this study, we develop FAIIR\n(Frontline Assistant: Issue Identification and Recommendation), an advanced\ntool leveraging an ensemble of domain-adapted and fine-tuned transformer models\ntrained on a large conversational dataset comprising 780,000 conversations. The\nprimary aim is to reduce the cognitive burden on CRs, enhance the accuracy of\nissue identification, and streamline post-conversation administrative tasks. We\nevaluate FAIIR on both retrospective and prospective conversations, emphasizing\nhuman-in-the-loop design with active CR engagement for model refinement,\nconsensus-building, and overall assessment. Our results indicate that FAIIR\nachieves an average AUCROC of 94%, a sample average F1-score of 64%, and a\nsample average recall score of 81% on the retrospective test set. We also\ndemonstrate the robustness and generalizability of the FAIIR tool during the\nsilent testing phase, with less than a 2% drop in all performance metrics.\nNotably, CRs' responses exhibited an overall agreement of 90.9% with FAIIR's\npredictions. Furthermore, expert agreement with FAIIR surpassed their agreement\nwith the original labels. To conclude, our findings indicate that assisting\nwith the identification of issues of relevance helps reduce the burden on CRs,\nensuring that appropriate resources can be provided and that active rescues and\nmandatory reporting can take place in critical situations requiring immediate\nde-escalation.\n", "date": "2024-05-28", "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": "2502.07755", "title": "An Advanced NLP Framework for Automated Medical Diagnosis with DeBERTa\n  and Dynamic Contextual Positional Gating", "abstract": "  This paper presents a novel Natural Language Processing (NLP) framework for\nenhancing medical diagnosis through the integration of advanced techniques in\ndata augmentation, feature extraction, and classification. The proposed\napproach employs back-translation to generate diverse paraphrased datasets,\nimproving robustness and mitigating overfitting in classification tasks.\nLeveraging Decoding-enhanced BERT with Disentangled Attention (DeBERTa) with\nDynamic Contextual Positional Gating (DCPG), the model captures fine-grained\ncontextual and positional relationships, dynamically adjusting the influence of\npositional information based on semantic context to produce high-quality text\nembeddings. For classification, an Attention-Based Feedforward Neural Network\n(ABFNN) is utilized, effectively focusing on the most relevant features to\nimprove decision-making accuracy. Applied to the classification of symptoms,\nclinical notes, and other medical texts, this architecture demonstrates its\nability to address the complexities of medical data. The combination of data\naugmentation, contextual embedding generation, and advanced classification\nmechanisms offers a robust and accurate diagnostic tool, with potential\napplications in automated medical diagnosis and clinical decision support. This\nmethod demonstrates the effectiveness of the proposed NLP framework for medical\ndiagnosis, achieving remarkable results with an accuracy of 99.78%, recall of\n99.72%, precision of 99.79%, and an F1-score of 99.75%. These metrics not only\nunderscore the model's robust performance in classifying medical texts with\nexceptional precision and reliability but also highlight its superiority over\nexisting methods, making it a highly promising tool for automated diagnostic\nsystems.\n", "date": "2025-02-11", "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": "2409.19737", "title": "A Systematic Review of NLP for Dementia -- Tasks, Datasets and\n  Opportunities", "abstract": "  The close link between cognitive decline and language has fostered\nlong-standing collaboration between the NLP and medical communities in dementia\nresearch. To examine this, we reviewed over 240 papers applying NLP to\ndementia-related efforts, drawing from medical, technological, and NLP-focused\nliterature. We identify key research areas, including dementia detection,\nlinguistic biomarker extraction, caregiver support, and patient assistance,\nshowing that half of all papers focus solely on dementia detection using\nclinical data. Yet, many directions remain unexplored -- artificially degraded\nlanguage models, synthetic data, digital twins, and more. We highlight gaps and\nopportunities around trust, scientific rigor, applicability and cross-community\ncollaboration. We raise ethical dilemmas in the field, and highlight the\ndiverse datasets encountered throughout our review -- recorded, written,\nstructured, spontaneous, synthetic, clinical, social media-based, and more.\nThis review aims to inspire more creative, impactful, and rigorous research on\nNLP for dementia.\n", "date": "2024-09-29", "categories": []}
{"id": "2502.04372", "title": "Mining Unstructured Medical Texts With Conformal Active Learning", "abstract": "  The extraction of relevant data from Electronic Health Records (EHRs) is\ncrucial to identifying symptoms and automating epidemiological surveillance\nprocesses. By harnessing the vast amount of unstructured text in EHRs, we can\ndetect patterns that indicate the onset of disease outbreaks, enabling faster,\nmore targeted public health responses. Our proposed framework provides a\nflexible and efficient solution for mining data from unstructured texts,\nsignificantly reducing the need for extensive manual labeling by specialists.\nExperiments show that our framework achieving strong performance with as few as\n200 manually labeled texts, even for complex classification problems.\nAdditionally, our approach can function with simple lightweight models,\nachieving competitive and occasionally even better results compared to more\nresource-intensive deep learning models. This capability not only accelerates\nprocessing times but also preserves patient privacy, as the data can be\nprocessed on weaker on-site hardware rather than being transferred to external\nsystems. Our methodology, therefore, offers a practical, scalable, and\nprivacy-conscious approach to real-time epidemiological monitoring, equipping\nhealth institutions to respond rapidly and effectively to emerging health\nthreats.\n", "date": "2025-02-05", "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": "2502.04288", "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": "2502.03257", "title": "Efficient extraction of medication information from clinical notes: an\n  evaluation in two languages", "abstract": "  Objective: To evaluate the accuracy, computational cost and portability of a\nnew Natural Language Processing (NLP) method for extracting medication\ninformation from clinical narratives. Materials and Methods: We propose an\noriginal transformer-based architecture for the extraction of entities and\ntheir relations pertaining to patients' medication regimen. First, we used this\napproach to train and evaluate a model on French clinical notes, using a newly\nannotated corpus from H\\^opitaux Universitaires de Strasbourg. Second, the\nportability of the approach was assessed by conducting an evaluation on\nclinical documents in English from the 2018 n2c2 shared task. Information\nextraction accuracy and computational cost were assessed by comparison with an\navailable method using transformers. Results: The proposed architecture\nachieves on the task of relation extraction itself performance that are\ncompetitive with the state-of-the-art on both French and English (F-measures\n0.82 and 0.96 vs 0.81 and 0.95), but reduce the computational cost by 10.\nEnd-to-end (Named Entity recognition and Relation Extraction) F1 performance is\n0.69 and 0.82 for French and English corpus. Discussion: While an existing\nsystem developed for English notes was deployed in a French hospital setting\nwith reasonable effort, we found that an alternative architecture offered\nend-to-end drug information extraction with comparable extraction performance\nand lower computational impact for both French and English clinical text\nprocessing, respectively. Conclusion: The proposed architecture can be used to\nextract medication information from clinical text with high performance and low\ncomputational cost and consequently suits with usually limited hospital IT\nresources\n", "date": "2025-02-05", "categories": []}
{"id": "2502.03298", "title": "MeDiSumQA: Patient-Oriented Question-Answer Generation from Discharge\n  Letters", "abstract": "  While increasing patients' access to medical documents improves medical care,\nthis benefit is limited by varying health literacy levels and complex medical\nterminology. Large language models (LLMs) offer solutions by simplifying\nmedical information. However, evaluating LLMs for safe and patient-friendly\ntext generation is difficult due to the lack of standardized evaluation\nresources. To fill this gap, we developed MeDiSumQA. MeDiSumQA is a dataset\ncreated from MIMIC-IV discharge summaries through an automated pipeline\ncombining LLM-based question-answer generation with manual quality checks. We\nuse this dataset to evaluate various LLMs on patient-oriented\nquestion-answering. Our findings reveal that general-purpose LLMs frequently\nsurpass biomedical-adapted models, while automated metrics correlate with human\njudgment. By releasing MeDiSumQA on PhysioNet, we aim to advance the\ndevelopment of LLMs to enhance patient understanding and ultimately improve\ncare outcomes.\n", "date": "2025-02-05", "categories": []}
{"id": "2410.22180", "title": "Natural Language Processing for Analyzing Electronic Health Records and\n  Clinical Notes in Cancer Research: A Review", "abstract": "  Objective: This review aims to analyze the application of natural language\nprocessing (NLP) techniques in cancer research using electronic health records\n(EHRs) and clinical notes. This review addresses gaps in the existing\nliterature by providing a broader perspective than previous studies focused on\nspecific cancer types or applications. Methods: A comprehensive literature\nsearch was conducted using the Scopus database, identifying 94 relevant studies\npublished between 2019 and 2024. Data extraction included study\ncharacteristics, cancer types, NLP methodologies, dataset information,\nperformance metrics, challenges, and future directions. Studies were\ncategorized based on cancer types and NLP applications. Results: The results\nshowed a growing trend in NLP applications for cancer research, with breast,\nlung, and colorectal cancers being the most studied. Information extraction and\ntext classification emerged as predominant NLP tasks. A shift from rule-based\nto advanced machine learning techniques, particularly transformer-based models,\nwas observed. The Dataset sizes used in existing studies varied widely. Key\nchallenges included the limited generalizability of proposed solutions and the\nneed for improved integration into clinical workflows. Conclusion: NLP\ntechniques show significant potential in analyzing EHRs and clinical notes for\ncancer research. However, future work should focus on improving model\ngeneralizability, enhancing robustness in handling complex clinical language,\nand expanding applications to understudied cancer types. Integration of NLP\ntools into clinical practice and addressing ethical considerations remain\ncrucial for utilizing the full potential of NLP in enhancing cancer diagnosis,\ntreatment, and patient outcomes.\n", "date": "2024-10-29", "categories": []}
{"id": "2502.01685", "title": "Automated Extraction of Spatio-Semantic Graphs for Identifying Cognitive\n  Impairment", "abstract": "  Existing methods for analyzing linguistic content from picture descriptions\nfor assessment of cognitive-linguistic impairment often overlook the\nparticipant's visual narrative path, which typically requires eye tracking to\nassess. Spatio-semantic graphs are a useful tool for analyzing this narrative\npath from transcripts alone, however they are limited by the need for manual\ntagging of content information units (CIUs). In this paper, we propose an\nautomated approach for estimation of spatio-semantic graphs (via automated\nextraction of CIUs) from the Cookie Theft picture commonly used in\ncognitive-linguistic analyses. The method enables the automatic\ncharacterization of the visual semantic path during picture description.\nExperiments demonstrate that the automatic spatio-semantic graphs effectively\ndifferentiate between cognitively impaired and unimpaired speakers. Statistical\nanalyses reveal that the features derived by the automated method produce\ncomparable results to the manual method, with even greater group differences\nbetween clinical groups of interest. These results highlight the potential of\nthe automated approach for extracting spatio-semantic features in developing\nclinical speech models for cognitive impairment assessment.\n", "date": "2025-02-02", "categories": []}
{"id": "2502.01691", "title": "Agent-Based Uncertainty Awareness Improves Automated Radiology Report\n  Labeling with an Open-Source Large Language Model", "abstract": "  Reliable extraction of structured data from radiology reports using Large\nLanguage Models (LLMs) remains challenging, especially for complex, non-English\ntexts like Hebrew. This study introduces an agent-based uncertainty-aware\napproach to improve the trustworthiness of LLM predictions in medical\napplications. We analyzed 9,683 Hebrew radiology reports from Crohn's disease\npatients (from 2010 to 2023) across three medical centers. A subset of 512\nreports was manually annotated for six gastrointestinal organs and 15\npathological findings, while the remaining reports were automatically annotated\nusing HSMP-BERT. Structured data extraction was performed using Llama 3.1\n(Llama 3-8b-instruct) with Bayesian Prompt Ensembles (BayesPE), which employed\nsix semantically equivalent prompts to estimate uncertainty. An Agent-Based\nDecision Model integrated multiple prompt outputs into five confidence levels\nfor calibrated uncertainty and was compared against three entropy-based models.\nPerformance was evaluated using accuracy, F1 score, precision, recall, and\nCohen's Kappa before and after filtering high-uncertainty cases. The\nagent-based model outperformed the baseline across all metrics, achieving an F1\nscore of 0.3967, recall of 0.6437, and Cohen's Kappa of 0.3006. After filtering\nhigh-uncertainty cases (greater than or equal to 0.5), the F1 score improved to\n0.4787, and Kappa increased to 0.4258. Uncertainty histograms demonstrated\nclear separation between correct and incorrect predictions, with the\nagent-based model providing the most well-calibrated uncertainty estimates. By\nincorporating uncertainty-aware prompt ensembles and an agent-based decision\nmodel, this approach enhances the performance and reliability of LLMs in\nstructured data extraction from radiology reports, offering a more\ninterpretable and trustworthy solution for high-stakes medical applications.\n", "date": "2025-02-02", "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": "2502.00063", "title": "A Multi-Layered Large Language Model Framework for Disease Prediction", "abstract": "  Social telehealth has revolutionized healthcare by enabling patients to share\nsymptoms and receive medical consultations remotely. Users frequently post\nsymptoms on social media and online health platforms, generating a vast\nrepository of medical data that can be leveraged for disease classification and\nsymptom severity assessment. Large language models (LLMs), such as LLAMA3,\nGPT-3.5 Turbo, and BERT, process complex medical data to enhance disease\nclassification. This study explores three Arabic medical text preprocessing\ntechniques: text summarization, text refinement, and Named Entity Recognition\n(NER). Evaluating CAMeL-BERT, AraBERT, and Asafaya-BERT with LoRA, the best\nperformance was achieved using CAMeL-BERT with NER-augmented text (83% type\nclassification, 69% severity assessment). Non-fine-tuned models performed\npoorly (13%-20% type classification, 40%-49% severity assessment). Integrating\nLLMs into social telehealth systems enhances diagnostic accuracy and treatment\noutcomes.\n", "date": "2025-01-30", "categories": []}
{"id": "2502.00832", "title": "Generalization of Medical Large Language Models through Cross-Domain\n  Weak Supervision", "abstract": "  The advancement of large language models (LLMs) has opened new frontiers in\nnatural language processing, particularly in specialized domains like\nhealthcare. In this paper, we propose the Incremental Curriculum-Based\nFine-Tuning (ICFT) framework to enhance the generative capabilities of medical\nlarge language models (MLLMs). ICFT combines curriculum-based learning,\ndual-stage memory coordination, and parameter-efficient fine-tuning to enable a\nprogressive transition from general linguistic knowledge to strong\ndomain-specific expertise. Experimental results across diverse medical NLP\ntasks, including question answering, preference classification, and response\ngeneration, demonstrate that ICFT consistently outperforms state-of-the-art\nbaselines, achieving improvements in both accuracy and efficiency. Further\nanalysis reveals the framework's ability to generalize to unseen data, reduce\nerrors, and deliver diverse, contextually relevant medical responses. These\nfindings establish ICFT as a robust and scalable solution for adapting LLMs to\nthe medical domain, offering practical benefits for real-world healthcare\napplications.\n", "date": "2025-02-02", "categories": []}
{"id": "2501.19134", "title": "Mixed Feelings: Cross-Domain Sentiment Classification of Patient\n  Feedback", "abstract": "  Sentiment analysis of patient feedback from the public health domain can aid\ndecision makers in evaluating the provided services. The current paper focuses\non free-text comments in patient surveys about general practitioners and\npsychiatric healthcare, annotated with four sentence-level polarity classes --\npositive, negative, mixed and neutral -- while also attempting to alleviate\ndata scarcity by leveraging general-domain sources in the form of reviews. For\nseveral different architectures, we compare in-domain and out-of-domain\neffects, as well as the effects of training joint multi-domain models.\n", "date": "2025-01-31", "categories": []}
{"id": "2409.09831", "title": "Generating Synthetic Free-text Medical Records with Low\n  Re-identification Risk using Masked Language Modeling", "abstract": "  The vast amount of available medical records has the potential to improve\nhealthcare and biomedical research. However, privacy restrictions make these\ndata accessible for internal use only. Recent works have addressed this problem\nby generating synthetic data using Causal Language Modeling. Unfortunately, by\ntaking this approach, it is often impossible to guarantee patient privacy while\noffering the ability to control the diversity of generations without increasing\nthe cost of generating such data. In contrast, we present a system for\ngenerating synthetic free-text medical records using Masked Language Modeling.\nThe system preserves critical medical information while introducing diversity\nin the generations and minimising re-identification risk. The system's size is\nabout 120M parameters, minimising inference cost. The results demonstrate\nhigh-quality synthetic data with a HIPAA-compliant PHI recall rate of 96% and a\nre-identification risk of 3.5%. Moreover, downstream evaluations show that the\ngenerated data can effectively train a model with performance comparable to\nreal data.\n", "date": "2024-09-15", "categories": []}
{"id": "2501.18435", "title": "GENIE: Generative Note Information Extraction model for structuring EHR\n  data", "abstract": "  Electronic Health Records (EHRs) hold immense potential for advancing\nhealthcare, offering rich, longitudinal data that combines structured\ninformation with valuable insights from unstructured clinical notes. However,\nthe unstructured nature of clinical text poses significant challenges for\nsecondary applications. Traditional methods for structuring EHR free-text data,\nsuch as rule-based systems and multi-stage pipelines, are often limited by\ntheir time-consuming configurations and inability to adapt across clinical\nnotes from diverse healthcare settings. Few systems provide a comprehensive\nattribute extraction for terminologies. While giant large language models\n(LLMs) like GPT-4 and LLaMA 405B excel at structuring tasks, they are slow,\ncostly, and impractical for large-scale use. To overcome these limitations, we\nintroduce GENIE, a Generative Note Information Extraction system that leverages\nLLMs to streamline the structuring of unstructured clinical text into usable\ndata with standardized format. GENIE processes entire paragraphs in a single\npass, extracting entities, assertion statuses, locations, modifiers, values,\nand purposes with high accuracy. Its unified, end-to-end approach simplifies\nworkflows, reduces errors, and eliminates the need for extensive manual\nintervention. Using a robust data preparation pipeline and fine-tuned small\nscale LLMs, GENIE achieves competitive performance across multiple information\nextraction tasks, outperforming traditional tools like cTAKES and MetaMap and\ncan handle extra attributes to be extracted. GENIE strongly enhances real-world\napplicability and scalability in healthcare systems. By open-sourcing the model\nand test data, we aim to encourage collaboration and drive further advancements\nin EHR structurization.\n", "date": "2025-01-30", "categories": []}
{"id": "2501.17286", "title": "Fine-Tuning Open-Source Large Language Models to Improve Their\n  Performance on Radiation Oncology Tasks: A Feasibility Study to Investigate\n  Their Potential Clinical Applications in Radiation Oncology", "abstract": "  Background: The radiation oncology clinical practice involves many steps\nrelying on the dynamic interplay of abundant text data. Large language models\nhave displayed remarkable capabilities in processing complex text information.\nBut their direct applications in specific fields like radiation oncology remain\nunderexplored.\n  Purpose: This study aims to investigate whether fine-tuning LLMs with domain\nknowledge can improve the performance on Task (1) treatment regimen generation,\nTask (2) treatment modality selection (photon, proton, electron, or\nbrachytherapy), and Task (3) ICD-10 code prediction in radiation oncology.\n  Methods: Data for 15,724 patient cases were extracted. Cases where patients\nhad a single diagnostic record, and a clearly identifiable primary treatment\nplan were selected for preprocessing and manual annotation to have 7,903 cases\nof the patient diagnosis, treatment plan, treatment modality, and ICD-10 code.\nEach case was used to construct a pair consisting of patient diagnostics\ndetails and an answer (treatment regimen, treatment modality, or ICD-10 code\nrespectively) for the supervised fine-tuning of these three tasks. Open source\nLLaMA2-7B and Mistral-7B models were utilized for the fine-tuning with the\nLow-Rank Approximations method. Accuracy and ROUGE-1 score were reported for\nthe fine-tuned models and original models. Clinical evaluation was performed on\nTask (1) by radiation oncologists, while precision, recall, and F-1 score were\nevaluated for Task (2) and (3). One-sided Wilcoxon signed-rank tests were used\nto statistically analyze the results.\n  Results: Fine-tuned LLMs outperformed original LLMs across all tasks with\np-value <= 0.001. Clinical evaluation demonstrated that over 60% of the\nfine-tuned LLMs-generated treatment regimens were clinically acceptable.\nPrecision, recall, and F1-score showed improved performance of fine-tuned LLMs.\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": "2409.13727", "title": "Classification performance and reproducibility of GPT-4 omni for\n  information extraction from veterinary electronic health records", "abstract": "  Large language models (LLMs) can extract information from veterinary\nelectronic health records (EHRs), but performance differences between models,\nthe effect of temperature settings, and the influence of text ambiguity have\nnot been previously evaluated. This study addresses these gaps by comparing the\nperformance of GPT-4 omni (GPT-4o) and GPT-3.5 Turbo under different conditions\nand investigating the relationship between human interobserver agreement and\nLLM errors. The LLMs and five humans were tasked with identifying six clinical\nsigns associated with Feline chronic enteropathy in 250 EHRs from a veterinary\nreferral hospital. At temperature 0, the performance of GPT-4o compared to the\nmajority opinion of human respondents, achieved 96.9% sensitivity\n(interquartile range [IQR] 92.9-99.3%), 97.6% specificity (IQR 96.5-98.5%),\n80.7% positive predictive value (IQR 70.8-84.6%), 99.5% negative predictive\nvalue (IQR 99.0-99.9%), 84.4% F1 score (IQR 77.3-90.4%), and 96.3% balanced\naccuracy (IQR 95.0-97.9%). The performance of GPT-4o was significantly better\nthan that of its predecessor, GPT-3.5 Turbo, particularly with respect to\nsensitivity where GPT-3.5 Turbo only achieved 81.7% (IQR 78.9-84.8%). Adjusting\nthe temperature for GPT-4o did not significantly impact classification\nperformance. GPT-4o demonstrated greater reproducibility than human pairs\nregardless of temperature, with an average Cohen's kappa of 0.98 (IQR\n0.98-0.99) at temperature 0 compared to 0.8 (IQR 0.78-0.81) for humans. Most\nGPT-4o errors occurred in instances where humans disagreed (35/43 errors,\n81.4%), suggesting that these errors were more likely caused by ambiguity of\nthe EHR than explicit model faults. Using GPT-4o to automate information\nextraction from veterinary EHRs is a viable alternative to manual extraction.\n", "date": "2024-09-09", "categories": []}
{"id": "2501.16672", "title": "VeriFact: Verifying Facts in LLM-Generated Clinical Text with Electronic\n  Health Records", "abstract": "  Methods to ensure factual accuracy of text generated by large language models\n(LLM) in clinical medicine are lacking. VeriFact is an artificial intelligence\nsystem that combines retrieval-augmented generation and LLM-as-a-Judge to\nverify whether LLM-generated text is factually supported by a patient's medical\nhistory based on their electronic health record (EHR). To evaluate this system,\nwe introduce VeriFact-BHC, a new dataset that decomposes Brief Hospital Course\nnarratives from discharge summaries into a set of simple statements with\nclinician annotations for whether each statement is supported by the patient's\nEHR clinical notes. Whereas highest agreement between clinicians was 88.5%,\nVeriFact achieves up to 92.7% agreement when compared to a denoised and\nadjudicated average human clinican ground truth, suggesting that VeriFact\nexceeds the average clinician's ability to fact-check text against a patient's\nmedical record. VeriFact may accelerate the development of LLM-based EHR\napplications by removing current evaluation bottlenecks.\n", "date": "2025-01-28", "categories": []}
{"id": "2311.00287", "title": "Knowledge-Infused Prompting: Assessing and Advancing Clinical Text Data\n  Generation with Large Language Models", "abstract": "  Clinical natural language processing requires methods that can address\ndomain-specific challenges, such as complex medical terminology and clinical\ncontexts. Recently, large language models (LLMs) have shown promise in this\ndomain. Yet, their direct deployment can lead to privacy issues and are\nconstrained by resources. To address this challenge, we delve into synthetic\nclinical text generation using LLMs for clinical NLP tasks. We propose an\ninnovative, resource-efficient approach, ClinGen, which infuses knowledge into\nthe process. Our model involves clinical knowledge extraction and\ncontext-informed LLM prompting. Both clinical topics and writing styles are\ndrawn from external domain-specific knowledge graphs and LLMs to guide data\ngeneration. Our extensive empirical study across 7 clinical NLP tasks and 16\ndatasets reveals that ClinGen consistently enhances performance across various\ntasks, effectively aligning the distribution of real datasets and significantly\nenriching the diversity of generated training instances. Our code is available\nat \\url{https://github.com/ritaranx/ClinGen}.\n", "date": "2023-11-01", "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": "2501.11199", "title": "Embedding-Driven Diversity Sampling to Improve Few-Shot Synthetic Data\n  Generation", "abstract": "  Accurate classification of clinical text often requires fine-tuning\npre-trained language models, a process that is costly and time-consuming due to\nthe need for high-quality data and expert annotators. Synthetic data generation\noffers an alternative, though pre-trained models may not capture the syntactic\ndiversity of clinical notes. We propose an embedding-driven approach that uses\ndiversity sampling from a small set of real clinical notes to guide large\nlanguage models in few-shot prompting, generating synthetic text that better\nreflects clinical syntax. We evaluated this method using the CheXpert dataset\non a classification task, comparing it to random few-shot and zero-shot\napproaches. Using cosine similarity and a Turing test, our approach produced\nsynthetic notes that more closely align with real clinical text. Our pipeline\nreduced the data needed to reach the 0.85 AUC cutoff by 40% for AUROC and 30%\nfor AUPRC, while augmenting models with synthetic data improved AUROC by 57%\nand AUPRC by 68%. Additionally, our synthetic data was 0.9 times as effective\nas real data, a 60% improvement in value.\n", "date": "2025-01-20", "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": "2501.14105", "title": "MedSlice: Fine-Tuned Large Language Models for Secure Clinical Note\n  Sectioning", "abstract": "  Extracting sections from clinical notes is crucial for downstream analysis\nbut is challenging due to variability in formatting and labor-intensive nature\nof manual sectioning. While proprietary large language models (LLMs) have shown\npromise, privacy concerns limit their accessibility. This study develops a\npipeline for automated note sectioning using open-source LLMs, focusing on\nthree sections: History of Present Illness, Interval History, and Assessment\nand Plan. We fine-tuned three open-source LLMs to extract sections using a\ncurated dataset of 487 progress notes, comparing results relative to\nproprietary models (GPT-4o, GPT-4o mini). Internal and external validity were\nassessed via precision, recall and F1 score. Fine-tuned Llama 3.1 8B\noutperformed GPT-4o (F1=0.92). On the external validity test set, performance\nremained high (F1= 0.85). Fine-tuned open-source LLMs can surpass proprietary\nmodels in clinical note sectioning, offering advantages in cost, performance,\nand accessibility.\n", "date": "2025-01-23", "categories": []}
{"id": "2405.03595", "title": "GREEN: Generative Radiology Report Evaluation and Error Notation", "abstract": "  Evaluating radiology reports is a challenging problem as factual correctness\nis extremely important due to the need for accurate medical communication about\nmedical images. Existing automatic evaluation metrics either suffer from\nfailing to consider factual correctness (e.g., BLEU and ROUGE) or are limited\nin their interpretability (e.g., F1CheXpert and F1RadGraph). In this paper, we\nintroduce GREEN (Generative Radiology Report Evaluation and Error Notation), a\nradiology report generation metric that leverages the natural language\nunderstanding of language models to identify and explain clinically significant\nerrors in candidate reports, both quantitatively and qualitatively. Compared to\ncurrent metrics, GREEN offers: 1) a score aligned with expert preferences, 2)\nhuman interpretable explanations of clinically significant errors, enabling\nfeedback loops with end-users, and 3) a lightweight open-source method that\nreaches the performance of commercial counterparts. We validate our GREEN\nmetric by comparing it to GPT-4, as well as to error counts of 6 experts and\npreferences of 2 experts. Our method demonstrates not only higher correlation\nwith expert error counts, but simultaneously higher alignment with expert\npreferences when compared to previous approaches.\n", "date": "2024-05-06", "categories": []}
{"id": "2501.08977", "title": "Development and Validation of the Provider Documentation Summarization\n  Quality Instrument for Large Language Models", "abstract": "  As Large Language Models (LLMs) are integrated into electronic health record\n(EHR) workflows, validated instruments are essential to evaluate their\nperformance before implementation. Existing instruments for provider\ndocumentation quality are often unsuitable for the complexities of\nLLM-generated text and lack validation on real-world data. The Provider\nDocumentation Summarization Quality Instrument (PDSQI-9) was developed to\nevaluate LLM-generated clinical summaries. Multi-document summaries were\ngenerated from real-world EHR data across multiple specialties using several\nLLMs (GPT-4o, Mixtral 8x7b, and Llama 3-8b). Validation included Pearson\ncorrelation for substantive validity, factor analysis and Cronbach's alpha for\nstructural validity, inter-rater reliability (ICC and Krippendorff's alpha) for\ngeneralizability, a semi-Delphi process for content validity, and comparisons\nof high-versus low-quality summaries for discriminant validity. Seven physician\nraters evaluated 779 summaries and answered 8,329 questions, achieving over 80%\npower for inter-rater reliability. The PDSQI-9 demonstrated strong internal\nconsistency (Cronbach's alpha = 0.879; 95% CI: 0.867-0.891) and high\ninter-rater reliability (ICC = 0.867; 95% CI: 0.867-0.868), supporting\nstructural validity and generalizability. Factor analysis identified a 4-factor\nmodel explaining 58% of the variance, representing organization, clarity,\naccuracy, and utility. Substantive validity was supported by correlations\nbetween note length and scores for Succinct (rho = -0.200, p = 0.029) and\nOrganized ($\\rho = -0.190$, $p = 0.037$). Discriminant validity distinguished\nhigh- from low-quality summaries ($p < 0.001$). The PDSQI-9 demonstrates robust\nconstruct validity, supporting its use in clinical practice to evaluate\nLLM-generated summaries and facilitate safer integration of LLMs into\nhealthcare workflows.\n", "date": "2025-01-15", "categories": []}
{"id": "2501.11114", "title": "Clinical trial cohort selection using Large Language Models on n2c2\n  Challenges", "abstract": "  Clinical trials are a critical process in the medical field for introducing\nnew treatments and innovations. However, cohort selection for clinical trials\nis a time-consuming process that often requires manual review of patient text\nrecords for specific keywords. Though there have been studies on standardizing\nthe information across the various platforms, Natural Language Processing (NLP)\ntools remain crucial for spotting eligibility criteria in textual reports.\nRecently, pre-trained large language models (LLMs) have gained popularity for\nvarious NLP tasks due to their ability to acquire a nuanced understanding of\ntext. In this paper, we study the performance of large language models on\nclinical trial cohort selection and leverage the n2c2 challenges to benchmark\ntheir performance. Our results are promising with regard to the incorporation\nof LLMs for simple cohort selection tasks, but also highlight the difficulties\nencountered by these models as soon as fine-grained knowledge and reasoning are\nrequired.\n", "date": "2025-01-19", "categories": []}
{"id": "2501.09744", "title": "KU AIGEN ICL EDI@BC8 Track 3: Advancing Phenotype Named Entity\n  Recognition and Normalization for Dysmorphology Physical Examination Reports", "abstract": "  The objective of BioCreative8 Track 3 is to extract phenotypic key medical\nfindings embedded within EHR texts and subsequently normalize these findings to\ntheir Human Phenotype Ontology (HPO) terms. However, the presence of diverse\nsurface forms in phenotypic findings makes it challenging to accurately\nnormalize them to the correct HPO terms. To address this challenge, we explored\nvarious models for named entity recognition and implemented data augmentation\ntechniques such as synonym marginalization to enhance the normalization step.\nOur pipeline resulted in an exact extraction and normalization F1 score 2.6\\%\nhigher than the mean score of all submissions received in response to the\nchallenge. Furthermore, in terms of the normalization F1 score, our approach\nsurpassed the average performance by 1.9\\%. These findings contribute to the\nadvancement of automated medical data extraction and normalization techniques,\nshowcasing potential pathways for future research and application in the\nbiomedical domain.\n", "date": "2025-01-16", "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": "2501.05479", "title": "Practical Design and Benchmarking of Generative AI Applications for\n  Surgical Billing and Coding", "abstract": "  Background: Healthcare has many manual processes that can benefit from\nautomation and augmentation with Generative Artificial Intelligence (AI), the\nmedical billing and coding process. However, current foundational Large\nLanguage Models (LLMs) perform poorly when tasked with generating accurate\nInternational Classification of Diseases, 10th edition, Clinical Modification\n(ICD-10-CM) and Current Procedural Terminology (CPT) codes. Additionally, there\nare many security and financial challenges in the application of generative AI\nto healthcare. We present a strategy for developing generative AI tools in\nhealthcare, specifically for medical billing and coding, that balances\naccuracy, accessibility, and patient privacy.\n  Methods: We fine tune the PHI-3 Mini and PHI-3 Medium LLMs using\ninstitutional data and compare the results against the PHI-3 base model, a\nPHI-3 RAG application, and GPT-4o. We use the post operative surgical report as\ninput and the patients billing claim the associated ICD-10, CPT, and Modifier\ncodes as the target result. Performance is measured by accuracy of code\ngeneration, proportion of invalid codes, and the fidelity of the billing claim\nformat.\n  Results: Both fine-tuned models performed better or as well as GPT-4o. The\nPhi-3 Medium fine-tuned model showed the best performance (ICD-10 Recall and\nPrecision: 72%, 72%; CPT Recall and Precision: 77%, 79%; Modifier Recall and\nPrecision: 63%, 64%). The Phi-3 Medium fine-tuned model only fabricated 1% of\nICD-10 codes and 0.6% of CPT codes generated.\n  Conclusions: Our study shows that a small model that is fine-tuned on\ndomain-specific data for specific tasks using a simple set of open-source tools\nand minimal technological and monetary requirements performs as well as the\nlarger contemporary consumer models.\n", "date": "2025-01-07", "categories": []}
{"id": "2410.19155", "title": "Lived Experience Not Found: LLMs Struggle to Align with Experts on\n  Addressing Adverse Drug Reactions from Psychiatric Medication Use", "abstract": "  Adverse Drug Reactions (ADRs) from psychiatric medications are the leading\ncause of hospitalizations among mental health patients. With healthcare systems\nand online communities facing limitations in resolving ADR-related issues,\nLarge Language Models (LLMs) have the potential to fill this gap. Despite the\nincreasing capabilities of LLMs, past research has not explored their\ncapabilities in detecting ADRs related to psychiatric medications or in\nproviding effective harm reduction strategies. To address this, we introduce\nthe Psych-ADR benchmark and the Adverse Drug Reaction Response Assessment\n(ADRA) framework to systematically evaluate LLM performance in detecting ADR\nexpressions and delivering expert-aligned mitigation strategies. Our analyses\nshow that LLMs struggle with understanding the nuances of ADRs and\ndifferentiating between types of ADRs. While LLMs align with experts in terms\nof expressed emotions and tone of the text, their responses are more complex,\nharder to read, and only 70.86% aligned with expert strategies. Furthermore,\nthey provide less actionable advice by a margin of 12.32% on average. Our work\nprovides a comprehensive benchmark and evaluation framework for assessing LLMs\nin strategy-driven tasks within high-risk domains.\n", "date": "2024-10-24", "categories": []}
{"id": "2411.10020", "title": "Information Extraction from Clinical Notes: Are We Ready to Switch to\n  Large Language Models?", "abstract": "  Backgrounds: Information extraction (IE) is critical in clinical natural\nlanguage processing (NLP). While large language models (LLMs) excel on\ngenerative tasks, their performance on extractive tasks remains debated.\nMethods: We investigated Named Entity Recognition (NER) and Relation Extraction\n(RE) using 1,588 clinical notes from four sources (UT Physicians, MTSamples,\nMIMIC-III, and i2b2). We developed an annotated corpus covering 4 clinical\nentities and 16 modifiers, and compared instruction-tuned LLaMA-2 and LLaMA-3\nagainst BERT in terms of performance, generalizability, computational\nresources, and throughput to BERT. Results: LLaMA models outperformed BERT\nacross datasets. With sufficient training data, LLaMA showed modest\nimprovements (1% on NER, 1.5-3.7% on RE); improvements were larger with limited\ntraining data. On unseen i2b2 data, LLaMA-3-70B outperformed BERT by 7% (F1) on\nNER and 4% on RE. However, LLaMA models required more computing resources and\nran up to 28 times slower. We implemented \"Kiwi,\" a clinical IE package\nfeaturing both models, available at https://kiwi.clinicalnlp.org/. Conclusion:\nThis study is among the first to develop and evaluate a comprehensive clinical\nIE system using open-source LLMs. Results indicate that LLaMA models outperform\nBERT for clinical NER and RE but with higher computational costs and lower\nthroughputs. These findings highlight that choosing between LLMs and\ntraditional deep learning methods for clinical IE applications should remain\ntask-specific, taking into account both performance metrics and practical\nconsiderations such as available computing resources and the intended use case\nscenarios.\n", "date": "2024-11-15", "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": "2412.19260", "title": "MEDEC: A Benchmark for Medical Error Detection and Correction in\n  Clinical Notes", "abstract": "  Several studies showed that Large Language Models (LLMs) can answer medical\nquestions correctly, even outperforming the average human score in some medical\nexams. However, to our knowledge, no study has been conducted to assess the\nability of language models to validate existing or generated medical text for\ncorrectness and consistency. In this paper, we introduce MEDEC\n(https://github.com/abachaa/MEDEC), the first publicly available benchmark for\nmedical error detection and correction in clinical notes, covering five types\nof errors (Diagnosis, Management, Treatment, Pharmacotherapy, and Causal\nOrganism). MEDEC consists of 3,848 clinical texts, including 488 clinical notes\nfrom three US hospital systems that were not previously seen by any LLM. The\ndataset has been used for the MEDIQA-CORR shared task to evaluate seventeen\nparticipating systems [Ben Abacha et al., 2024]. In this paper, we describe the\ndata creation methods and we evaluate recent LLMs (e.g., o1-preview, GPT-4,\nClaude 3.5 Sonnet, and Gemini 2.0 Flash) for the tasks of detecting and\ncorrecting medical errors requiring both medical knowledge and reasoning\ncapabilities. We also conducted a comparative study where two medical doctors\nperformed the same task on the MEDEC test set. The results showed that MEDEC is\na sufficiently challenging benchmark to assess the ability of models to\nvalidate existing or generated notes and to correct medical errors. We also\nfound that although recent LLMs have a good performance in error detection and\ncorrection, they are still outperformed by medical doctors in these tasks. We\ndiscuss the potential factors behind this gap, the insights from our\nexperiments, the limitations of current evaluation metrics, and share potential\npointers for future research.\n", "date": "2024-12-26", "categories": []}
{"id": "2501.00031", "title": "Distilling Large Language Models for Efficient Clinical Information\n  Extraction", "abstract": "  Large language models (LLMs) excel at clinical information extraction but\ntheir computational demands limit practical deployment. Knowledge\ndistillation--the process of transferring knowledge from larger to smaller\nmodels--offers a potential solution. We evaluate the performance of distilled\nBERT models, which are approximately 1,000 times smaller than modern LLMs, for\nclinical named entity recognition (NER) tasks. We leveraged state-of-the-art\nLLMs (Gemini and OpenAI models) and medical ontologies (RxNorm and SNOMED) as\nteacher labelers for medication, disease, and symptom extraction. We applied\nour approach to over 3,300 clinical notes spanning five publicly available\ndatasets, comparing distilled BERT models against both their teacher labelers\nand BERT models fine-tuned on human labels. External validation was conducted\nusing clinical notes from the MedAlign dataset. For disease extraction, F1\nscores were 0.82 (teacher model), 0.89 (BioBERT trained on human labels), and\n0.84 (BioBERT-distilled). For medication, F1 scores were 0.84 (teacher model),\n0.91 (BioBERT-human), and 0.87 (BioBERT-distilled). For symptoms: F1 score of\n0.73 (teacher model) and 0.68 (BioBERT-distilled). Distilled BERT models had\nfaster inference (12x, 4x, 8x faster than GPT-4o, o1-mini, and Gemini Flash\nrespectively) and lower costs (85x, 101x, 2x cheaper than GPT-4o, o1-mini, and\nGemini Flash respectively). On the external validation dataset, the distilled\nBERT model achieved F1 scores of 0.883 (medication), 0.726 (disease), and 0.699\n(symptom). Distilled BERT models were up to 101x cheaper and 12x faster than\nstate-of-the-art LLMs while achieving similar performance on NER tasks.\nDistillation offers a computationally efficient and scalable alternative to\nlarge LLMs for clinical information extraction.\n", "date": "2024-12-21", "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": "2501.00644", "title": "Efficient Standardization of Clinical Notes using Large Language Models", "abstract": "  Clinician notes are a rich source of patient information but often contain\ninconsistencies due to varied writing styles, colloquialisms, abbreviations,\nmedical jargon, grammatical errors, and non-standard formatting. These\ninconsistencies hinder the extraction of meaningful data from electronic health\nrecords (EHRs), posing challenges for quality improvement, population health,\nprecision medicine, decision support, and research.\n  We present a large language model approach to standardizing a corpus of 1,618\nclinical notes. Standardization corrected an average of $4.9 +/- 1.8$\ngrammatical errors, $3.3 +/- 5.2$ spelling errors, converted $3.1 +/- 3.0$\nnon-standard terms to standard terminology, and expanded $15.8 +/- 9.1$\nabbreviations and acronyms per note. Additionally, notes were re-organized into\ncanonical sections with standardized headings. This process prepared notes for\nkey concept extraction, mapping to medical ontologies, and conversion to\ninteroperable data formats such as FHIR.\n  Expert review of randomly sampled notes found no significant data loss after\nstandardization. This proof-of-concept study demonstrates that standardization\nof clinical notes can improve their readability, consistency, and usability,\nwhile also facilitating their conversion into interoperable data formats.\n", "date": "2024-12-31", "categories": []}
{"id": "2501.01195", "title": "Data Augmentation Techniques for Chinese Disease Name Normalization", "abstract": "  Disease name normalization is an important task in the medical domain. It\nclassifies disease names written in various formats into standardized names,\nserving as a fundamental component in smart healthcare systems for various\ndisease-related functions. Nevertheless, the most significant obstacle to\nexisting disease name normalization systems is the severe shortage of training\ndata. Consequently, we present a novel data augmentation approach that includes\na series of data augmentation techniques and some supporting modules to help\nmitigate the problem. Through extensive experimentation, we illustrate that our\nproposed approach exhibits significant performance improvements across various\nbaseline models and training objectives, particularly in scenarios with limited\ntraining data\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": "2406.10671", "title": "Augmenting Biomedical Named Entity Recognition with General-domain\n  Resources", "abstract": "  Training a neural network-based biomedical named entity recognition (BioNER)\nmodel usually requires extensive and costly human annotations. While several\nstudies have employed multi-task learning with multiple BioNER datasets to\nreduce human effort, this approach does not consistently yield performance\nimprovements and may introduce label ambiguity in different biomedical corpora.\nWe aim to tackle those challenges through transfer learning from easily\naccessible resources with fewer concept overlaps with biomedical datasets. We\nproposed GERBERA, a simple-yet-effective method that utilized general-domain\nNER datasets for training. We performed multi-task learning to train a\npre-trained biomedical language model with both the target BioNER dataset and\nthe general-domain dataset. Subsequently, we fine-tuned the models specifically\nfor the BioNER dataset. We systematically evaluated GERBERA on five datasets of\neight entity types, collectively consisting of 81,410 instances. Despite using\nfewer biomedical resources, our models demonstrated superior performance\ncompared to baseline models trained with additional BioNER datasets.\nSpecifically, our models consistently outperformed the baseline models in six\nout of eight entity types, achieving an average improvement of 0.9% over the\nbest baseline performance across eight entities. Our method was especially\neffective in amplifying performance on BioNER datasets characterized by limited\ndata, with a 4.7% improvement in F1 scores on the JNLPBA-RNA dataset. This\nstudy introduces a new training method that leverages cost-effective\ngeneral-domain NER datasets to augment BioNER models. This approach\nsignificantly improves BioNER model performance, making it a valuable asset for\nscenarios with scarce or costly biomedical datasets.\n", "date": "2024-06-15", "categories": []}
{"id": "2406.16341", "title": "EHRCon: Dataset for Checking Consistency between Unstructured Notes and\n  Structured Tables in Electronic Health Records", "abstract": "  Electronic Health Records (EHRs) are integral for storing comprehensive\npatient medical records, combining structured data (e.g., medications) with\ndetailed clinical notes (e.g., physician notes). These elements are essential\nfor straightforward data retrieval and provide deep, contextual insights into\npatient care. However, they often suffer from discrepancies due to unintuitive\nEHR system designs and human errors, posing serious risks to patient safety. To\naddress this, we developed EHRCon, a new dataset and task specifically designed\nto ensure data consistency between structured tables and unstructured notes in\nEHRs. EHRCon was crafted in collaboration with healthcare professionals using\nthe MIMIC-III EHR dataset, and includes manual annotations of 4,101 entities\nacross 105 clinical notes checked against database entries for consistency.\nEHRCon has two versions, one using the original MIMIC-III schema, and another\nusing the OMOP CDM schema, in order to increase its applicability and\ngeneralizability. Furthermore, leveraging the capabilities of large language\nmodels, we introduce CheckEHR, a novel framework for verifying the consistency\nbetween clinical notes and database tables. CheckEHR utilizes an eight-stage\nprocess and shows promising results in both few-shot and zero-shot settings.\nThe code is available at https://github.com/dustn1259/EHRCon.\n", "date": "2024-06-24", "categories": []}
{"id": "2409.19467", "title": "INSIGHTBUDDY-AI: Medication Extraction and Entity Linking using Large\n  Language Models and Ensemble Learning", "abstract": "  Medication Extraction and Mining play an important role in healthcare NLP\nresearch due to its practical applications in hospital settings, such as their\nmapping into standard clinical knowledge bases (SNOMED-CT, BNF, etc.). In this\nwork, we investigate state-of-the-art LLMs in text mining tasks on medications\nand their related attributes such as dosage, route, strength, and adverse\neffects. In addition, we explore different ensemble learning methods\n(\\textsc{Stack-Ensemble} and \\textsc{Voting-Ensemble}) to augment the model\nperformances from individual LLMs. Our ensemble learning result demonstrated\nbetter performances than individually fine-tuned base models BERT, RoBERTa,\nRoBERTa-L, BioBERT, BioClinicalBERT, BioMedRoBERTa, ClinicalBERT, and\nPubMedBERT across general and specific domains. Finally, we build up an entity\nlinking function to map extracted medical terminologies into the SNOMED-CT\ncodes and the British National Formulary (BNF) codes, which are further mapped\nto the Dictionary of Medicines and Devices (dm+d), and ICD. Our model's toolkit\nand desktop applications are publicly available (at\n\\url{https://github.com/HECTA-UoM/ensemble-NER}).\n", "date": "2024-09-28", "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": "2310.11275", "title": "xMEN: A Modular Toolkit for Cross-Lingual Medical Entity Normalization", "abstract": "  Objective: To improve performance of medical entity normalization across many\nlanguages, especially when fewer language resources are available compared to\nEnglish.\n  Materials and Methods: We introduce xMEN, a modular system for cross-lingual\nmedical entity normalization, which performs well in both low- and\nhigh-resource scenarios. When synonyms in the target language are scarce for a\ngiven terminology, we leverage English aliases via cross-lingual candidate\ngeneration. For candidate ranking, we incorporate a trainable cross-encoder\nmodel if annotations for the target task are available. We also evaluate\ncross-encoders trained in a weakly supervised manner based on\nmachine-translated datasets from a high resource domain. Our system is publicly\navailable as an extensible Python toolkit.\n  Results: xMEN improves the state-of-the-art performance across a wide range\nof multilingual benchmark datasets. Weakly supervised cross-encoders are\neffective when no training data is available for the target task. Through the\ncompatibility of xMEN with the BigBIO framework, it can be easily used with\nexisting and prospective datasets.\n  Discussion: Our experiments show the importance of balancing the output of\ngeneral-purpose candidate generators with subsequent trainable re-rankers,\nwhich we achieve through a rank regularization term in the loss function of the\ncross-encoder. However, error analysis reveals that multi-word expressions and\nother complex entities are still challenging.\n  Conclusion: xMEN exhibits strong performance for medical entity normalization\nin multiple languages, even when no labeled data and few terminology aliases\nfor the target language are available. Its configuration system and evaluation\nmodules enable reproducible benchmarks. Models and code are available online at\nthe following URL: https://github.com/hpi-dhc/xmen\n", "date": "2023-10-17", "categories": []}
{"id": "2412.19070", "title": "Cross-Demographic Portability of Deep NLP-Based Depression Models", "abstract": "  Deep learning models are rapidly gaining interest for real-world applications\nin behavioral health. An important gap in current literature is how well such\nmodels generalize over different populations. We study Natural Language\nProcessing (NLP) based models to explore portability over two different corpora\nhighly mismatched in age. The first and larger corpus contains younger\nspeakers. It is used to train an NLP model to predict depression. When testing\non unseen speakers from the same age distribution, this model performs at\nAUC=0.82. We then test this model on the second corpus, which comprises seniors\nfrom a retirement community. Despite the large demographic differences in the\ntwo corpora, we saw only modest degradation in performance for the\nsenior-corpus data, achieving AUC=0.76. Interestingly, in the senior\npopulation, we find AUC=0.81 for the subset of patients whose health state is\nconsistent over time. Implications for demographic portability of speech-based\napplications are discussed.\n", "date": "2024-12-26", "categories": []}
{"id": "2412.19072", "title": "Robust Speech and Natural Language Processing Models for Depression\n  Screening", "abstract": "  Depression is a global health concern with a critical need for increased\npatient screening. Speech technology offers advantages for remote screening but\nmust perform robustly across patients. We have described two deep learning\nmodels developed for this purpose. One model is based on acoustics; the other\nis based on natural language processing. Both models employ transfer learning.\nData from a depression-labeled corpus in which 11,000 unique users interacted\nwith a human-machine application using conversational speech is used. Results\non binary depression classification have shown that both models perform at or\nabove AUC=0.80 on unseen data with no speaker overlap. Performance is further\nanalyzed as a function of test subset characteristics, finding that the models\nare generally robust over speaker and session variables. We conclude that\nmodels based on these approaches offer promise for generalized automated\ndepression screening.\n", "date": "2024-12-26", "categories": []}
{"id": "2412.19346", "title": "Semi-Supervised Learning from Small Annotated Data and Large Unlabeled\n  Data for Fine-grained PICO Entity Recognition", "abstract": "  Objective: Extracting PICO elements -- Participants, Intervention,\nComparison, and Outcomes -- from clinical trial literature is essential for\nclinical evidence retrieval, appraisal, and synthesis. Existing approaches do\nnot distinguish the attributes of PICO entities. This study aims to develop a\nnamed entity recognition (NER) model to extract PICO entities with fine\ngranularities.\n  Materials and Methods: Using a corpus of 2,511 abstracts with PICO mentions\nfrom 4 public datasets, we developed a semi-supervised method to facilitate the\ntraining of a NER model, FinePICO, by combining limited annotated data of PICO\nentities and abundant unlabeled data. For evaluation, we divided the entire\ndataset into two subsets: a smaller group with annotations and a larger group\nwithout annotations. We then established the theoretical lower and upper\nperformance bounds based on the performance of supervised learning models\ntrained solely on the small, annotated subset and on the entire set with\ncomplete annotations, respectively. Finally, we evaluated FinePICO on both the\nsmaller annotated subset and the larger, initially unannotated subset. We\nmeasured the performance of FinePICO using precision, recall, and F1.\n  Results: Our method achieved precision/recall/F1 of 0.567/0.636/0.60,\nrespectively, using a small set of annotated samples, outperforming the\nbaseline model (F1: 0.437) by more than 16\\%. The model demonstrates\ngeneralizability to a different PICO framework and to another corpus, which\nconsistently outperforms the benchmark in diverse experimental settings\n(p-value \\textless0.001).\n  Conclusion: This study contributes a generalizable and effective\nsemi-supervised approach to named entity recognition leveraging large unlabeled\ndata together with small, annotated data. It also initially supports\nfine-grained PICO extraction.\n", "date": "2024-12-26", "categories": []}
{"id": "2412.16291", "title": "Benchmarking LLMs and SLMs for patient reported outcomes", "abstract": "  LLMs have transformed the execution of numerous tasks, including those in the\nmedical domain. Among these, summarizing patient-reported outcomes (PROs) into\nconcise natural language reports is of particular interest to clinicians, as it\nenables them to focus on critical patient concerns and spend more time in\nmeaningful discussions. While existing work with LLMs like GPT-4 has shown\nimpressive results, real breakthroughs could arise from leveraging SLMs as they\noffer the advantage of being deployable locally, ensuring patient data privacy\nand compliance with healthcare regulations. This study benchmarks several SLMs\nagainst LLMs for summarizing patient-reported Q\\&A forms in the context of\nradiotherapy. Using various metrics, we evaluate their precision and\nreliability. The findings highlight both the promise and limitations of SLMs\nfor high-stakes medical tasks, fostering more efficient and privacy-preserving\nAI-driven healthcare solutions.\n", "date": "2024-12-20", "categories": []}
{"id": "2412.16341", "title": "A Machine Learning Approach for Emergency Detection in Medical Scenarios\n  Using Large Language Models", "abstract": "  The rapid identification of medical emergencies through digital communication\nchannels remains a critical challenge in modern healthcare delivery,\nparticularly with the increasing prevalence of telemedicine. This paper\npresents a novel approach leveraging large language models (LLMs) and prompt\nengineering techniques for automated emergency detection in medical\ncommunications. We developed and evaluated a comprehensive system using\nmultiple LLaMA model variants (1B, 3B, and 7B parameters) to classify medical\nscenarios as emergency or non-emergency situations. Our methodology\nincorporated both system prompts and in-prompt training approaches, evaluated\nacross different hardware configurations. The results demonstrate exceptional\nperformance, with the LLaMA 2 (7B) model achieving 99.7% accuracy and the LLaMA\n3.2 (3B) model reaching 99.6% accuracy with optimal prompt engineering. Through\nsystematic testing of training examples within the prompts, we identified that\nincluding 10 example scenarios in the model prompts yielded optimal\nclassification performance. Processing speeds varied significantly between\nplatforms, ranging from 0.05 to 2.2 seconds per request. The system showed\nparticular strength in minimizing high-risk false negatives in emergency\nscenarios, which is crucial for patient safety. The code implementation and\nevaluation framework are publicly available on GitHub, facilitating further\nresearch and development in this crucial area of healthcare technology.\n", "date": "2024-12-20", "categories": []}
{"id": "2412.16423", "title": "Technical Report: Small Language Model for Japanese Clinical and\n  Medicine", "abstract": "  This report presents a small language model (SLM) for Japanese clinical and\nmedicine, named NCVC-slm-1. This 1B parameters model was trained using Japanese\ntext classified to be of high-quality. Moreover, NCVC-slm-1 was augmented with\nrespect to clinical and medicine content that includes the variety of diseases,\ndrugs, and examinations. Using a carefully designed pre-processing, a\nspecialized morphological analyzer and tokenizer, this small and light-weight\nmodel performed not only to generate text but also indicated the feasibility of\nunderstanding clinical and medicine text. In comparison to other large language\nmodels, a fine-tuning NCVC-slm-1 demonstrated the highest scores on 6 tasks of\ntotal 8 on JMED-LLM. According to this result, SLM indicated the feasibility of\nperforming several downstream tasks in the field of clinical and medicine.\nHopefully, NCVC-slm-1 will be contributed to develop and accelerate the field\nof clinical and medicine for a bright future.\n", "date": "2024-12-21", "categories": []}
{"id": "2412.17449", "title": "Applying LLM and Topic Modelling in Psychotherapeutic Contexts", "abstract": "  This study explores the use of Large language models to analyze therapist\nremarks in a psychotherapeutic setting. The paper focuses on the application of\nBERTopic, a machine learning-based topic modeling tool, to the dialogue of two\ndifferent groups of therapists (classical and modern), which makes it possible\nto identify and describe a set of topics that consistently emerge across these\ngroups. The paper describes in detail the chosen algorithm for BERTopic, which\nincluded creating a vector space from a corpus of therapist remarks, reducing\nits dimensionality, clustering the space, and creating and optimizing topic\nrepresentation. Along with the automatic topical modeling by the BERTopic, the\nresearch involved an expert assessment of the findings and manual topic\nstructure optimization. The topic modeling results highlighted the most common\nand stable topics in therapists speech, offering insights into how language\npatterns in therapy develop and remain stable across different therapeutic\nstyles. This work contributes to the growing field of machine learning in\npsychotherapy by demonstrating the potential of automated methods to improve\nboth the practice and training of therapists. The study highlights the value of\ntopic modeling as a tool for gaining a deeper understanding of therapeutic\ndialogue and offers new opportunities for improving therapeutic effectiveness\nand clinical supervision.\n", "date": "2024-12-23", "categories": []}
{"id": "2410.20695", "title": "Combining Domain-Specific Models and LLMs for Automated Disease\n  Phenotyping from Survey Data", "abstract": "  This exploratory pilot study investigated the potential of combining a\ndomain-specific model, BERN2, with large language models (LLMs) to enhance\nautomated disease phenotyping from research survey data. Motivated by the need\nfor efficient and accurate methods to harmonize the growing volume of survey\ndata with standardized disease ontologies, we employed BERN2, a biomedical\nnamed entity recognition and normalization model, to extract disease\ninformation from the ORIGINS birth cohort survey data. After rigorously\nevaluating BERN2's performance against a manually curated ground truth dataset,\nwe integrated various LLMs using prompt engineering, Retrieval-Augmented\nGeneration (RAG), and Instructional Fine-Tuning (IFT) to refine the model's\noutputs. BERN2 demonstrated high performance in extracting and normalizing\ndisease mentions, and the integration of LLMs, particularly with Few Shot\nInference and RAG orchestration, further improved accuracy. This approach,\nespecially when incorporating structured examples, logical reasoning prompts,\nand detailed context, offers a promising avenue for developing tools to enable\nefficient cohort profiling and data harmonization across large, heterogeneous\nresearch datasets.\n", "date": "2024-10-28", "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": "2412.15360", "title": "Decade of Natural Language Processing in Chronic Pain: A Systematic\n  Review", "abstract": "  In recent years, the intersection of Natural Language Processing (NLP) and\npublic health has opened innovative pathways for investigating various domains,\nincluding chronic pain in textual datasets. Despite the promise of NLP in\nchronic pain, the literature is dispersed across various disciplines, and there\nis a need to consolidate existing knowledge, identify knowledge gaps in the\nliterature, and inform future research directions in this emerging field. This\nreview aims to investigate the state of the research on NLP-based interventions\ndesigned for chronic pain research. A search strategy was formulated and\nexecuted across PubMed, Web of Science, IEEE Xplore, Scopus, and ACL Anthology\nto find studies published in English between 2014 and 2024. After screening 132\npapers, 26 studies were included in the final review. Key findings from this\nreview underscore the significant potential of NLP techniques to address\npressing challenges in chronic pain research. The past 10 years in this field\nhave showcased the utilization of advanced methods (transformers like RoBERTa\nand BERT) achieving high-performance metrics (e.g., F1>0.8) in classification\ntasks, while unsupervised approaches like Latent Dirichlet Allocation (LDA) and\nk-means clustering have proven effective for exploratory analyses. Results also\nreveal persistent challenges such as limited dataset diversity, inadequate\nsample sizes, and insufficient representation of underrepresented populations.\nFuture research studies should explore multimodal data validation systems,\ncontext-aware mechanistic modeling, and the development of standardized\nevaluation metrics to enhance reproducibility and equity in chronic pain\nresearch.\n", "date": "2024-12-19", "categories": []}
{"id": "2412.15510", "title": "ADEQA: A Question Answer based approach for joint ADE-Suspect Extraction\n  using Sequence-To-Sequence Transformers", "abstract": "  Early identification of Adverse Drug Events (ADE) is critical for taking\nprompt actions while introducing new drugs into the market. These ADEs\ninformation are available through various unstructured data sources like\nclinical study reports, patient health records, social media posts, etc.\nExtracting ADEs and the related suspect drugs using machine learning is a\nchallenging task due to the complex linguistic relations between drug ADE pairs\nin textual data and unavailability of large corpus of labelled datasets. This\npaper introduces ADEQA, a question-answer(QA) based approach using quasi\nsupervised labelled data and sequence-to-sequence transformers to extract ADEs,\ndrug suspects and the relationships between them. Unlike traditional QA models,\nnatural language generation (NLG) based models don't require extensive token\nlevel labelling and thereby reduces the adoption barrier significantly. On a\npublic ADE corpus, we were able to achieve state-of-the-art results with an F1\nscore of 94% on establishing the relationships between ADEs and the respective\nsuspects.\n", "date": "2024-12-20", "categories": []}
{"id": "2412.15772", "title": "Linguistic Features Extracted by GPT-4 Improve Alzheimer's Disease\n  Detection based on Spontaneous Speech", "abstract": "  Alzheimer's Disease (AD) is a significant and growing public health concern.\nInvestigating alterations in speech and language patterns offers a promising\npath towards cost-effective and non-invasive early detection of AD on a large\nscale. Large language models (LLMs), such as GPT, have enabled powerful new\npossibilities for semantic text analysis. In this study, we leverage GPT-4 to\nextract five semantic features from transcripts of spontaneous patient speech.\nThe features capture known symptoms of AD, but they are difficult to quantify\neffectively using traditional methods of computational linguistics. We\ndemonstrate the clinical significance of these features and further validate\none of them (\"Word-Finding Difficulties\") against a proxy measure and human\nraters. When combined with established linguistic features and a Random Forest\nclassifier, the GPT-derived features significantly improve the detection of AD.\nOur approach proves effective for both manually transcribed and automatically\ngenerated transcripts, representing a novel and impactful use of recent\nadvancements in LLMs for AD speech analysis.\n", "date": "2024-12-20", "categories": []}
{"id": "2403.13369", "title": "Clinical information extraction for Low-resource languages with Few-shot\n  learning using Pre-trained language models and Prompting", "abstract": "  Automatic extraction of medical information from clinical documents poses\nseveral challenges: high costs of required clinical expertise, limited\ninterpretability of model predictions, restricted computational resources and\nprivacy regulations. Recent advances in domain-adaptation and prompting methods\nshowed promising results with minimal training data using lightweight masked\nlanguage models, which are suited for well-established interpretability\nmethods. We are first to present a systematic evaluation of these methods in a\nlow-resource setting, by performing multi-class section classification on\nGerman doctor's letters. We conduct extensive class-wise evaluations supported\nby Shapley values, to validate the quality of our small training data set and\nto ensure the interpretability of model predictions. We demonstrate that a\nlightweight, domain-adapted pretrained model, prompted with just 20 shots,\noutperforms a traditional classification model by 30.5% accuracy. Our results\nserve as a process-oriented guideline for clinical information extraction\nprojects working with low-resource.\n", "date": "2024-03-20", "categories": []}
{"id": "2403.01002", "title": "Attribute Structuring Improves LLM-Based Evaluation of Clinical Text\n  Summaries", "abstract": "  Summarizing clinical text is crucial in health decision-support and clinical\nresearch. Large language models (LLMs) have shown the potential to generate\naccurate clinical text summaries, but still struggle with issues regarding\ngrounding and evaluation, especially in safety-critical domains such as health.\nHolistically evaluating text summaries is challenging because they may contain\nunsubstantiated information. Here, we explore a general mitigation framework\nusing Attribute Structuring (AS), which structures the summary evaluation\nprocess. It decomposes the evaluation process into a grounded procedure that\nuses an LLM for relatively simple structuring and scoring tasks, rather than\nthe full task of holistic summary evaluation. Experiments show that AS\nconsistently improves the correspondence between human annotations and\nautomated metrics in clinical text summarization. Additionally, AS yields\ninterpretations in the form of a short text span corresponding to each output,\nwhich enables efficient human auditing, paving the way towards trustworthy\nevaluation of clinical information in resource-constrained scenarios. We\nrelease our code, prompts, and an open-source benchmark at\nhttps://github.com/microsoft/attribute-structuring.\n", "date": "2024-03-01", "categories": []}
{"id": "2408.10039", "title": "MSDiagnosis: A Benchmark for Evaluating Large Language Models in\n  Multi-Step Clinical Diagnosis", "abstract": "  Clinical diagnosis is critical in medical practice, typically requiring a\ncontinuous and evolving process that includes primary diagnosis, differential\ndiagnosis, and final diagnosis. However, most existing clinical diagnostic\ntasks are single-step processes, which does not align with the complex\nmulti-step diagnostic procedures found in real-world clinical settings. In this\npaper, we propose a Chinese clinical diagnostic benchmark, called MSDiagnosis.\nThis benchmark consists of 2,225 cases from 12 departments, covering tasks such\nas primary diagnosis, differential diagnosis, and final diagnosis.\nAdditionally, we propose a novel and effective framework. This framework\ncombines forward inference, backward inference, reflection, and refinement,\nenabling the large language model to self-evaluate and adjust its diagnostic\nresults. To this end, we test open-source models, closed-source models, and our\nproposed framework.The experimental results demonstrate the effectiveness of\nthe proposed method. We also provide a comprehensive experimental analysis and\nsuggest future research directions for this task.\n", "date": "2024-08-19", "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": "2412.10918", "title": "LLMs-in-the-Loop Part 2: Expert Small AI Models for Anonymization and\n  De-identification of PHI Across Multiple Languages", "abstract": "  The rise of chronic diseases and pandemics like COVID-19 has emphasized the\nneed for effective patient data processing while ensuring privacy through\nanonymization and de-identification of protected health information (PHI).\nAnonymized data facilitates research without compromising patient\nconfidentiality. This paper introduces expert small AI models developed using\nthe LLM-in-the-loop methodology to meet the demand for domain-specific\nde-identification NER models. These models overcome the privacy risks\nassociated with large language models (LLMs) used via APIs by eliminating the\nneed to transmit or store sensitive data. More importantly, they consistently\noutperform LLMs in de-identification tasks, offering superior performance and\nreliability. Our de-identification NER models, developed in eight languages\n(English, German, Italian, French, Romanian, Turkish, Spanish, and Arabic)\nachieved f1-micro score averages of 0.966, 0.975, 0.976, 0.970, 0.964, 0.974,\n0.978, and 0.953 respectively. These results establish them as the most\naccurate healthcare anonymization solutions, surpassing existing small models\nand even general-purpose LLMs such as GPT-4o. While Part-1 of this series\nintroduced the LLM-in-the-loop methodology for bio-medical document\ntranslation, this second paper showcases its success in developing\ncost-effective expert small NER models in de-identification tasks. Our findings\nlay the groundwork for future healthcare AI innovations, including biomedical\nentity and relation extraction, demonstrating the value of specialized models\nfor domain-specific challenges.\n", "date": "2024-12-14", "categories": []}
{"id": "2412.11472", "title": "Leveraging Foundation Language Models (FLMs) for Automated Cohort\n  Extraction from Large EHR Databases", "abstract": "  A crucial step in cohort studies is to extract the required cohort from one\nor more study datasets. This step is time-consuming, especially when a\nresearcher is presented with a dataset that they have not previously worked\nwith. When the cohort has to be extracted from multiple datasets, cohort\nextraction can be extremely laborious. In this study, we present an approach\nfor partially automating cohort extraction from multiple electronic health\nrecord (EHR) databases. We formulate the guided multi-dataset cohort extraction\nproblem in which selection criteria are first converted into queries,\ntranslating them from natural language text to language that maps to database\nentities. Then, using FLMs, columns of interest identified from the queries are\nautomatically matched between the study databases. Finally, the generated\nqueries are run across all databases to extract the study cohort. We propose\nand evaluate an algorithm for automating column matching on two large, popular\nand publicly-accessible EHR databases -- MIMIC-III and eICU. Our approach\nachieves a high top-three accuracy of $92\\%$, correctly matching $12$ out of\nthe $13$ columns of interest, when using a small, pre-trained general purpose\nlanguage model. Furthermore, this accuracy is maintained even as the search\nspace (i.e., size of the database) increases.\n", "date": "2024-12-16", "categories": []}
{"id": "2412.11477", "title": "NoteContrast: Contrastive Language-Diagnostic Pretraining for Medical\n  Text", "abstract": "  Accurate diagnostic coding of medical notes is crucial for enhancing patient\ncare, medical research, and error-free billing in healthcare organizations.\nManual coding is a time-consuming task for providers, and diagnostic codes\noften exhibit low sensitivity and specificity, whereas the free text in medical\nnotes can be a more precise description of a patients status. Thus, accurate\nautomated diagnostic coding of medical notes has become critical for a learning\nhealthcare system. Recent developments in long-document transformer\narchitectures have enabled attention-based deep-learning models to adjudicate\nmedical notes. In addition, contrastive loss functions have been used to\njointly pre-train large language and image models with noisy labels. To further\nimprove the automated adjudication of medical notes, we developed an approach\nbased on i) models for ICD-10 diagnostic code sequences using a large\nreal-world data set, ii) large language models for medical notes, and iii)\ncontrastive pre-training to build an integrated model of both ICD-10 diagnostic\ncodes and corresponding medical text. We demonstrate that a contrastive\napproach for pre-training improves performance over prior state-of-the-art\nmodels for the MIMIC-III-50, MIMIC-III-rare50, and MIMIC-III-full diagnostic\ncoding tasks.\n", "date": "2024-12-16", "categories": []}
{"id": "2412.11671", "title": "BioBridge: Unified Bio-Embedding with Bridging Modality in Code-Switched\n  EMR", "abstract": "  Pediatric Emergency Department (PED) overcrowding presents a significant\nglobal challenge, prompting the need for efficient solutions. This paper\nintroduces the BioBridge framework, a novel approach that applies Natural\nLanguage Processing (NLP) to Electronic Medical Records (EMRs) in written\nfree-text form to enhance decision-making in PED. In non-English speaking\ncountries, such as South Korea, EMR data is often written in a Code-Switching\n(CS) format that mixes the native language with English, with most\ncode-switched English words having clinical significance. The BioBridge\nframework consists of two core modules: \"bridging modality in context\" and\n\"unified bio-embedding.\" The \"bridging modality in context\" module improves the\ncontextual understanding of bilingual and code-switched EMRs. In the \"unified\nbio-embedding\" module, the knowledge of the model trained in the medical domain\nis injected into the encoder-based model to bridge the gap between the medical\nand general domains. Experimental results demonstrate that the proposed\nBioBridge significantly performance traditional machine learning and\npre-trained encoder-based models on several metrics, including F1 score, area\nunder the receiver operating characteristic curve (AUROC), area under the\nprecision-recall curve (AUPRC), and Brier score. Specifically, BioBridge-XLM\nachieved enhancements of 0.85% in F1 score, 0.75% in AUROC, and 0.76% in AUPRC,\nalong with a notable 3.04% decrease in the Brier score, demonstrating marked\nimprovements in accuracy, reliability, and prediction calibration over the\nbaseline XLM model. The source code will be made publicly available.\n", "date": "2024-12-16", "categories": []}
{"id": "2412.12001", "title": "LLM-RG4: Flexible and Factual Radiology Report Generation across Diverse\n  Input Contexts", "abstract": "  Drafting radiology reports is a complex task requiring flexibility, where\nradiologists tail content to available information and particular clinical\ndemands. However, most current radiology report generation (RRG) models are\nconstrained to a fixed task paradigm, such as predicting the full ``finding''\nsection from a single image, inherently involving a mismatch between inputs and\noutputs. The trained models lack the flexibility for diverse inputs and could\ngenerate harmful, input-agnostic hallucinations. To bridge the gap between\ncurrent RRG models and the clinical demands in practice, we first develop a\ndata generation pipeline to create a new MIMIC-RG4 dataset, which considers\nfour common radiology report drafting scenarios and has perfectly corresponded\ninput and output. Secondly, we propose a novel large language model (LLM) based\nRRG framework, namely LLM-RG4, which utilizes LLM's flexible\ninstruction-following capabilities and extensive general knowledge. We further\ndevelop an adaptive token fusion module that offers flexibility to handle\ndiverse scenarios with different input combinations, while minimizing the\nadditional computational burden associated with increased input volumes.\nBesides, we propose a token-level loss weighting strategy to direct the model's\nattention towards positive and uncertain descriptions. Experimental results\ndemonstrate that LLM-RG4 achieves state-of-the-art performance in both clinical\nefficiency and natural language generation on the MIMIC-RG4 and MIMIC-CXR\ndatasets. We quantitatively demonstrate that our model has minimal\ninput-agnostic hallucinations, whereas current open-source models commonly\nsuffer from this problem.\n", "date": "2024-12-16", "categories": []}
{"id": "2310.16995", "title": "TOP-Training: Target-Oriented Pretraining for Medical Extractive\n  Question Answering", "abstract": "  We study extractive question-answering in the medical domain (Medical-EQA).\nThis problem has two main challenges: (i) domain specificity, as most AI models\nlack necessary domain knowledge, and (ii) extraction-based answering style,\nwhich restricts most autoregressive LLMs due to potential hallucinations. To\nhandle those challenges, we propose TOP-Training, a target-oriented\npre-training paradigm that stands out among all domain adaptation techniques\nwith two desirable features: (i) TOP-Training moves one step further than\npopular domain-oriented fine-tuning since it not only moves closer to the\ntarget domain, but also familiarizes itself with the target dataset, and (ii)\nit does not assume the existence of a large set of unlabeled instances from the\ntarget domain. Specifically, for a target Medical-EQA dataset, we extract its\nentities and leverage large language models (LLMs) to generate synthetic texts\ncontaining those entities; we then demonstrate that pretraining on this\nsynthetic text data yields better performance on the target Medical-EQA\nbenchmarks. Overall, our contributions are threefold: (i) TOP-Training, a new\npretraining technique to effectively adapt LLMs to better solve a target\nproblem, (ii) TOP-Training has a wide application scope because it does not\nrequire the target problem to have a large set of unlabeled data, and (iii) our\nexperiments highlight the limitations of autoregressive LLMs, emphasizing\nTOP-Training as a means to unlock the true potential of bidirectional LLMs.\n", "date": "2023-10-25", "categories": []}
{"id": "2412.08900", "title": "AI-assisted Knowledge Discovery in Biomedical Literature to Support\n  Decision-making in Precision Oncology", "abstract": "  The delivery of appropriate targeted therapies to cancer patients requires\nthe complete analysis of the molecular profiling of tumors and the patient's\nclinical characteristics in the context of existing knowledge and recent\nfindings described in biomedical literature and several other sources. We\nevaluated the potential contributions of specific natural language processing\nsolutions to support knowledge discovery from biomedical literature. Two models\nfrom the Bidirectional Encoder Representations from Transformers (BERT) family,\ntwo Large Language Models, and PubTator 3.0 were tested for their ability to\nsupport the named entity recognition (NER) and the relation extraction (RE)\ntasks. PubTator 3.0 and the BioBERT model performed best in the NER task (best\nF1-score equal to 0.93 and 0.89, respectively), while BioBERT outperformed all\nother solutions in the RE task (best F1-score 0.79) and a specific use case it\nwas applied to by recognizing nearly all entity mentions and most of the\nrelations.\n", "date": "2024-12-12", "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": "2412.05533", "title": "Can large language models be privacy preserving and fair medical coders?", "abstract": "  Protecting patient data privacy is a critical concern when deploying machine\nlearning algorithms in healthcare. Differential privacy (DP) is a common method\nfor preserving privacy in such settings and, in this work, we examine two key\ntrade-offs in applying DP to the NLP task of medical coding (ICD\nclassification). Regarding the privacy-utility trade-off, we observe a\nsignificant performance drop in the privacy preserving models, with more than a\n40% reduction in micro F1 scores on the top 50 labels in the MIMIC-III dataset.\nFrom the perspective of the privacy-fairness trade-off, we also observe an\nincrease of over 3% in the recall gap between male and female patients in the\nDP models. Further understanding these trade-offs will help towards the\nchallenges of real-world deployment.\n", "date": "2024-12-07", "categories": []}
{"id": "2412.05845", "title": "Are Clinical T5 Models Better for Clinical Text?", "abstract": "  Large language models with a transformer-based encoder/decoder architecture,\nsuch as T5, have become standard platforms for supervised tasks. To bring these\ntechnologies to the clinical domain, recent work has trained new or adapted\nexisting models to clinical data. However, the evaluation of these clinical T5\nmodels and comparison to other models has been limited. Are the clinical T5\nmodels better choices than FLAN-tuned generic T5 models? Do they generalize\nbetter to new clinical domains that differ from the training sets? We\ncomprehensively evaluate these models across several clinical tasks and\ndomains. We find that clinical T5 models provide marginal improvements over\nexisting models, and perform worse when evaluated on different domains. Our\nresults inform future choices in developing clinical LLMs.\n", "date": "2024-12-08", "categories": []}
{"id": "2412.05861", "title": "Depression detection from Social Media Bangla Text Using Recurrent\n  Neural Networks", "abstract": "  Emotion artificial intelligence is a field of study that focuses on figuring\nout how to recognize emotions, especially in the area of text mining. Today is\nthe age of social media which has opened a door for us to share our individual\nexpressions, emotions, and perspectives on any event. We can analyze sentiment\non social media posts to detect positive, negative, or emotional behavior\ntoward society. One of the key challenges in sentiment analysis is to identify\ndepressed text from social media text that is a root cause of mental\nill-health. Furthermore, depression leads to severe impairment in day-to-day\nliving and is a major source of suicide incidents. In this paper, we apply\nnatural language processing techniques on Facebook texts for conducting emotion\nanalysis focusing on depression using multiple machine learning algorithms.\nPreprocessing steps like stemming, stop word removal, etc. are used to clean\nthe collected data, and feature extraction techniques like stylometric feature,\nTF-IDF, word embedding, etc. are applied to the collected dataset which\nconsists of 983 texts collected from social media posts. In the process of\nclass prediction, LSTM, GRU, support vector machine, and Naive-Bayes\nclassifiers have been used. We have presented the results using the primary\nclassification metrics including F1-score, and accuracy. This work focuses on\ndepression detection from social media posts to help psychologists to analyze\nsentiment from shared posts which may reduce the undesirable behaviors of\ndepressed individuals through diagnosis and treatment.\n", "date": "2024-12-08", "categories": []}
{"id": "2412.04512", "title": "Prompting Large Language Models for Clinical Temporal Relation\n  Extraction", "abstract": "  Objective: This paper aims to prompt large language models (LLMs) for\nclinical temporal relation extraction (CTRE) in both few-shot and fully\nsupervised settings. Materials and Methods: This study utilizes four LLMs:\nEncoder-based GatorTron-Base (345M)/Large (8.9B); Decoder-based\nLLaMA3-8B/MeLLaMA-13B. We developed full (FFT) and parameter-efficient (PEFT)\nfine-tuning strategies and evaluated these strategies on the 2012 i2b2 CTRE\ntask. We explored four fine-tuning strategies for GatorTron-Base: (1) Standard\nFine-Tuning, (2) Hard-Prompting with Unfrozen LLMs, (3) Soft-Prompting with\nFrozen LLMs, and (4) Low-Rank Adaptation (LoRA) with Frozen LLMs. For\nGatorTron-Large, we assessed two PEFT strategies-Soft-Prompting and LoRA with\nFrozen LLMs-leveraging Quantization techniques. Additionally, LLaMA3-8B and\nMeLLaMA-13B employed two PEFT strategies: LoRA strategy with Quantization\n(QLoRA) applied to Frozen LLMs using instruction tuning and standard\nfine-tuning. Results: Under fully supervised settings, Hard-Prompting with\nUnfrozen GatorTron-Base achieved the highest F1 score (89.54%), surpassing the\nSOTA model (85.70%) by 3.74%. Additionally, two variants of QLoRA adapted to\nGatorTron-Large and Standard Fine-Tuning of GatorTron-Base exceeded the SOTA\nmodel by 2.36%, 1.88%, and 0.25%, respectively. Decoder-based models with\nfrozen parameters outperformed their Encoder-based counterparts in this\nsetting; however, the trend reversed in few-shot scenarios. Discussions and\nConclusions: This study presented new methods that significantly improved CTRE\nperformance, benefiting downstream tasks reliant on CTRE systems. The findings\nunderscore the importance of selecting appropriate models and fine-tuning\nstrategies based on task requirements and data availability. Future work will\nexplore larger models and broader CTRE applications.\n", "date": "2024-12-04", "categories": []}
{"id": "2412.04573", "title": "Give me Some Hard Questions: Synthetic Data Generation for Clinical QA", "abstract": "  Clinical Question Answering (QA) systems enable doctors to quickly access\npatient information from electronic health records (EHRs). However, training\nthese systems requires significant annotated data, which is limited due to the\nexpertise needed and the privacy concerns associated with clinical data. This\npaper explores generating Clinical QA data using large language models (LLMs)\nin a zero-shot setting. We find that naive prompting often results in easy\nquestions that do not reflect the complexity of clinical scenarios. To address\nthis, we propose two prompting strategies: 1) instructing the model to generate\nquestions that do not overlap with the input context, and 2) summarizing the\ninput record using a predefined schema to scaffold question generation.\nExperiments on two Clinical QA datasets demonstrate that our method generates\nmore challenging questions, significantly improving fine-tuning performance\nover baselines. We compare synthetic and gold data and find a gap between their\ntraining efficacy resulting from the quality of synthetically generated\nanswers.\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": "2412.01955", "title": "The use of large language models to enhance cancer clinical trial\n  educational materials", "abstract": "  Cancer clinical trials often face challenges in recruitment and engagement\ndue to a lack of participant-facing informational and educational resources.\nThis study investigated the potential of Large Language Models (LLMs),\nspecifically GPT4, in generating patient-friendly educational content from\nclinical trial informed consent forms. Using data from ClinicalTrials.gov, we\nemployed zero-shot learning for creating trial summaries and one-shot learning\nfor developing multiple-choice questions, evaluating their effectiveness\nthrough patient surveys and crowdsourced annotation. Results showed that\nGPT4-generated summaries were both readable and comprehensive, and may improve\npatients' understanding and interest in clinical trials. The multiple-choice\nquestions demonstrated high accuracy and agreement with crowdsourced\nannotators. For both resource types, hallucinations were identified that\nrequire ongoing human oversight. The findings demonstrate the potential of LLMs\n\"out-of-the-box\" to support the generation of clinical trial education\nmaterials with minimal trial-specific engineering, but implementation with a\nhuman-in-the-loop is still needed to avoid misinformation risks.\n", "date": "2024-12-02", "categories": []}
{"id": "2412.03176", "title": "Automatic detection of diseases in Spanish clinical notes combining\n  medical language models and ontologies", "abstract": "  In this paper we present a hybrid method for the automatic detection of\ndermatological pathologies in medical reports. We use a large language model\ncombined with medical ontologies to predict, given a first appointment or\nfollow-up medical report, the pathology a person may suffer from. The results\nshow that teaching the model to learn the type, severity and location on the\nbody of a dermatological pathology, as well as in which order it has to learn\nthese three features, significantly increases its accuracy. The article\npresents the demonstration of state-of-the-art results for classification of\nmedical texts with a precision of 0.84, micro and macro F1-score of 0.82 and\n0.75, and makes both the method and the data set used available to the\ncommunity.\n", "date": "2024-12-04", "categories": []}
{"id": "2402.16886", "title": "Using text embedding models as text classifiers with medical data", "abstract": "  The advent of Large Language Models (LLMs) is promising and LLMs have been\napplied to numerous fields. However, it is not trivial to implement LLMs in the\nmedical field, due to the high standards for precision and accuracy. Currently,\nthe diagnosis of medical ailments must be done by hand, as it is costly to\nbuild a sufficiently broad LLM that can diagnose a wide range of diseases.\nHere, we explore the use of vector databases and embedding models as a means of\nencoding and classifying text with medical text data without the need to train\na new model altogether. We used various LLMs to generate the medical data, then\nencoded the data with a text embedding model and stored it in a vector\ndatabase. We hypothesized that higher embedding dimensions coupled with\ndescriptive data in the vector database would lead to better classifications\nand designed a robustness test to test our hypothesis. By using vector\ndatabases and text embedding models to classify a clinician's notes on a\npatient presenting with a certain ailment, we showed that these tools can be\nsuccessful at classifying medical text data. We found that a higher embedding\ndimension did indeed yield better results, however, querying with simple data\nin the database was optimal for performance. We have shown in this study the\napplicability of text embedding models and vector databases on a small scale,\nand our work lays the groundwork for applying these tools on a larger scale.\n", "date": "2024-02-07", "categories": []}
{"id": "2404.10503", "title": "A Sentiment Analysis of Medical Text Based on Deep Learning", "abstract": "  The field of natural language processing (NLP) has made significant progress\nwith the rapid development of deep learning technologies. One of the research\ndirections in text sentiment analysis is sentiment analysis of medical texts,\nwhich holds great potential for application in clinical diagnosis. However, the\nmedical field currently lacks sufficient text datasets, and the effectiveness\nof sentiment analysis is greatly impacted by different model design approaches,\nwhich presents challenges. Therefore, this paper focuses on the medical domain,\nusing bidirectional encoder representations from transformers (BERT) as the\nbasic pre-trained model and experimenting with modules such as convolutional\nneural network (CNN), fully connected network (FCN), and graph convolutional\nnetworks (GCN) at the output layer. Experiments and analyses were conducted on\nthe METS-CoV dataset to explore the training performance after integrating\ndifferent deep learning networks. The results indicate that CNN models\noutperform other networks when trained on smaller medical text datasets in\ncombination with pre-trained models like BERT. This study highlights the\nsignificance of model selection in achieving effective sentiment analysis in\nthe medical domain and provides a reference for future research to develop more\nefficient model architectures.\n", "date": "2024-04-16", "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": "2407.04125", "title": "Query-Guided Self-Supervised Summarization of Nursing Notes", "abstract": "  Nursing notes, an important part of Electronic Health Records (EHRs), track a\npatient's health during a care episode. Summarizing key information in nursing\nnotes can help clinicians quickly understand patients' conditions. However,\nexisting summarization methods in the clinical setting, especially abstractive\nmethods, have overlooked nursing notes and require reference summaries for\ntraining. We introduce QGSumm, a novel query-guided self-supervised domain\nadaptation approach for abstractive nursing note summarization. The method uses\npatient-related clinical queries for guidance, and hence does not need\nreference summaries for training. Through automatic experiments and manual\nevaluation by an expert clinician, we study our approach and other\nstate-of-the-art Large Language Models (LLMs) for nursing note summarization.\nOur experiments show: 1) GPT-4 is competitive in maintaining information in the\noriginal nursing notes, 2) QGSumm can generate high-quality summaries with a\ngood balance between recall of the original content and hallucination rate\nlower than other top methods. Ultimately, our work offers a new perspective on\nconditional text summarization, tailored to clinical applications.\n", "date": "2024-07-04", "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": "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": "2411.18922", "title": "Devising a Set of Compact and Explainable Spoken Language Feature for\n  Screening Alzheimer's Disease", "abstract": "  Alzheimer's disease (AD) has become one of the most significant health\nchallenges in an aging society. The use of spoken language-based AD detection\nmethods has gained prevalence due to their scalability due to their\nscalability. Based on the Cookie Theft picture description task, we devised an\nexplainable and effective feature set that leverages the visual capabilities of\na large language model (LLM) and the Term Frequency-Inverse Document Frequency\n(TF-IDF) model. Our experimental results show that the newly proposed features\nconsistently outperform traditional linguistic features across two different\nclassifiers with high dimension efficiency. Our new features can be well\nexplained and interpreted step by step which enhance the interpretability of\nautomatic AD screening.\n", "date": "2024-11-28", "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": "2405.02040", "title": "Large Multimodal Model based Standardisation of Pathology Reports with\n  Confidence and their Prognostic Significance", "abstract": "  Pathology reports are rich in clinical and pathological details but are often\npresented in free-text format. The unstructured nature of these reports\npresents a significant challenge limiting the accessibility of their content.\nIn this work, we present a practical approach based on the use of large\nmultimodal models (LMMs) for automatically extracting information from scanned\nimages of pathology reports with the goal of generating a standardised report\nspecifying the value of different fields along with estimated confidence about\nthe accuracy of the extracted fields. The proposed approach overcomes\nlimitations of existing methods which do not assign confidence scores to\nextracted fields limiting their practical use. The proposed framework uses two\nstages of prompting a Large Multimodal Model (LMM) for information extraction\nand validation. The framework generalises to textual reports from multiple\nmedical centres as well as scanned images of legacy pathology reports. We show\nthat the estimated confidence is an effective indicator of the accuracy of the\nextracted information that can be used to select only accurately extracted\nfields. We also show the prognostic significance of structured and unstructured\ndata from pathology reports and show that the automatically extracted field\nvalues significant prognostic value for patient stratification. The framework\nis available for evaluation via the URL: https://labieb.dcs.warwick.ac.uk/.\n", "date": "2024-05-03", "categories": []}
{"id": "2405.18015", "title": "MultiADE: A Multi-domain Benchmark for Adverse Drug Event Extraction", "abstract": "  Active adverse event surveillance monitors Adverse Drug Events (ADE) from\ndifferent data sources, such as electronic health records, medical literature,\nsocial media and search engine logs. Over the years, many datasets have been\ncreated, and shared tasks have been organised to facilitate active adverse\nevent surveillance. However, most - if not all - datasets or shared tasks focus\non extracting ADEs from a particular type of text. Domain generalisation - the\nability of a machine learning model to perform well on new, unseen domains\n(text types) - is under-explored. Given the rapid advancements in natural\nlanguage processing, one unanswered question is how far we are from having a\nsingle ADE extraction model that is effective on various types of text, such as\nscientific literature and social media posts. We contribute to answering this\nquestion by building a multi-domain benchmark for adverse drug event\nextraction, which we named MultiADE. The new benchmark comprises several\nexisting datasets sampled from different text types and our newly created\ndataset - CADECv2, which is an extension of CADEC, covering online posts\nregarding more diverse drugs than CADEC. Our new dataset is carefully annotated\nby human annotators following detailed annotation guidelines. Our benchmark\nresults show that the generalisation of the trained models is far from perfect,\nmaking it infeasible to be deployed to process different types of text. In\naddition, although intermediate transfer learning is a promising approach to\nutilising existing resources, further investigation is needed on methods of\ndomain adaptation, particularly cost-effective methods to select useful\ntraining instances. The newly created CADECv2 and the scripts for building the\nbenchmark are publicly available at CSIRO's Data Portal.\n", "date": "2024-05-28", "categories": []}
{"id": "2411.15491", "title": "Traditional Chinese Medicine Case Analysis System for High-Level\n  Semantic Abstraction: Optimized with Prompt and RAG", "abstract": "  This paper details a technical plan for building a clinical case database for\nTraditional Chinese Medicine (TCM) using web scraping. Leveraging multiple\nplatforms, including 360doc, we gathered over 5,000 TCM clinical cases,\nperformed data cleaning, and structured the dataset with crucial fields such as\npatient details, pathogenesis, syndromes, and annotations. Using the\n$Baidu\\_ERNIE\\_Speed\\_128K$ API, we removed redundant information and generated\nthe final answers through the $DeepSeekv2$ API, outputting results in standard\nJSON format. We optimized data recall with RAG and rerank techniques during\nretrieval and developed a hybrid matching scheme. By combining two-stage\nretrieval method with keyword matching via Jieba, we significantly enhanced the\naccuracy of model outputs.\n", "date": "2024-11-23", "categories": []}
{"id": "2411.15666", "title": "Ontology-Constrained Generation of Domain-Specific Clinical Summaries", "abstract": "  Large Language Models (LLMs) offer promising solutions for text\nsummarization. However, some domains require specific information to be\navailable in the summaries. Generating these domain-adapted summaries is still\nan open challenge. Similarly, hallucinations in generated content is a major\ndrawback of current approaches, preventing their deployment. This study\nproposes a novel approach that leverages ontologies to create domain-adapted\nsummaries both structured and unstructured. We employ an ontology-guided\nconstrained decoding process to reduce hallucinations while improving\nrelevance. When applied to the medical domain, our method shows potential in\nsummarizing Electronic Health Records (EHRs) across different specialties,\nallowing doctors to focus on the most relevant information to their domain.\nEvaluation on the MIMIC-III dataset demonstrates improvements in generating\ndomain-adapted summaries of clinical notes and hallucination reduction.\n", "date": "2024-11-23", "categories": []}
{"id": "2411.14456", "title": "Can Artificial Intelligence Generate Quality Research Topics Reflecting\n  Patient Concerns?", "abstract": "  Patient-centered research is increasingly important in narrowing the gap\nbetween research and patient care, yet incorporating patient perspectives into\nhealth research has been inconsistent. We propose an automated framework\nleveraging innovative natural language processing (NLP) and artificial\nintelligence (AI) with patient portal messages to generate research ideas that\nprioritize important patient issues. We further quantified the quality of\nAI-generated research topics. To define patient clinical concerns, we analyzed\n614,464 patient messages from 25,549 individuals with breast or skin cancer\nobtained from a large academic hospital (2013 to 2024), constructing a 2-staged\nunsupervised NLP topic model. Then, we generated research topics to resolve the\ndefined issues using a widely used AI (ChatGPT-4o, OpenAI Inc, April 2024\nversion) with prompt-engineering strategies. We guided AI to perform\nmulti-level tasks: 1) knowledge interpretation and summarization (e.g.,\ninterpreting and summarizing the NLP-defined topics), 2) knowledge generation\n(e.g., generating research ideas corresponding to patients issues), 3)\nself-reflection and correction (e.g., ensuring and revising the research ideas\nafter searching for scientific articles), and 4) self-reassurance (e.g.,\nconfirming and finalizing the research ideas). Six highly experienced breast\noncologists and dermatologists assessed the significance and novelty of\nAI-generated research topics using a 5-point Likert scale (1-exceptional,\n5-poor). One-third of the AI-suggested research topics were highly significant\nand novel when both scores were lower than the average. Two-thirds of the\nAI-suggested topics were novel in both cancers. Our findings demonstrate that\nAI-generated research topics reflecting patient perspectives via a large volume\nof patient messages can meaningfully guide future directions in\npatient-centered health research.\n", "date": "2024-11-15", "categories": []}
{"id": "2411.13604", "title": "RadPhi-3: Small Language Models for Radiology", "abstract": "  LLM based copilot assistants are useful in everyday tasks. There is a\nproliferation in the exploration of AI assistant use cases to support radiology\nworkflows in a reliable manner. In this work, we present RadPhi-3, a Small\nLanguage Model instruction tuned from Phi-3-mini-4k-instruct with 3.8B\nparameters to assist with various tasks in radiology workflows. While\nimpression summary generation has been the primary task which has been explored\nin prior works w.r.t radiology reports of Chest X-rays, we also explore other\nuseful tasks like change summary generation comparing the current radiology\nreport and its prior report, section extraction from radiology reports, tagging\nthe reports with various pathologies and tubes, lines or devices present in\nthem etc. In-addition, instruction tuning RadPhi-3 involved learning from a\ncredible knowledge source used by radiologists, Radiopaedia.org. RadPhi-3 can\nbe used both to give reliable answers for radiology related queries as well as\nperform useful tasks related to radiology reports. RadPhi-3 achieves SOTA\nresults on the RaLEs radiology report generation benchmark.\n", "date": "2024-11-19", "categories": []}
{"id": "2411.13518", "title": "Advancing Complex Medical Communication in Arabic with Sporo AraSum:\n  Surpassing Existing Large Language Models", "abstract": "  The increasing demand for multilingual capabilities in healthcare underscores\nthe need for AI models adept at processing diverse languages, particularly in\nclinical documentation and decision-making. Arabic, with its complex\nmorphology, syntax, and diglossia, poses unique challenges for natural language\nprocessing (NLP) in medical contexts. This case study evaluates Sporo AraSum, a\nlanguage model tailored for Arabic clinical documentation, against JAIS, the\nleading Arabic NLP model. Using synthetic datasets and modified PDQI-9 metrics\nmodified ourselves for the purposes of assessing model performances in a\ndifferent language. The study assessed the models' performance in summarizing\npatient-physician interactions, focusing on accuracy, comprehensiveness,\nclinical utility, and linguistic-cultural competence.\n  Results indicate that Sporo AraSum significantly outperforms JAIS in\nAI-centric quantitative metrics and all qualitative attributes measured in our\nmodified version of the PDQI-9. AraSum's architecture enables precise and\nculturally sensitive documentation, addressing the linguistic nuances of Arabic\nwhile mitigating risks of AI hallucinations. These findings suggest that Sporo\nAraSum is better suited to meet the demands of Arabic-speaking healthcare\nenvironments, offering a transformative solution for multilingual clinical\nworkflows. Future research should incorporate real-world data to further\nvalidate these findings and explore broader integration into healthcare\nsystems.\n", "date": "2024-11-20", "categories": []}
{"id": "2302.04725", "title": "Lightweight Transformers for Clinical Natural Language Processing", "abstract": "  Specialised pre-trained language models are becoming more frequent in NLP\nsince they can potentially outperform models trained on generic texts. BioBERT\nand BioClinicalBERT are two examples of such models that have shown promise in\nmedical NLP tasks. Many of these models are overparametrised and\nresource-intensive, but thanks to techniques like Knowledge Distillation (KD),\nit is possible to create smaller versions that perform almost as well as their\nlarger counterparts. In this work, we specifically focus on development of\ncompact language models for processing clinical texts (i.e. progress notes,\ndischarge summaries etc). We developed a number of efficient lightweight\nclinical transformers using knowledge distillation and continual learning, with\nthe number of parameters ranging from 15 million to 65 million. These models\nperformed comparably to larger models such as BioBERT and ClinicalBioBERT and\nsignificantly outperformed other compact models trained on general or\nbiomedical data. Our extensive evaluation was done across several standard\ndatasets and covered a wide range of clinical text-mining tasks, including\nNatural Language Inference, Relation Extraction, Named Entity Recognition, and\nSequence Classification. To our knowledge, this is the first comprehensive\nstudy specifically focused on creating efficient and compact transformers for\nclinical NLP tasks. The models and code used in this study can be found on our\nHuggingface profile at https://huggingface.co/nlpie and Github page at\nhttps://github.com/nlpie-research/Lightweight-Clinical-Transformers,\nrespectively, promoting reproducibility of our results.\n", "date": "2023-02-09", "categories": []}
{"id": "2411.12712", "title": "Enhancing Multi-Class Disease Classification: Neoplasms, Cardiovascular,\n  Nervous System, and Digestive Disorders Using Advanced LLMs", "abstract": "  In this research, we explored the improvement in terms of multi-class disease\nclassification via pre-trained language models over Medical-Abstracts-TC-Corpus\nthat spans five medical conditions. We excluded non-cancer conditions and\nexamined four specific diseases. We assessed four LLMs, BioBERT, XLNet, and\nBERT, as well as a novel base model (Last-BERT). BioBERT, which was pre-trained\non medical data, demonstrated superior performance in medical text\nclassification (97% accuracy). Surprisingly, XLNet followed closely (96%\naccuracy), demonstrating its generalizability across domains even though it was\nnot pre-trained on medical data. LastBERT, a custom model based on the lighter\nversion of BERT, also proved competitive with 87.10% accuracy (just under\nBERT's 89.33%). Our findings confirm the importance of specialized models such\nas BioBERT and also support impressions around more general solutions like\nXLNet and well-tuned transformer architectures with fewer parameters (in this\ncase, LastBERT) in medical domain tasks.\n", "date": "2024-11-19", "categories": []}
{"id": "2208.02554", "title": "Vocabulary Transfer for Biomedical Texts: Add Tokens if You Can Not Add\n  Data", "abstract": "  Working within specific NLP subdomains presents significant challenges,\nprimarily due to a persistent deficit of data. Stringent privacy concerns and\nlimited data accessibility often drive this shortage. Additionally, the medical\ndomain demands high accuracy, where even marginal improvements in model\nperformance can have profound impacts. In this study, we investigate the\npotential of vocabulary transfer to enhance model performance in biomedical NLP\ntasks. Specifically, we focus on vocabulary extension, a technique that\ninvolves expanding the target vocabulary to incorporate domain-specific\nbiomedical terms. Our findings demonstrate that vocabulary extension, leads to\nmeasurable improvements in both downstream model performance and inference\ntime.\n", "date": "2022-08-04", "categories": []}
{"id": "2411.10761", "title": "Can Generic LLMs Help Analyze Child-adult Interactions Involving\n  Children with Autism in Clinical Observation?", "abstract": "  Large Language Models (LLMs) have shown significant potential in\nunderstanding human communication and interaction. However, their performance\nin the domain of child-inclusive interactions, including in clinical settings,\nremains less explored. In this work, we evaluate generic LLMs' ability to\nanalyze child-adult dyadic interactions in a clinically relevant context\ninvolving children with ASD. Specifically, we explore LLMs in performing four\ntasks: classifying child-adult utterances, predicting engaged activities,\nrecognizing language skills and understanding traits that are clinically\nrelevant. Our evaluation shows that generic LLMs are highly capable of\nanalyzing long and complex conversations in clinical observation sessions,\noften surpassing the performance of non-expert human evaluators. The results\nshow their potential to segment interactions of interest, assist in language\nskills evaluation, identify engaged activities, and offer clinical-relevant\ncontext for assessments.\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": "2406.18027", "title": "Automated Clinical Data Extraction with Knowledge Conditioned LLMs", "abstract": "  The extraction of lung lesion information from clinical and medical imaging\nreports is crucial for research on and clinical care of lung-related diseases.\nLarge language models (LLMs) can be effective at interpreting unstructured text\nin reports, but they often hallucinate due to a lack of domain-specific\nknowledge, leading to reduced accuracy and posing challenges for use in\nclinical settings. To address this, we propose a novel framework that aligns\ngenerated internal knowledge with external knowledge through in-context\nlearning (ICL). Our framework employs a retriever to identify relevant units of\ninternal or external knowledge and a grader to evaluate the truthfulness and\nhelpfulness of the retrieved internal-knowledge rules, to align and update the\nknowledge bases. Experiments with expert-curated test datasets demonstrate that\nthis ICL approach can increase the F1 score for key fields (lesion size, margin\nand solidity) by an average of 12.9% over existing ICL methods.\n", "date": "2024-06-26", "categories": []}
{"id": "2407.05250", "title": "CLIMB: A Benchmark of Clinical Bias in Large Language Models", "abstract": "  Large language models (LLMs) are increasingly applied to clinical\ndecision-making. However, their potential to exhibit bias poses significant\nrisks to clinical equity. Currently, there is a lack of benchmarks that\nsystematically evaluate such clinical bias in LLMs. While in downstream tasks,\nsome biases of LLMs can be avoided such as by instructing the model to answer\n\"I'm not sure...\", the internal bias hidden within the model still lacks deep\nstudies. We introduce CLIMB (shorthand for A Benchmark of Clinical Bias in\nLarge Language Models), a pioneering comprehensive benchmark to evaluate both\nintrinsic (within LLMs) and extrinsic (on downstream tasks) bias in LLMs for\nclinical decision tasks. Notably, for intrinsic bias, we introduce a novel\nmetric, AssocMAD, to assess the disparities of LLMs across multiple demographic\ngroups. Additionally, we leverage counterfactual intervention to evaluate\nextrinsic bias in a task of clinical diagnosis prediction. Our experiments\nacross popular and medically adapted LLMs, particularly from the Mistral and\nLLaMA families, unveil prevalent behaviors with both intrinsic and extrinsic\nbias. This work underscores the critical need to mitigate clinical bias and\nsets a new standard for future evaluations of LLMs' clinical bias.\n", "date": "2024-07-07", "categories": []}
{"id": "2411.09933", "title": "JRadiEvo: A Japanese Radiology Report Generation Model Enhanced by\n  Evolutionary Optimization of Model Merging", "abstract": "  With the rapid advancement of large language models (LLMs), foundational\nmodels (FMs) have seen significant advancements. Healthcare is one of the most\ncrucial application areas for these FMs, given the significant time and effort\nrequired for physicians to analyze large volumes of patient data. Recent\nefforts have focused on adapting multimodal FMs to the medical domain through\ntechniques like instruction-tuning, leading to the development of medical\nfoundation models (MFMs). However, these approaches typically require large\namounts of training data to effectively adapt models to the medical field.\nMoreover, most existing models are trained on English datasets, limiting their\npracticality in non-English-speaking regions where healthcare professionals and\npatients are not always fluent in English. The need for translation introduces\nadditional costs and inefficiencies. To address these challenges, we propose a\n\\textbf{J}apanese \\textbf{Radi}ology report generation model enhanced by\n\\textbf{Evo}lutionary optimization of model merging (JRadiEvo). This is the\nfirst attempt to extend a non-medical vision-language foundation model to the\nmedical domain through evolutionary optimization of model merging. We\nsuccessfully created a model that generates accurate Japanese reports from\nX-ray images using only 50 translated samples from publicly available data.\nThis model, developed with highly efficient use of limited data, outperformed\nleading models from recent research trained on much larger datasets.\nAdditionally, with only 8 billion parameters, this relatively compact\nfoundation model can be deployed locally within hospitals, making it a\npractical solution for environments where APIs and other external services\ncannot be used due to strict privacy and security requirements.\n", "date": "2024-11-15", "categories": []}
{"id": "2402.12298", "title": "Is Open-Source There Yet? A Comparative Study on Commercial and\n  Open-Source LLMs in Their Ability to Label Chest X-Ray Reports", "abstract": "  Introduction: With the rapid advances in large language models (LLMs), there\nhave been numerous new open source as well as commercial models. While recent\npublications have explored GPT-4 in its application to extracting information\nof interest from radiology reports, there has not been a real-world comparison\nof GPT-4 to different leading open-source models.\n  Materials and Methods: Two different and independent datasets were used. The\nfirst dataset consists of 540 chest x-ray reports that were created at the\nMassachusetts General Hospital between July 2019 and July 2021. The second\ndataset consists of 500 chest x-ray reports from the ImaGenome dataset. We then\ncompared the commercial models GPT-3.5 Turbo and GPT-4 from OpenAI to the\nopen-source models Mistral-7B, Mixtral-8x7B, Llama2-13B, Llama2-70B,\nQWEN1.5-72B and CheXbert and CheXpert-labeler in their ability to accurately\nlabel the presence of multiple findings in x-ray text reports using different\nprompting techniques.\n  Results: On the ImaGenome dataset, the best performing open-source model was\nLlama2-70B with micro F1-scores of 0.972 and 0.970 for zero- and few-shot\nprompts, respectively. GPT-4 achieved micro F1-scores of 0.975 and 0.984,\nrespectively. On the institutional dataset, the best performing open-source\nmodel was QWEN1.5-72B with micro F1-scores of 0.952 and 0.965 for zero- and\nfew-shot prompting, respectively. GPT-4 achieved micro F1-scores of 0.975 and\n0.973, respectively.\n  Conclusion: In this paper, we show that while GPT-4 is superior to\nopen-source models in zero-shot report labeling, the implementation of few-shot\nprompting can bring open-source models on par with GPT-4. This shows that\nopen-source models could be a performant and privacy preserving alternative to\nGPT-4 for the task of radiology report classification.\n", "date": "2024-02-19", "categories": []}
{"id": "2411.08586", "title": "Optimizing Automatic Summarization of Long Clinical Records Using\n  Dynamic Context Extension:Testing and Evaluation of the NBCE Method", "abstract": "  Summarizing patient clinical notes is vital for reducing documentation\nburdens. Current manual summarization makes medical staff struggle. We propose\nan automatic method using LLMs, but long inputs cause LLMs to lose context,\nreducing output quality especially in small size model. We used a 7B model,\nopen-calm-7b, enhanced with Native Bayes Context Extend and a redesigned\ndecoding mechanism to reference one sentence at a time, keeping inputs within\ncontext windows, 2048 tokens. Our improved model achieved near parity with\nGoogle's over 175B Gemini on ROUGE-L metrics with 200 samples, indicating\nstrong performance using less resources, enhancing automated EMR summarization\nfeasibility.\n", "date": "2024-11-13", "categories": []}
{"id": "2410.09729", "title": "MIRAGE: Multimodal Identification and Recognition of Annotations in\n  Indian General Prescriptions", "abstract": "  Hospitals in India still rely on handwritten medical records despite the\navailability of Electronic Medical Records (EMR), complicating statistical\nanalysis and record retrieval. Handwritten records pose a unique challenge,\nrequiring specialized data for training models to recognize medications and\ntheir recommendation patterns. While traditional handwriting recognition\napproaches employ 2-D LSTMs, recent studies have explored using Multimodal\nLarge Language Models (MLLMs) for OCR tasks. Building on this approach, we\nfocus on extracting medication names and dosages from simulated medical\nrecords. Our methodology MIRAGE (Multimodal Identification and Recognition of\nAnnotations in indian GEneral prescriptions) involves fine-tuning the QWEN VL,\nLLaVA 1.6 and Idefics2 models on 743,118 high resolution simulated medical\nrecord images-fully annotated from 1,133 doctors across India. Our approach\nachieves 82% accuracy in extracting medication names and dosages.\n", "date": "2024-10-13", "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": "2411.06264", "title": "GuidelineGuard: An Agentic Framework for Medical Note Evaluation with\n  Guideline Adherence", "abstract": "  Although rapid advancements in Large Language Models (LLMs) are facilitating\nthe integration of artificial intelligence-based applications and services in\nhealthcare, limited research has focused on the systematic evaluation of\nmedical notes for guideline adherence. This paper introduces GuidelineGuard, an\nagentic framework powered by LLMs that autonomously analyzes medical notes,\nsuch as hospital discharge and office visit notes, to ensure compliance with\nestablished healthcare guidelines. By identifying deviations from recommended\npractices and providing evidence-based suggestions, GuidelineGuard helps\nclinicians adhere to the latest standards from organizations like the WHO and\nCDC. This framework offers a novel approach to improving documentation quality\nand reducing clinical errors.\n", "date": "2024-11-09", "categories": []}
{"id": "2411.06549", "title": "In-Context Learning for Preserving Patient Privacy: A Framework for\n  Synthesizing Realistic Patient Portal Messages", "abstract": "  Since the COVID-19 pandemic, clinicians have seen a large and sustained\ninflux in patient portal messages, significantly contributing to clinician\nburnout. To the best of our knowledge, there are no large-scale public patient\nportal messages corpora researchers can use to build tools to optimize\nclinician portal workflows. Informed by our ongoing work with a regional\nhospital, this study introduces an LLM-powered framework for configurable and\nrealistic patient portal message generation. Our approach leverages few-shot\ngrounded text generation, requiring only a small number of de-identified\npatient portal messages to help LLMs better match the true style and tone of\nreal data. Clinical experts in our team deem this framework as HIPAA-friendly,\nunlike existing privacy-preserving approaches to synthetic text generation\nwhich cannot guarantee all sensitive attributes will be protected. Through\nextensive quantitative and human evaluation, we show that our framework\nproduces data of higher quality than comparable generation methods as well as\nall related datasets. We believe this work provides a path forward for (i) the\nrelease of large-scale synthetic patient message datasets that are\nstylistically similar to ground-truth samples and (ii) HIPAA-friendly data\ngeneration which requires minimal human de-identification efforts.\n", "date": "2024-11-10", "categories": []}
{"id": "2411.06713", "title": "Ambient AI Scribing Support: Comparing the Performance of Specialized AI\n  Agentic Architecture to Leading Foundational Models", "abstract": "  This study compares Sporo Health's AI Scribe, a proprietary model fine-tuned\nfor medical scribing, with various LLMs (GPT-4o, GPT-3.5, Gemma-9B, and\nLlama-3.2-3B) in clinical documentation. We analyzed de-identified patient\ntranscripts from partner clinics, using clinician-provided SOAP notes as the\nground truth. Each model generated SOAP summaries using zero-shot prompting,\nwith performance assessed via recall, precision, and F1 scores. Sporo\noutperformed all models, achieving the highest recall (73.3%), precision\n(78.6%), and F1 score (75.3%) with the lowest performance variance.\nStatistically significant differences (p < 0.05) were found between Sporo and\nthe other models, with post-hoc tests showing significant improvements over\nGPT-3.5, Gemma-9B, and Llama 3.2-3B. While Sporo outperformed GPT-4o by up to\n10%, the difference was not statistically significant (p = 0.25). Clinical user\nsatisfaction, measured with a modified PDQI-9 inventory, favored Sporo.\nEvaluations indicated Sporo's outputs were more accurate and relevant. This\nhighlights the potential of Sporo's multi-agentic architecture to improve\nclinical workflows.\n", "date": "2024-11-11", "categories": []}
{"id": "2411.06823", "title": "Large Language Model in Medical Informatics: Direct Classification and\n  Enhanced Text Representations for Automatic ICD Coding", "abstract": "  Addressing the complexity of accurately classifying International\nClassification of Diseases (ICD) codes from medical discharge summaries is\nchallenging due to the intricate nature of medical documentation. This paper\nexplores the use of Large Language Models (LLM), specifically the LLAMA\narchitecture, to enhance ICD code classification through two methodologies:\ndirect application as a classifier and as a generator of enriched text\nrepresentations within a Multi-Filter Residual Convolutional Neural Network\n(MultiResCNN) framework. We evaluate these methods by comparing them against\nstate-of-the-art approaches, revealing LLAMA's potential to significantly\nimprove classification outcomes by providing deep contextual insights into\nmedical texts.\n", "date": "2024-11-11", "categories": []}
{"id": "2407.07330", "title": "Interpretable Differential Diagnosis with Dual-Inference Large Language\n  Models", "abstract": "  Automatic differential diagnosis (DDx) is an essential medical task that\ngenerates a list of potential diseases as differentials based on patient\nsymptom descriptions. In practice, interpreting these differential diagnoses\nyields significant value but remains under-explored. Given the powerful\ncapabilities of large language models (LLMs), we investigated using LLMs for\ninterpretable DDx. Specifically, we curated the first DDx dataset with\nexpert-derived interpretation on 570 clinical notes. Besides, we proposed\nDual-Inf, a novel framework that enabled LLMs to conduct bidirectional\ninference (i.e., from symptoms to diagnoses and vice versa) for DDx\ninterpretation. Both human and automated evaluation validated its efficacy in\npredicting and elucidating differentials across four base LLMs. In addition,\nDual-Inf could reduce interpretation errors and hold promise for rare disease\nexplanations. To the best of our knowledge, it is the first work that\ncustomizes LLMs for DDx explanation and comprehensively evaluates their\ninterpretation performance. Overall, our study bridges a critical gap in DDx\ninterpretation and enhances clinical decision-making.\n", "date": "2024-07-10", "categories": []}
{"id": "2410.17235", "title": "Automated Spinal MRI Labelling from Reports Using a Large Language Model", "abstract": "  We propose a general pipeline to automate the extraction of labels from\nradiology reports using large language models, which we validate on spinal MRI\nreports. The efficacy of our labelling method is measured on five distinct\nconditions: spinal cancer, stenosis, spondylolisthesis, cauda equina\ncompression and herniation. Using open-source models, our method equals or\nsurpasses GPT-4 on a held-out set of reports. Furthermore, we show that the\nextracted labels can be used to train imaging models to classify the identified\nconditions in the accompanying MR scans. All classifiers trained using\nautomated labels achieve comparable performance to models trained using scans\nmanually annotated by clinicians. Code can be found at\nhttps://github.com/robinyjpark/AutoLabelClassifier.\n", "date": "2024-10-22", "categories": []}
{"id": "2401.11505", "title": "CheX-GPT: Harnessing Large Language Models for Enhanced Chest X-ray\n  Report Labeling", "abstract": "  Free-text radiology reports present a rich data source for various medical\ntasks, but effectively labeling these texts remains challenging. Traditional\nrule-based labeling methods fall short of capturing the nuances of diverse\nfree-text patterns. Moreover, models using expert-annotated data are limited by\ndata scarcity and pre-defined classes, impacting their performance, flexibility\nand scalability. To address these issues, our study offers three main\ncontributions: 1) We demonstrate the potential of GPT as an adept labeler using\ncarefully designed prompts. 2) Utilizing only the data labeled by GPT, we\ntrained a BERT-based labeler, CheX-GPT, which operates faster and more\nefficiently than its GPT counterpart. 3) To benchmark labeler performance, we\nintroduced a publicly available expert-annotated test set, MIMIC-500,\ncomprising 500 cases from the MIMIC validation set. Our findings demonstrate\nthat CheX-GPT not only excels in labeling accuracy over existing models, but\nalso showcases superior efficiency, flexibility, and scalability, supported by\nour introduction of the MIMIC-500 dataset for robust benchmarking. Code and\nmodels are available at https://github.com/Soombit-ai/CheXGPT.\n", "date": "2024-01-21", "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": "2411.02523", "title": "Evaluating the Impact of Lab Test Results on Large Language Models\n  Generated Differential Diagnoses from Clinical Case Vignettes", "abstract": "  Differential diagnosis is crucial for medicine as it helps healthcare\nproviders systematically distinguish between conditions that share similar\nsymptoms. This study assesses the impact of lab test results on differential\ndiagnoses (DDx) made by large language models (LLMs). Clinical vignettes from\n50 case reports from PubMed Central were created incorporating patient\ndemographics, symptoms, and lab results. Five LLMs GPT-4, GPT-3.5, Llama-2-70b,\nClaude-2, and Mixtral-8x7B were tested to generate Top 10, Top 5, and Top 1 DDx\nwith and without lab data. A comprehensive evaluation involving GPT-4, a\nknowledge graph, and clinicians was conducted. GPT-4 performed best, achieving\n55% accuracy for Top 1 diagnoses and 60% for Top 10 with lab data, with lenient\naccuracy up to 80%. Lab results significantly improved accuracy, with GPT-4 and\nMixtral excelling, though exact match rates were low. Lab tests, including\nliver function, metabolic/toxicology panels, and serology/immune tests, were\ngenerally interpreted correctly by LLMs for differential diagnosis.\n", "date": "2024-11-01", "categories": []}
{"id": "2402.12749", "title": "Me LLaMA: Foundation Large Language Models for Medical Applications", "abstract": "  Recent advancements in large language models (LLMs) like ChatGPT and LLaMA\nshow promise in medical applications, yet challenges remain in medical language\ncomprehension. This study presents Me-LLaMA, a new medical LLM family based on\nopen-source LLaMA models, optimized for medical text analysis and diagnosis by\nleveraging large-scale, domain-specific datasets. The Me-LLaMA family,\nincluding foundation models Me-LLaMA 13/70B and their chat-enhanced versions,\nwas developed through continued pre-training and instruction tuning with 129B\ntokens and 214K samples from biomedical and clinical sources. Training the 70B\nmodels required over 100,000 A100 GPU hours. Me-LLaMA's performance was\nevaluated across six medical text analysis tasks using 12 benchmark datasets\nand complex clinical case diagnosis, with automatic and human evaluations.\nResults indicate Me-LLaMA outperforms LLaMA and other open-source medical LLMs\nin zero-shot and supervised settings. Task-specific tuning further boosts\nperformance, surpassing ChatGPT on 7 of 8 datasets and GPT-4 on 5 of 8. For\ncomplex clinical cases, Me-LLaMA achieves performance comparable to ChatGPT and\nGPT-4. This work underscores the importance of domain-specific data in\ndeveloping medical LLMs and addresses the high computational costs involved in\ntraining, highlighting a balance between pre-training and fine-tuning\nstrategies. Me-LLaMA models are now accessible under user agreements, providing\na valuable resource for advancing medical AI.\n", "date": "2024-02-20", "categories": []}
{"id": "2411.02036", "title": "Explainable cognitive decline detection in free dialogues with a Machine\n  Learning approach based on pre-trained Large Language Models", "abstract": "  Cognitive and neurological impairments are very common, but only a small\nproportion of affected individuals are diagnosed and treated, partly because of\nthe high costs associated with frequent screening. Detecting pre-illness stages\nand analyzing the progression of neurological disorders through effective and\nefficient intelligent systems can be beneficial for timely diagnosis and early\nintervention. We propose using Large Language Models to extract features from\nfree dialogues to detect cognitive decline. These features comprise high-level\nreasoning content-independent features (such as comprehension, decreased\nawareness, increased distraction, and memory problems). Our solution comprises\n(i) preprocessing, (ii) feature engineering via Natural Language Processing\ntechniques and prompt engineering, (iii) feature analysis and selection to\noptimize performance, and (iv) classification, supported by automatic\nexplainability. We also explore how to improve Chatgpt's direct cognitive\nimpairment prediction capabilities using the best features in our models.\nEvaluation metrics obtained endorse the effectiveness of a mixed approach\ncombining feature extraction with Chatgpt and a specialized Machine Learning\nmodel to detect cognitive decline within free-form conversational dialogues\nwith older adults. Ultimately, our work may facilitate the development of an\ninexpensive, non-invasive, and rapid means of detecting and explaining\ncognitive decline.\n", "date": "2024-11-04", "categories": []}
{"id": "2312.14180", "title": "Dynamic Topic Language Model on Heterogeneous Children's Mental Health\n  Clinical Notes", "abstract": "  Mental health diseases affect children's lives and well-beings which have\nreceived increased attention since the COVID-19 pandemic. Analyzing psychiatric\nclinical notes with topic models is critical to evaluating children's mental\nstatus over time. However, few topic models are built for longitudinal\nsettings, and most existing approaches fail to capture temporal trajectories\nfor each document. To address these challenges, we develop a dynamic topic\nmodel with consistent topics and individualized temporal dependencies on the\nevolving document metadata. Our model preserves the semantic meaning of\ndiscovered topics over time and incorporates heterogeneity among documents. In\nparticular, when documents can be categorized, we propose a classifier-free\napproach to maximize topic heterogeneity across different document groups. We\nalso present an efficient variational optimization procedure adapted for the\nmultistage longitudinal setting. In this case study, we apply our method to the\npsychiatric clinical notes from a large tertiary pediatric hospital in Southern\nCalifornia and achieve a 38% increase in the overall coherence of extracted\ntopics. Our real data analysis reveals that children tend to express more\nnegative emotions during state shutdowns and more positive when schools reopen.\nFurthermore, it suggests that sexual and gender minority (SGM) children display\nmore pronounced reactions to major COVID-19 events and a greater sensitivity to\nvaccine-related news than non-SGM children. This study examines children's\nmental health progression during the pandemic and offers clinicians valuable\ninsights to recognize disparities in children's mental health related to their\nsexual and gender identities.\n", "date": "2023-12-19", "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": "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": "2406.06591", "title": "Exploring Multilingual Large Language Models for Enhanced TNM\n  classification of Radiology Report in lung cancer staging", "abstract": "  Background: Structured radiology reports remains underdeveloped due to\nlabor-intensive structuring and narrative-style reporting. Deep learning,\nparticularly large language models (LLMs) like GPT-3.5, offers promise in\nautomating the structuring of radiology reports in natural languages. However,\nalthough it has been reported that LLMs are less effective in languages other\nthan English, their radiological performance has not been extensively studied.\nPurpose: This study aimed to investigate the accuracy of TNM classification\nbased on radiology reports using GPT3.5-turbo (GPT3.5) and the utility of\nmultilingual LLMs in both Japanese and English. Material and Methods: Utilizing\nGPT3.5, we developed a system to automatically generate TNM classifications\nfrom chest CT reports for lung cancer and evaluate its performance. We\nstatistically analyzed the impact of providing full or partial TNM definitions\nin both languages using a Generalized Linear Mixed Model. Results: Highest\naccuracy was attained with full TNM definitions and radiology reports in\nEnglish (M = 94%, N = 80%, T = 47%, and ALL = 36%). Providing definitions for\neach of the T, N, and M factors statistically improved their respective\naccuracies (T: odds ratio (OR) = 2.35, p < 0.001; N: OR = 1.94, p < 0.01; M: OR\n= 2.50, p < 0.001). Japanese reports exhibited decreased N and M accuracies (N\naccuracy: OR = 0.74 and M accuracy: OR = 0.21). Conclusion: This study\nunderscores the potential of multilingual LLMs for automatic TNM classification\nin radiology reports. Even without additional model training, performance\nimprovements were evident with the provided TNM definitions, indicating LLMs'\nrelevance in radiology contexts.\n", "date": "2024-06-05", "categories": []}
{"id": "2410.19826", "title": "Novel Development of LLM Driven mCODE Data Model for Improved Clinical\n  Trial Matching to Enable Standardization and Interoperability in Oncology\n  Research", "abstract": "  Each year, the lack of efficient data standardization and interoperability in\ncancer care contributes to the severe lack of timely and effective diagnosis,\nwhile constantly adding to the burden of cost, with cancer costs nationally\nreaching over $208 billion in 2023 alone. Traditional methods regarding\nclinical trial enrollment and clinical care in oncology are often manual,\ntime-consuming, and lack a data-driven approach. This paper presents a novel\nframework to streamline standardization, interoperability, and exchange of\ncancer domains and enhance the integration of oncology-based EHRs across\ndisparate healthcare systems. This paper utilizes advanced LLMs and Computer\nEngineering to streamline cancer clinical trials and discovery. By utilizing\nFHIR's resource-based approach and LLM-generated mCODE profiles, we ensure\ntimely, accurate, and efficient sharing of patient information across disparate\nhealthcare systems. Our methodology involves transforming unstructured patient\ntreatment data, PDFs, free-text information, and progress notes into enriched\nmCODE profiles, facilitating seamless integration with our novel AI and\nML-based clinical trial matching engine. The results of this study show a\nsignificant improvement in data standardization, with accuracy rates of our\ntrained LLM peaking at over 92% with datasets consisting of thousands of\npatient data. Additionally, our LLM demonstrated an accuracy rate of 87% for\nSNOMED-CT, 90% for LOINC, and 84% for RxNorm codes. This trumps the current\nstatus quo, with LLMs such as GPT-4 and Claude's 3.5 peaking at an average of\n77%. This paper successfully underscores the potential of our standardization\nand interoperability framework, paving the way for more efficient and\npersonalized cancer treatment.\n", "date": "2024-10-18", "categories": []}
{"id": "2410.20428", "title": "MedGo: A Chinese Medical Large Language Model", "abstract": "  Large models are a hot research topic in the field of artificial\nintelligence. Leveraging their generative capabilities has the potential to\nenhance the level and quality of medical services. In response to the\nlimitations of current large language models, which often struggle with\naccuracy and have narrow capabilities in medical applications, this paper\npresents a Chinese medical large language model, MedGo. MedGo was trained using\na combination of high quality unsupervised medical data, supervised data, and\npreference alignment data, aimed at enhancing both its versatility and\nprecision in medical tasks. The model was evaluated through the public CBLUE\nbenchmark and a manually constructed dataset ClinicalQA. The results\ndemonstrate that MedGo achieved promising performance across various Chinese\nmedical information processing tasks, achieved the first place in the CBLUE\nevaluation. Additionally, on our constructed dataset ClinicalQA, MedGo\noutperformed its base model Qwen2, highlighting its potential to improve both\nautomated medical question answering and clinical decision support. These\nexperimental results demonstrate that MedGo possesses strong information\nprocessing capabilities in the medical field. At present, we have successfully\ndeployed MedGo at Shanghai East Hospital.\n", "date": "2024-10-27", "categories": []}
{"id": "2312.15561", "title": "README: Bridging Medical Jargon and Lay Understanding for Patient\n  Education through Data-Centric NLP", "abstract": "  The advancement in healthcare has shifted focus toward patient-centric\napproaches, particularly in self-care and patient education, facilitated by\naccess to Electronic Health Records (EHR). However, medical jargon in EHRs\nposes significant challenges in patient comprehension. To address this, we\nintroduce a new task of automatically generating lay definitions, aiming to\nsimplify complex medical terms into patient-friendly lay language. We first\ncreated the README dataset, an extensive collection of over 50,000 unique\n(medical term, lay definition) pairs and 300,000 mentions, each offering\ncontext-aware lay definitions manually annotated by domain experts. We have\nalso engineered a data-centric Human-AI pipeline that synergizes data\nfiltering, augmentation, and selection to improve data quality. We then used\nREADME as the training data for models and leveraged a Retrieval-Augmented\nGeneration method to reduce hallucinations and improve the quality of model\noutputs. Our extensive automatic and human evaluations demonstrate that\nopen-source mobile-friendly models, when fine-tuned with high-quality data, are\ncapable of matching or even surpassing the performance of state-of-the-art\nclosed-source large language models like ChatGPT. This research represents a\nsignificant stride in closing the knowledge gap in patient education and\nadvancing patient-centric healthcare solutions.\n", "date": "2023-12-24", "categories": []}
{"id": "2402.18419", "title": "Can GPT Improve the State of Prior Authorization via Guideline Based\n  Automated Question Answering?", "abstract": "  Health insurance companies have a defined process called prior authorization\n(PA) which is a health plan cost-control process that requires doctors and\nother healthcare professionals to get clearance in advance from a health plan\nbefore performing a particular procedure on a patient in order to be eligible\nfor payment coverage. For health insurance companies, approving PA requests for\npatients in the medical domain is a time-consuming and challenging task. One of\nthose key challenges is validating if a request matches up to certain criteria\nsuch as age, gender, etc. In this work, we evaluate whether GPT can validate\nnumerous key factors, in turn helping health plans reach a decision drastically\nfaster. We frame it as a question answering task, prompting GPT to answer a\nquestion from patient electronic health record. We experiment with different\nconventional prompting techniques as well as introduce our own novel prompting\ntechnique. Moreover, we report qualitative assessment by humans on the natural\nlanguage generation outputs from our approach. Results show that our method\nachieves superior performance with the mean weighted F1 score of 0.61 as\ncompared to its standard counterparts.\n", "date": "2024-02-28", "categories": []}
{"id": "2405.18682", "title": "Can GPT Redefine Medical Understanding? Evaluating GPT on Biomedical\n  Machine Reading Comprehension", "abstract": "  Large language models (LLMs) have shown remarkable performance on many tasks\nin different domains. However, their performance in closed-book biomedical\nmachine reading comprehension (MRC) has not been evaluated in depth. In this\nwork, we evaluate GPT on four closed-book biomedical MRC benchmarks. We\nexperiment with different conventional prompting techniques as well as\nintroduce our own novel prompting method. To solve some of the retrieval\nproblems inherent to LLMs, we propose a prompting strategy named Implicit\nRetrieval Augmented Generation (RAG) that alleviates the need for using vector\ndatabases to retrieve important chunks in traditional RAG setups. Moreover, we\nreport qualitative assessments on the natural language generation outputs from\nour approach. The results show that our new prompting technique is able to get\nthe best performance in two out of four datasets and ranks second in rest of\nthem. Experiments show that modern-day LLMs like GPT even in a zero-shot\nsetting can outperform supervised models, leading to new state-of-the-art\n(SoTA) results on two of the benchmarks.\n", "date": "2024-05-29", "categories": []}
{"id": "2404.14463", "title": "DAIC-WOZ: On the Validity of Using the Therapist's prompts in Automatic\n  Depression Detection from Clinical Interviews", "abstract": "  Automatic depression detection from conversational data has gained\nsignificant interest in recent years. The DAIC-WOZ dataset, interviews\nconducted by a human-controlled virtual agent, has been widely used for this\ntask. Recent studies have reported enhanced performance when incorporating\ninterviewer's prompts into the model. In this work, we hypothesize that this\nimprovement might be mainly due to a bias present in these prompts, rather than\nthe proposed architectures and methods. Through ablation experiments and\nqualitative analysis, we discover that models using interviewer's prompts learn\nto focus on a specific region of the interviews, where questions about past\nexperiences with mental health issues are asked, and use them as discriminative\nshortcuts to detect depressed participants. In contrast, models using\nparticipant responses gather evidence from across the entire interview.\nFinally, to highlight the magnitude of this bias, we achieve a 0.90 F1 score by\nintentionally exploiting it, the highest result reported to date on this\ndataset using only textual information. Our findings underline the need for\ncaution when incorporating interviewers' prompts into models, as they may\ninadvertently learn to exploit targeted prompts, rather than learning to\ncharacterize the language and behavior that are genuinely indicative of the\npatient's mental health condition.\n", "date": "2024-04-22", "categories": []}
{"id": "2406.16845", "title": "RaTEScore: A Metric for Radiology Report Generation", "abstract": "  This paper introduces a novel, entity-aware metric, termed as Radiological\nReport (Text) Evaluation (RaTEScore), to assess the quality of medical reports\ngenerated by AI models. RaTEScore emphasizes crucial medical entities such as\ndiagnostic outcomes and anatomical details, and is robust against complex\nmedical synonyms and sensitive to negation expressions. Technically, we\ndeveloped a comprehensive medical NER dataset, RaTE-NER, and trained an NER\nmodel specifically for this purpose. This model enables the decomposition of\ncomplex radiological reports into constituent medical entities. The metric\nitself is derived by comparing the similarity of entity embeddings, obtained\nfrom a language model, based on their types and relevance to clinical\nsignificance. Our evaluations demonstrate that RaTEScore aligns more closely\nwith human preference than existing metrics, validated both on established\npublic benchmarks and our newly proposed RaTE-Eval benchmark.\n", "date": "2024-06-24", "categories": []}
{"id": "2410.17035", "title": "DIRI: Adversarial Patient Reidentification with Large Language Models\n  for Evaluating Clinical Text Anonymization", "abstract": "  Sharing protected health information (PHI) is critical for furthering\nbiomedical research. Before data can be distributed, practitioners often\nperform deidentification to remove any PHI contained in the text. Contemporary\ndeidentification methods are evaluated on highly saturated datasets (tools\nachieve near-perfect accuracy) which may not reflect the full variability or\ncomplexity of real-world clinical text and annotating them is resource\nintensive, which is a barrier to real-world applications. To address this gap,\nwe developed an adversarial approach using a large language model (LLM) to\nre-identify the patient corresponding to a redacted clinical note and evaluated\nthe performance with a novel De-Identification/Re-Identification (DIRI) method.\nOur method uses a large language model to reidentify the patient corresponding\nto a redacted clinical note. We demonstrate our method on medical data from\nWeill Cornell Medicine anonymized with three deidentification tools: rule-based\nPhilter and two deep-learning-based models, BiLSTM-CRF and ClinicalBERT.\nAlthough ClinicalBERT was the most effective, masking all identified PII, our\ntool still reidentified 9% of clinical notes Our study highlights significant\nweaknesses in current deidentification technologies while providing a tool for\niterative development and improvement.\n", "date": "2024-10-22", "categories": []}
{"id": "2303.09038", "title": "Translating Radiology Reports into Plain Language using ChatGPT and\n  GPT-4 with Prompt Learning: Promising Results, Limitations, and Potential", "abstract": "  The large language model called ChatGPT has drawn extensively attention\nbecause of its human-like expression and reasoning abilities. In this study, we\ninvestigate the feasibility of using ChatGPT in experiments on using ChatGPT to\ntranslate radiology reports into plain language for patients and healthcare\nproviders so that they are educated for improved healthcare. Radiology reports\nfrom 62 low-dose chest CT lung cancer screening scans and 76 brain MRI\nmetastases screening scans were collected in the first half of February for\nthis study. According to the evaluation by radiologists, ChatGPT can\nsuccessfully translate radiology reports into plain language with an average\nscore of 4.27 in the five-point system with 0.08 places of information missing\nand 0.07 places of misinformation. In terms of the suggestions provided by\nChatGPT, they are general relevant such as keeping following-up with doctors\nand closely monitoring any symptoms, and for about 37% of 138 cases in total\nChatGPT offers specific suggestions based on findings in the report. ChatGPT\nalso presents some randomness in its responses with occasionally\nover-simplified or neglected information, which can be mitigated using a more\ndetailed prompt. Furthermore, ChatGPT results are compared with a newly\nreleased large model GPT-4, showing that GPT-4 can significantly improve the\nquality of translated reports. Our results show that it is feasible to utilize\nlarge language models in clinical education, and further efforts are needed to\naddress limitations and maximize their potential.\n", "date": "2023-03-16", "categories": []}
{"id": "2403.17199", "title": "Extracting Social Support and Social Isolation Information from Clinical\n  Psychiatry Notes: Comparing a Rule-based NLP System and a Large Language\n  Model", "abstract": "  Background: Social support (SS) and social isolation (SI) are social\ndeterminants of health (SDOH) associated with psychiatric outcomes. In\nelectronic health records (EHRs), individual-level SS/SI is typically\ndocumented as narrative clinical notes rather than structured coded data.\nNatural language processing (NLP) algorithms can automate the otherwise\nlabor-intensive process of data extraction.\n  Data and Methods: Psychiatric encounter notes from Mount Sinai Health System\n(MSHS, n=300) and Weill Cornell Medicine (WCM, n=225) were annotated and\nestablished a gold standard corpus. A rule-based system (RBS) involving\nlexicons and a large language model (LLM) using FLAN-T5-XL were developed to\nidentify mentions of SS and SI and their subcategories (e.g., social network,\ninstrumental support, and loneliness).\n  Results: For extracting SS/SI, the RBS obtained higher macro-averaged\nf-scores than the LLM at both MSHS (0.89 vs. 0.65) and WCM (0.85 vs. 0.82). For\nextracting subcategories, the RBS also outperformed the LLM at both MSHS (0.90\nvs. 0.62) and WCM (0.82 vs. 0.81).\n  Discussion and Conclusion: Unexpectedly, the RBS outperformed the LLMs across\nall metrics. Intensive review demonstrates that this finding is due to the\ndivergent approach taken by the RBS and LLM. The RBS were designed and refined\nto follow the same specific rules as the gold standard annotations. Conversely,\nthe LLM were more inclusive with categorization and conformed to common\nEnglish-language understanding. Both approaches offer advantages and are made\navailable open-source for future testing.\n", "date": "2024-03-25", "categories": []}
{"id": "2410.15528", "title": "Improving Clinical Documentation with AI: A Comparative Study of Sporo\n  AI Scribe and GPT-4o mini", "abstract": "  AI-powered medical scribes have emerged as a promising solution to alleviate\nthe documentation burden in healthcare. Ambient AI scribes provide real-time\ntranscription and automated data entry into Electronic Health Records (EHRs),\nwith the potential to improve efficiency, reduce costs, and enhance\nscalability. Despite early success, the accuracy of AI scribes remains\ncritical, as errors can lead to significant clinical consequences.\nAdditionally, AI scribes face challenges in handling the complexity and\nvariability of medical language and ensuring the privacy of sensitive patient\ndata. This case study aims to evaluate Sporo Health's AI scribe, a multi-agent\nsystem leveraging fine-tuned medical LLMs, by comparing its performance with\nOpenAI's GPT-4o Mini on multiple performance metrics. Using a dataset of\nde-identified patient conversation transcripts, AI-generated summaries were\ncompared to clinician-generated notes (the ground truth) based on clinical\ncontent recall, precision, and F1 scores. Evaluations were further supplemented\nby clinician satisfaction assessments using a modified Physician Documentation\nQuality Instrument revision 9 (PDQI-9), rated by both a medical student and a\nphysician. The results show that Sporo AI consistently outperformed GPT-4o\nMini, achieving higher recall, precision, and overall F1 scores. Moreover, the\nAI generated summaries provided by Sporo were rated more favorably in terms of\naccuracy, comprehensiveness, and relevance, with fewer hallucinations. These\nfindings demonstrate that Sporo AI Scribe is an effective and reliable tool for\nclinical documentation, enhancing clinician workflows while maintaining high\nstandards of privacy and security.\n", "date": "2024-10-20", "categories": []}
{"id": "2410.15998", "title": "1024m at SMM4H 2024: Tasks 3, 5 & 6 -- Ensembles of Transformers and\n  Large Language Models for Medical Text Classification", "abstract": "  Social media is a great source of data for users reporting information and\nregarding their health and how various things have had an effect on them. This\npaper presents various approaches using Transformers and Large Language Models\nand their ensembles, their performance along with advantages and drawbacks for\nvarious tasks of SMM4H'24 - Classifying texts on impact of nature and outdoor\nspaces on the author's mental health (Task 3), Binary classification of tweets\nreporting their children's health disorders like Asthma, Autism, ADHD and\nSpeech disorder (task 5), Binary classification of users self-reporting their\nage (task 6).\n", "date": "2024-10-21", "categories": []}
{"id": "2410.14236", "title": "A Novel Method to Metigate Demographic and Expert Bias in ICD Coding\n  with Causal Inference", "abstract": "  ICD(International Classification of Diseases) coding involves assigning ICD\ncodes to patients visit based on their medical notes. Considering ICD coding as\na multi-label text classification task, researchers have developed\nsophisticated methods. Despite progress, these models often suffer from label\nimbalance and may develop spurious correlations with demographic factors.\nAdditionally, while human coders assign ICD codes, the inclusion of irrelevant\ninformation from unrelated experts introduces biases. To combat these issues,\nwe propose a novel method to mitigate Demographic and Expert biases in ICD\ncoding through Causal Inference (DECI). We provide a novel causality-based\ninterpretation in ICD Coding that models make predictions by three distinct\npathways. And based counterfactual reasoning, DECI mitigate demographic and\nexpert biases. Experimental results show that DECI outperforms state-of-the-art\nmodels, offering a significant advancement in accurate and unbiased ICD coding.\n", "date": "2024-10-18", "categories": []}
{"id": "2410.12860", "title": "LLMD: A Large Language Model for Interpreting Longitudinal Medical\n  Records", "abstract": "  We introduce LLMD, a large language model designed to analyze a patient's\nmedical history based on their medical records. Along with domain knowledge,\nLLMD is trained on a large corpus of records collected over time and across\nfacilities, as well as tasks and labels that make nuanced connections among\nthem. This approach is critical to an accurate picture of patient health, and\nhas distinctive advantages over models trained on knowledge alone, unlabeled\nrecords, structured EHR data, or records from a single health system.\n  The recipe for LLMD continues pretraining a foundational model on both domain\nknowledge and the contents of millions of records. These span an average of 10\nyears of care and as many as 140 care sites per patient. LLMD is then\ninstruction fine-tuned on structuring and abstraction tasks. The former jointly\nidentify and normalize document metadata, provenance information, clinical\nnamed-entities, and ontology mappings, while the latter roll these into\nhigher-level representations, such a continuous era of time a patient was on a\nmedication. LLMD is deployed within a layered validation system that includes\ncontinual random audits and review by experts, e.g. based on uncertainty,\ndisease-specific rules, or use-case.\n  LLMD exhibits large gains over both more-powerful generalized models and\ndomain-specific models. On medical knowledge benchmarks, LLMD-8B achieves state\nof the art accuracy on PubMedQA text responses, besting orders-of-magnitude\nlarger models. On production tasks, we show that LLMD significantly outperforms\nall other models evaluated, and among alternatives, large general purpose LLMs\nlike GPT-4o are more accurate than models emphasizing medical knowledge. We\nfind strong evidence that accuracy on today's medical benchmarks is not the\nmost significant factor when analyzing real-world patient data, an insight with\nimplications for future medical LLMs.'\n", "date": "2024-10-11", "categories": []}
{"id": "2410.13458", "title": "MedINST: Meta Dataset of Biomedical Instructions", "abstract": "  The integration of large language model (LLM) techniques in the field of\nmedical analysis has brought about significant advancements, yet the scarcity\nof large, diverse, and well-annotated datasets remains a major challenge.\nMedical data and tasks, which vary in format, size, and other parameters,\nrequire extensive preprocessing and standardization for effective use in\ntraining LLMs. To address these challenges, we introduce MedINST, the Meta\nDataset of Biomedical Instructions, a novel multi-domain, multi-task\ninstructional meta-dataset. MedINST comprises 133 biomedical NLP tasks and over\n7 million training samples, making it the most comprehensive biomedical\ninstruction dataset to date. Using MedINST as the meta dataset, we curate\nMedINST32, a challenging benchmark with different task difficulties aiming to\nevaluate LLMs' generalization ability. We fine-tune several LLMs on MedINST and\nevaluate on MedINST32, showcasing enhanced cross-task generalization.\n", "date": "2024-10-17", "categories": []}
{"id": "2405.00716", "title": "Large Language Models in the Clinic: A Comprehensive Benchmark", "abstract": "  The adoption of large language models (LLMs) to assist clinicians has\nattracted remarkable attention. Existing works mainly adopt the close-ended\nquestion-answering (QA) task with answer options for evaluation. However, many\nclinical decisions involve answering open-ended questions without pre-set\noptions. To better understand LLMs in the clinic, we construct a benchmark\nClinicBench. We first collect eleven existing datasets covering diverse\nclinical language generation, understanding, and reasoning tasks. Furthermore,\nwe construct six novel datasets and clinical tasks that are complex but common\nin real-world practice, e.g., open-ended decision-making, long document\nprocessing, and emerging drug analysis. We conduct an extensive evaluation of\ntwenty-two LLMs under both zero-shot and few-shot settings. Finally, we invite\nmedical experts to evaluate the clinical usefulness of LLMs. The benchmark data\nis available at https://github.com/AI-in-Health/ClinicBench.\n", "date": "2024-04-25", "categories": []}
{"id": "2401.09637", "title": "Impact of Large Language Model Assistance on Patients Reading Clinical\n  Notes: A Mixed-Methods Study", "abstract": "  Large language models (LLMs) have immense potential to make information more\naccessible, particularly in medicine, where complex medical jargon can hinder\npatient comprehension of clinical notes. We developed a patient-facing tool\nusing LLMs to make clinical notes more readable by simplifying, extracting\ninformation from, and adding context to the notes. We piloted the tool with\nclinical notes donated by patients with a history of breast cancer and\nsynthetic notes from a clinician. Participants (N=200, healthy,\nfemale-identifying patients) were randomly assigned three clinical notes in our\ntool with varying levels of augmentations and answered quantitative and\nqualitative questions evaluating their understanding of follow-up actions.\nAugmentations significantly increased their quantitative understanding scores.\nIn-depth interviews were conducted with participants (N=7, patients with a\nhistory of breast cancer), revealing both positive sentiments about the\naugmentations and concerns about AI. We also performed a qualitative\nclinician-driven analysis of the model's error modes.\n", "date": "2024-01-17", "categories": []}
{"id": "2406.09923", "title": "CliBench: A Multifaceted and Multigranular Evaluation of Large Language\n  Models for Clinical Decision Making", "abstract": "  The integration of Artificial Intelligence (AI), especially Large Language\nModels (LLMs), into the clinical diagnosis process offers significant potential\nto improve the efficiency and accessibility of medical care. While LLMs have\nshown some promise in the medical domain, their application in clinical\ndiagnosis remains underexplored, especially in real-world clinical practice,\nwhere highly sophisticated, patient-specific decisions need to be made. Current\nevaluations of LLMs in this field are often narrow in scope, focusing on\nspecific diseases or specialties and employing simplified diagnostic tasks. To\nbridge this gap, we introduce CliBench, a novel benchmark developed from the\nMIMIC IV dataset, offering a comprehensive and realistic assessment of LLMs'\ncapabilities in clinical diagnosis. This benchmark not only covers diagnoses\nfrom a diverse range of medical cases across various specialties but also\nincorporates tasks of clinical significance: treatment procedure\nidentification, lab test ordering and medication prescriptions. Supported by\nstructured output ontologies, CliBench enables a precise and multi-granular\nevaluation, offering an in-depth understanding of LLM's capability on diverse\nclinical tasks of desired granularity. We conduct a zero-shot evaluation of\nleading LLMs to assess their proficiency in clinical decision-making. Our\npreliminary results shed light on the potential and limitations of current LLMs\nin clinical settings, providing valuable insights for future advancements in\nLLM-powered healthcare.\n", "date": "2024-06-14", "categories": []}
{"id": "2410.09234", "title": "Fine-Tuning In-House Large Language Models to Infer Differential\n  Diagnosis from Radiology Reports", "abstract": "  Radiology reports summarize key findings and differential diagnoses derived\nfrom medical imaging examinations. The extraction of differential diagnoses is\ncrucial for downstream tasks, including patient management and treatment\nplanning. However, the unstructured nature of these reports, characterized by\ndiverse linguistic styles and inconsistent formatting, presents significant\nchallenges. Although proprietary large language models (LLMs) such as GPT-4 can\neffectively retrieve clinical information, their use is limited in practice by\nhigh costs and concerns over the privacy of protected health information (PHI).\nThis study introduces a pipeline for developing in-house LLMs tailored to\nidentify differential diagnoses from radiology reports. We first utilize GPT-4\nto create 31,056 labeled reports, then fine-tune open source LLM using this\ndataset. Evaluated on a set of 1,067 reports annotated by clinicians, the\nproposed model achieves an average F1 score of 92.1\\%, which is on par with\nGPT-4 (90.8\\%). Through this study, we provide a methodology for constructing\nin-house LLMs that: match the performance of GPT, reduce dependence on\nexpensive proprietary models, and enhance the privacy and security of PHI.\n", "date": "2024-10-11", "categories": []}
{"id": "2410.09628", "title": "Enhanced Electronic Health Records Text Summarization Using Large\n  Language Models", "abstract": "  The development of Electronic Health Records summarization systems has\nrevolutionized patient data management. Previous research advanced this field\nby adapting Large Language Models for clinical tasks, using diverse datasets to\ngenerate general EHR summaries. However, clinicians often require specific,\nfocused summaries for quicker insights. This project builds on prior work by\ncreating a system that generates clinician-preferred, focused summaries,\nimproving EHR summarization for more efficient patient care. The proposed\nsystem leverages the Google Flan-T5 model to generate tailored EHR summaries\nbased on clinician-specified topics. The approach involved fine-tuning the\nFlan-T5 model on an EHR question-answering dataset formatted in the Stanford\nQuestion Answering Dataset (SQuAD) style, which is a large-scale reading\ncomprehension dataset with questions and answers. Fine-tuning utilized the\nSeq2SeqTrainer from the Hugging Face Transformers library with optimized\nhyperparameters. Key evaluation metrics demonstrated promising results: the\nsystem achieved an Exact Match (EM) score of 81.81%. ROUGE (Recall-Oriented\nUnderstudy for Gisting Evaluation) metrics showed strong performance, with\nROUGE-1 at 96.03%, ROUGE-2 at 86.67%, and ROUGE-L at 96.10%. Additionally, the\nBilingual Evaluation Understudy (BLEU) score was 63%, reflecting the model's\ncoherence in generating summaries. By enhancing EHR summarization through LLMs,\nthis project supports digital transformation efforts in healthcare,\nstreamlining workflows, and enabling more personalized patient care.\n", "date": "2024-10-12", "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": "2410.07951", "title": "Disease Entity Recognition and Normalization is Improved with Large\n  Language Model Derived Synthetic Normalized Mentions", "abstract": "  Background: Machine learning methods for clinical named entity recognition\nand entity normalization systems can utilize both labeled corpora and Knowledge\nGraphs (KGs) for learning. However, infrequently occurring concepts may have\nfew mentions in training corpora and lack detailed descriptions or synonyms,\neven in large KGs. For Disease Entity Recognition (DER) and Disease Entity\nNormalization (DEN), this can result in fewer high quality training examples\nrelative to the number of known diseases. Large Language Model (LLM) generation\nof synthetic training examples could improve performance in these information\nextraction tasks.\n  Methods: We fine-tuned a LLaMa-2 13B Chat LLM to generate a synthetic corpus\ncontaining normalized mentions of concepts from the Unified Medical Language\nSystem (UMLS) Disease Semantic Group. We measured overall and Out of\nDistribution (OOD) performance for DER and DEN, with and without synthetic data\naugmentation. We evaluated performance on 3 different disease corpora using 4\ndifferent data augmentation strategies, assessed using BioBERT for DER and\nSapBERT and KrissBERT for DEN.\n  Results: Our synthetic data yielded a substantial improvement for DEN, in all\n3 training corpora the top 1 accuracy of both SapBERT and KrissBERT improved by\n3-9 points in overall performance and by 20-55 points in OOD data. A small\nimprovement (1-2 points) was also seen for DER in overall performance, but only\none dataset showed OOD improvement.\n  Conclusion: LLM generation of normalized disease mentions can improve DEN\nrelative to normalization approaches that do not utilize LLMs to augment data\nwith synthetic mentions. Ablation studies indicate that performance gains for\nDEN were only partially attributable to improvements in OOD performance. The\nsame approach has only a limited ability to improve DER. We make our software\nand dataset publicly available.\n", "date": "2024-10-10", "categories": []}
{"id": "2402.12801", "title": "Few-shot clinical entity recognition in English, French and Spanish:\n  masked language models outperform generative model prompting", "abstract": "  Large language models (LLMs) have become the preferred solution for many\nnatural language processing tasks. In low-resource environments such as\nspecialized domains, their few-shot capabilities are expected to deliver high\nperformance. Named Entity Recognition (NER) is a critical task in information\nextraction that is not covered in recent LLM benchmarks. There is a need for\nbetter understanding the performance of LLMs for NER in a variety of settings\nincluding languages other than English. This study aims to evaluate generative\nLLMs, employed through prompt engineering, for few-shot clinical NER. %from the\nperspective of F1 performance and environmental impact. We compare 13\nauto-regressive models using prompting and 16 masked models using fine-tuning\non 14 NER datasets covering English, French and Spanish. While prompt-based\nauto-regressive models achieve competitive F1 for general NER, they are\noutperformed within the clinical domain by lighter biLSTM-CRF taggers based on\nmasked models. Additionally, masked models exhibit lower environmental impact\ncompared to auto-regressive models. Findings are consistent across the three\nlanguages studied, which suggests that LLM prompting is not yet suited for NER\nproduction in the clinical domain.\n", "date": "2024-02-20", "categories": []}
{"id": "2405.12021", "title": "Can AI Relate: Testing Large Language Model Response for Mental Health\n  Support", "abstract": "  Large language models (LLMs) are already being piloted for clinical use in\nhospital systems like NYU Langone, Dana-Farber and the NHS. A proposed\ndeployment use case is psychotherapy, where a LLM-powered chatbot can treat a\npatient undergoing a mental health crisis. Deployment of LLMs for mental health\nresponse could hypothetically broaden access to psychotherapy and provide new\npossibilities for personalizing care. However, recent high-profile failures,\nlike damaging dieting advice offered by the Tessa chatbot to patients with\neating disorders, have led to doubt about their reliability in high-stakes and\nsafety-critical settings.\n  In this work, we develop an evaluation framework for determining whether LLM\nresponse is a viable and ethical path forward for the automation of mental\nhealth treatment. Our framework measures equity in empathy and adherence of LLM\nresponses to motivational interviewing theory. Using human evaluation with\ntrained clinicians and automatic quality-of-care metrics grounded in psychology\nresearch, we compare the responses provided by peer-to-peer responders to those\nprovided by a state-of-the-art LLM.\n  We show that LLMs like GPT-4 use implicit and explicit cues to infer patient\ndemographics like race. We then show that there are statistically significant\ndiscrepancies between patient subgroups: Responses to Black posters\nconsistently have lower empathy than for any other demographic group (2%-13%\nlower than the control group). Promisingly, we do find that the manner in which\nresponses are generated significantly impacts the quality of the response. We\nconclude by proposing safety guidelines for the potential deployment of LLMs\nfor mental health response.\n", "date": "2024-05-20", "categories": []}
{"id": "2410.07111", "title": "Utility of Multimodal Large Language Models in Analyzing Chest X-ray\n  with Incomplete Contextual Information", "abstract": "  Background: Large language models (LLMs) are gaining use in clinical\nsettings, but their performance can suffer with incomplete radiology reports.\nWe tested whether multimodal LLMs (using text and images) could improve\naccuracy and understanding in chest radiography reports, making them more\neffective for clinical decision support.\n  Purpose: To assess the robustness of LLMs in generating accurate impressions\nfrom chest radiography reports using both incomplete data and multimodal data.\nMaterial and Methods: We used 300 radiology image-report pairs from the\nMIMIC-CXR database. Three LLMs (OpenFlamingo, MedFlamingo, IDEFICS) were tested\nin both text-only and multimodal formats. Impressions were first generated from\nthe full text, then tested by removing 20%, 50%, and 80% of the text. The\nimpact of adding images was evaluated using chest x-rays, and model performance\nwas compared using three metrics with statistical analysis.\n  Results: The text-only models (OpenFlamingo, MedFlamingo, IDEFICS) had\nsimilar performance (ROUGE-L: 0.39 vs. 0.21 vs. 0.21; F1RadGraph: 0.34 vs. 0.17\nvs. 0.17; F1CheXbert: 0.53 vs. 0.40 vs. 0.40), with OpenFlamingo performing\nbest on complete text (p<0.001). Performance declined with incomplete data\nacross all models. However, adding images significantly boosted the performance\nof MedFlamingo and IDEFICS (p<0.001), equaling or surpassing OpenFlamingo, even\nwith incomplete text. Conclusion: LLMs may produce low-quality outputs with\nincomplete radiology data, but multimodal LLMs can improve reliability and\nsupport clinical decision-making.\n  Keywords: Large language model; multimodal; semantic analysis; Chest\nRadiography; Clinical Decision Support;\n", "date": "2024-09-20", "categories": []}
{"id": "2409.05370", "title": "KARGEN: Knowledge-enhanced Automated Radiology Report Generation Using\n  Large Language Models", "abstract": "  Harnessing the robust capabilities of Large Language Models (LLMs) for\nnarrative generation, logical reasoning, and common-sense knowledge\nintegration, this study delves into utilizing LLMs to enhance automated\nradiology report generation (R2Gen). Despite the wealth of knowledge within\nLLMs, efficiently triggering relevant knowledge within these large models for\nspecific tasks like R2Gen poses a critical research challenge. This paper\npresents KARGEN, a Knowledge-enhanced Automated radiology Report GENeration\nframework based on LLMs. Utilizing a frozen LLM to generate reports, the\nframework integrates a knowledge graph to unlock chest disease-related\nknowledge within the LLM to enhance the clinical utility of generated reports.\nThis is achieved by leveraging the knowledge graph to distill disease-related\nfeatures in a designed way. Since a radiology report encompasses both normal\nand disease-related findings, the extracted graph-enhanced disease-related\nfeatures are integrated with regional image features, attending to both\naspects. We explore two fusion methods to automatically prioritize and select\nthe most relevant features. The fused features are employed by LLM to generate\nreports that are more sensitive to diseases and of improved quality. Our\napproach demonstrates promising results on the MIMIC-CXR and IU-Xray datasets.\n", "date": "2024-09-09", "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": "2410.03770", "title": "A Two-Stage Proactive Dialogue Generator for Efficient Clinical\n  Information Collection Using Large Language Model", "abstract": "  Efficient patient-doctor interaction is among the key factors for a\nsuccessful disease diagnosis. During the conversation, the doctor could query\ncomplementary diagnostic information, such as the patient's symptoms, previous\nsurgery, and other related information that goes beyond medical evidence data\n(test results) to enhance disease diagnosis. However, this procedure is usually\ntime-consuming and less-efficient, which can be potentially optimized through\ncomputer-assisted systems. As such, we propose a diagnostic dialogue system to\nautomate the patient information collection procedure. By exploiting medical\nhistory and conversation logic, our conversation agents, particularly the\ndoctor agent, can pose multi-round clinical queries to effectively collect the\nmost relevant disease diagnostic information. Moreover, benefiting from our\ntwo-stage recommendation structure, carefully designed ranking criteria, and\ninteractive patient agent, our model is able to overcome the under-exploration\nand non-flexible challenges in dialogue generation. Our experimental results on\na real-world medical conversation dataset show that our model can generate\nclinical queries that mimic the conversation style of real doctors, with\nefficient fluency, professionalism, and safety, while effectively collecting\nrelevant disease diagnostic information.\n", "date": "2024-10-02", "categories": []}
{"id": "2410.05046", "title": "Named Clinical Entity Recognition Benchmark", "abstract": "  This technical report introduces a Named Clinical Entity Recognition\nBenchmark for evaluating language models in healthcare, addressing the crucial\nnatural language processing (NLP) task of extracting structured information\nfrom clinical narratives to support applications like automated coding,\nclinical trial cohort identification, and clinical decision support.\n  The leaderboard provides a standardized platform for assessing diverse\nlanguage models, including encoder and decoder architectures, on their ability\nto identify and classify clinical entities across multiple medical domains. A\ncurated collection of openly available clinical datasets is utilized,\nencompassing entities such as diseases, symptoms, medications, procedures, and\nlaboratory measurements. Importantly, these entities are standardized according\nto the Observational Medical Outcomes Partnership (OMOP) Common Data Model,\nensuring consistency and interoperability across different healthcare systems\nand datasets, and a comprehensive evaluation of model performance. Performance\nof models is primarily assessed using the F1-score, and it is complemented by\nvarious assessment modes to provide comprehensive insights into model\nperformance. The report also includes a brief analysis of models evaluated to\ndate, highlighting observed trends and limitations.\n  By establishing this benchmarking framework, the leaderboard aims to promote\ntransparency, facilitate comparative analyses, and drive innovation in clinical\nentity recognition tasks, addressing the need for robust evaluation methods in\nhealthcare NLP.\n", "date": "2024-10-07", "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": "2311.09581", "title": "DocLens: Multi-aspect Fine-grained Evaluation for Medical Text\n  Generation", "abstract": "  Medical text generation aims to assist with administrative work and highlight\nsalient information to support decision-making. To reflect the specific\nrequirements of medical text, in this paper, we propose a set of metrics to\nevaluate the completeness, conciseness, and attribution of the generated text\nat a fine-grained level. The metrics can be computed by various types of\nevaluators including instruction-following (both proprietary and open-source)\nand supervised entailment models. We demonstrate the effectiveness of the\nresulting framework, DocLens, with three evaluators on three tasks: clinical\nnote generation, radiology report summarization, and patient question\nsummarization. A comprehensive human study shows that DocLens exhibits\nsubstantially higher agreement with the judgments of medical experts than\nexisting metrics. The results also highlight the need to improve open-source\nevaluators and suggest potential directions.\n", "date": "2023-11-16", "categories": []}
{"id": "2402.13919", "title": "SYNFAC-EDIT: Synthetic Imitation Edit Feedback for Factual Alignment in\n  Clinical Summarization", "abstract": "  Large Language Models (LLMs) such as GPT & Llama have demonstrated\nsignificant achievements in summarization tasks but struggle with factual\ninaccuracies, a critical issue in clinical NLP applications where errors could\nlead to serious consequences. To counter the high costs and limited\navailability of expert-annotated data for factual alignment, this study\nintroduces an innovative pipeline that utilizes >100B parameter GPT variants\nlike GPT-3.5 & GPT-4 to act as synthetic experts to generate high-quality\nsynthetics feedback aimed at enhancing factual consistency in clinical note\nsummarization. Our research primarily focuses on edit feedback generated by\nthese synthetic feedback experts without additional human annotations,\nmirroring and optimizing the practical scenario in which medical professionals\nrefine AI system outputs. Although such 100B+ parameter GPT variants have\nproven to demonstrate expertise in various clinical NLP tasks, such as the\nMedical Licensing Examination, there is scant research on their capacity to act\nas synthetic feedback experts and deliver expert-level edit feedback for\nimproving the generation quality of weaker (<10B parameter) LLMs like GPT-2\n(1.5B) & Llama 2 (7B) in clinical domain. So in this work, we leverage 100B+\nGPT variants to act as synthetic feedback experts offering expert-level edit\nfeedback, that is used to reduce hallucinations and align weaker (<10B\nparameter) LLMs with medical facts using two distinct alignment algorithms (DPO\n& SALT), endeavoring to narrow the divide between AI-generated content and\nfactual accuracy. This highlights the substantial potential of LLM-based\nsynthetic edits in enhancing the alignment of clinical factuality.\n", "date": "2024-02-21", "categories": []}
{"id": "2405.15766", "title": "Enhancing Adverse Drug Event Detection with Multimodal Dataset: Corpus\n  Creation and Model Development", "abstract": "  The mining of adverse drug events (ADEs) is pivotal in pharmacovigilance,\nenhancing patient safety by identifying potential risks associated with\nmedications, facilitating early detection of adverse events, and guiding\nregulatory decision-making. Traditional ADE detection methods are reliable but\nslow, not easily adaptable to large-scale operations, and offer limited\ninformation. With the exponential increase in data sources like social media\ncontent, biomedical literature, and Electronic Medical Records (EMR),\nextracting relevant ADE-related information from these unstructured texts is\nimperative. Previous ADE mining studies have focused on text-based\nmethodologies, overlooking visual cues, limiting contextual comprehension, and\nhindering accurate interpretation. To address this gap, we present a MultiModal\nAdverse Drug Event (MMADE) detection dataset, merging ADE-related textual\ninformation with visual aids. Additionally, we introduce a framework that\nleverages the capabilities of LLMs and VLMs for ADE detection by generating\ndetailed descriptions of medical images depicting ADEs, aiding healthcare\nprofessionals in visually identifying adverse events. Using our MMADE dataset,\nwe showcase the significance of integrating visual cues from images to enhance\noverall performance. This approach holds promise for patient safety, ADE\nawareness, and healthcare accessibility, paving the way for further exploration\nin personalized healthcare.\n", "date": "2024-05-24", "categories": []}
{"id": "2410.00250", "title": "A Methodology for Explainable Large Language Models with Integrated\n  Gradients and Linguistic Analysis in Text Classification", "abstract": "  Neurological disorders that affect speech production, such as Alzheimer's\nDisease (AD), significantly impact the lives of both patients and caregivers,\nwhether through social, psycho-emotional effects or other aspects not yet fully\nunderstood. Recent advancements in Large Language Model (LLM) architectures\nhave developed many tools to identify representative features of neurological\ndisorders through spontaneous speech. However, LLMs typically lack\ninterpretability, meaning they do not provide clear and specific reasons for\ntheir decisions. Therefore, there is a need for methods capable of identifying\nthe representative features of neurological disorders in speech and explaining\nclearly why these features are relevant. This paper presents an explainable LLM\nmethod, named SLIME (Statistical and Linguistic Insights for Model\nExplanation), capable of identifying lexical components representative of AD\nand indicating which components are most important for the LLM's decision. In\ndeveloping this method, we used an English-language dataset consisting of\ntranscriptions from the Cookie Theft picture description task. The LLM\nBidirectional Encoder Representations from Transformers (BERT) classified the\ntextual descriptions as either AD or control groups. To identify representative\nlexical features and determine which are most relevant to the model's decision,\nwe used a pipeline involving Integrated Gradients (IG), Linguistic Inquiry and\nWord Count (LIWC), and statistical analysis. Our method demonstrates that BERT\nleverages lexical components that reflect a reduction in social references in\nAD and identifies which further improve the LLM's accuracy. Thus, we provide an\nexplainability tool that enhances confidence in applying LLMs to neurological\nclinical contexts, particularly in the study of neurodegeneration.\n", "date": "2024-09-30", "categories": []}
{"id": "2410.00616", "title": "Detecci\\'on Autom\\'atica de Patolog\\'ias en Notas Cl\\'inicas en\n  Espa\\~nol Combinando Modelos de Lenguaje y Ontolog\\'ias M\\'edicos", "abstract": "  In this paper we present a hybrid method for the automatic detection of\ndermatological pathologies in medical reports. We use a large language model\ncombined with medical ontologies to predict, given a first appointment or\nfollow-up medical report, the pathology a person may suffer from. The results\nshow that teaching the model to learn the type, severity and location on the\nbody of a dermatological pathology as well as in which order it has to learn\nthese three features significantly increases its accuracy. The article presents\nthe demonstration of state-of-the-art results for classification of medical\ntexts with a precision of 0.84, micro and macro F1-score of 0.82 and 0.75, and\nmakes both the method and the dataset used available to the community.\n  --\n  En este art\\'iculo presentamos un m\\'etodo h\\'ibrido para la detecci\\'on\nautom\\'atica de patolog\\'ias dermatol\\'ogicas en informes m\\'edicos. Usamos un\nmodelo de lenguaje amplio en espa\\~nol combinado con ontolog\\'ias m\\'edicas\npara predecir, dado un informe m\\'edico de primera cita o de seguimiento, la\npatolog\\'ia del paciente. Los resultados muestran que el tipo, la gravedad y el\nsitio en el cuerpo de una patolog\\'ia dermatol\\'ogica, as\\'i como en qu\\'e\norden tiene un modelo que aprender esas tres caracter\\'isticas, aumentan su\nprecisi\\'on. El art\\'iculo presenta la demostraci\\'on de resultados comparables\nal estado del arte de clasificaci\\'on de textos m\\'edicos con una precisi\\'on\nde 0.84, micro y macro F1-score de 0.82 y 0.75, y deja a disposici\\'on de la\ncomunidad tanto el m\\'etodo como el conjunto de datos utilizado.\n", "date": "2024-10-01", "categories": []}
{"id": "2409.19796", "title": "Black-Box Segmentation of Electronic Medical Records", "abstract": "  Electronic medical records (EMRs) contain the majority of patients'\nhealthcare details. It is an abundant resource for developing an automatic\nhealthcare system. Most of the natural language processing (NLP) studies on EMR\nprocessing, such as concept extraction, are adversely affected by the\ninaccurate segmentation of EMR sections. At the same time, not enough attention\nhas been given to the accurate sectioning of EMRs. The information that may\noccur in section structures is unvalued. This work focuses on the segmentation\nof EMRs and proposes a black-box segmentation method using a simple sentence\nembedding model and neural network, along with a proper training method. To\nachieve universal adaptivity, we train our model on the dataset with different\nsection headings formats. We compare several advanced deep learning-based NLP\nmethods, and our method achieves the best segmentation accuracies (above 98%)\non various test data with a proper training corpus.\n", "date": "2024-09-29", "categories": []}
{"id": "2409.20147", "title": "Classification of Radiological Text in Small and Imbalanced Datasets in\n  a Non-English Language", "abstract": "  Natural language processing (NLP) in the medical domain can underperform in\nreal-world applications involving small datasets in a non-English language with\nfew labeled samples and imbalanced classes. There is yet no consensus on how to\napproach this problem. We evaluated a set of NLP models including BERT-like\ntransformers, few-shot learning with sentence transformers (SetFit), and\nprompted large language models (LLM), using three datasets of radiology reports\non magnetic resonance images of epilepsy patients in Danish, a low-resource\nlanguage. Our results indicate that BERT-like models pretrained in the target\ndomain of radiology reports currently offer the optimal performances for this\nscenario. Notably, the SetFit and LLM models underperformed compared to\nBERT-like models, with LLM performing the worst. Importantly, none of the\nmodels investigated was sufficiently accurate to allow for text classification\nwithout any supervision. However, they show potential for data filtering, which\ncould reduce the amount of manual labeling required.\n", "date": "2024-09-30", "categories": []}
{"id": "2401.02369", "title": "SPEER: Sentence-Level Planning of Long Clinical Summaries via Embedded\n  Entity Retrieval", "abstract": "  Clinician must write a lengthy summary each time a patient is discharged from\nthe hospital. This task is time-consuming due to the sheer number of unique\nclinical concepts covered in the admission. Identifying and covering salient\nentities is vital for the summary to be clinically useful. We fine-tune\nopen-source LLMs (Mistral-7B-Instruct and Zephyr-7B-beta) on the task and find\nthat they generate incomplete and unfaithful summaries. To increase entity\ncoverage, we train a smaller, encoder-only model to predict salient entities,\nwhich are treated as content-plans to guide the LLM. To encourage the LLM to\nfocus on specific mentions in the source notes, we propose SPEER:\nSentence-level Planning via Embedded Entity Retrieval. Specifically, we mark\neach salient entity span with special \"{{ }}\" boundary tags and instruct the\nLLM to retrieve marked spans before generating each sentence. Sentence-level\nplanning acts as a form of state tracking in that the model is explicitly\nrecording the entities it uses. We fine-tune Mistral and Zephyr variants on a\nlarge-scale, diverse dataset of ~167k in-patient hospital admissions and\nevaluate on 3 datasets. SPEER shows gains in both coverage and faithfulness\nmetrics over non-guided and guided baselines.\n", "date": "2024-01-04", "categories": []}
{"id": "2405.06802", "title": "Summarizing Radiology Reports Findings into Impressions", "abstract": "  Patient hand-off and triage are two fundamental problems in health care.\nOften doctors must painstakingly summarize complex findings to efficiently\ncommunicate with specialists and quickly make decisions on which patients have\nthe most urgent cases. In pursuit of these challenges, we present (1) a model\nwith state-of-art radiology report summarization performance using (2) a novel\nmethod for augmenting medical data, and (3) an analysis of the model\nlimitations and radiology knowledge gain. We also provide a data processing\npipeline for future models developed on the the MIMIC CXR dataset. Our best\nperforming model was a fine-tuned BERT-to-BERT encoder-decoder with 58.75/100\nROUGE-L F1, which outperformed specialized checkpoints with more sophisticated\nattention mechanisms. We investigate these aspects in this work.\n", "date": "2024-05-10", "categories": []}
{"id": "2409.18170", "title": "Evaluation of Large Language Models for Summarization Tasks in the\n  Medical Domain: A Narrative Review", "abstract": "  Large Language Models have advanced clinical Natural Language Generation,\ncreating opportunities to manage the volume of medical text. However, the\nhigh-stakes nature of medicine requires reliable evaluation, which remains a\nchallenge. In this narrative review, we assess the current evaluation state for\nclinical summarization tasks and propose future directions to address the\nresource constraints of expert human evaluation.\n", "date": "2024-09-26", "categories": []}
{"id": "2409.18290", "title": "Retrospective Comparative Analysis of Prostate Cancer In-Basket\n  Messages: Responses from Closed-Domain LLM vs. Clinical Teams", "abstract": "  In-basket message interactions play a crucial role in physician-patient\ncommunication, occurring during all phases (pre-, during, and post) of a\npatient's care journey. However, responding to these patients' inquiries has\nbecome a significant burden on healthcare workflows, consuming considerable\ntime for clinical care teams. To address this, we introduce RadOnc-GPT, a\nspecialized Large Language Model (LLM) powered by GPT-4 that has been designed\nwith a focus on radiotherapeutic treatment of prostate cancer with advanced\nprompt engineering, and specifically designed to assist in generating\nresponses. We integrated RadOnc-GPT with patient electronic health records\n(EHR) from both the hospital-wide EHR database and an internal,\nradiation-oncology-specific database. RadOnc-GPT was evaluated on 158\npreviously recorded in-basket message interactions. Quantitative natural\nlanguage processing (NLP) analysis and two grading studies with clinicians and\nnurses were used to assess RadOnc-GPT's responses. Our findings indicate that\nRadOnc-GPT slightly outperformed the clinical care team in \"Clarity\" and\n\"Empathy,\" while achieving comparable scores in \"Completeness\" and\n\"Correctness.\" RadOnc-GPT is estimated to save 5.2 minutes per message for\nnurses and 2.4 minutes for clinicians, from reading the inquiry to sending the\nresponse. Employing RadOnc-GPT for in-basket message draft generation has the\npotential to alleviate the workload of clinical care teams and reduce\nhealthcare costs by producing high-quality, timely responses.\n", "date": "2024-09-26", "categories": []}
{"id": "2409.17683", "title": "Zero- and Few-shot Named Entity Recognition and Text Expansion in\n  Medication Prescriptions using ChatGPT", "abstract": "  Introduction: Medication prescriptions are often in free text and include a\nmix of two languages, local brand names, and a wide range of idiosyncratic\nformats and abbreviations. Large language models (LLMs) have shown promising\nability to generate text in response to input prompts. We use ChatGPT 3.5 to\nautomatically structure and expand medication statements in discharge summaries\nand thus make them easier to interpret for people and machines. Methods:\nNamed-entity Recognition (NER) and Text Expansion (EX) are used in a zero- and\nfew-shot setting with different prompt strategies. 100 medication statements\nwere manually annotated and curated. NER performance was measured by using\nstrict and partial matching. For the task EX, two experts interpreted the\nresults by assessing semantic equivalence between original and expanded\nstatements. The model performance was measured by precision, recall, and F1\nscore. Results: For NER, the best-performing prompt reached an average F1 score\nof 0.94 in the test set. For EX, the few-shot prompt showed superior\nperformance among other prompts, with an average F1 score of 0.87. Conclusion:\nOur study demonstrates good performance for NER and EX tasks in free-text\nmedication statements using ChatGPT. Compared to a zero-shot baseline, a\nfew-shot approach prevented the system from hallucinating, which would be\nunacceptable when processing safety-relevant medication data.\n", "date": "2024-09-26", "categories": []}
{"id": "2409.16307", "title": "DeepScore: A Comprehensive Approach to Measuring Quality in AI-Generated\n  Clinical Documentation", "abstract": "  Medical practitioners are rapidly adopting generative AI solutions for\nclinical documentation, leading to significant time savings and reduced stress.\nHowever, evaluating the quality of AI-generated documentation is a complex and\nongoing challenge. This paper presents an overview of DeepScribe's\nmethodologies for assessing and managing note quality, focusing on various\nmetrics and the composite \"DeepScore\", an overall index of quality and\naccuracy. These methodologies aim to enhance the quality of patient care\ndocumentation through accountability and continuous improvement.\n", "date": "2024-09-10", "categories": []}
{"id": "2409.16563", "title": "Enhancing disease detection in radiology reports through fine-tuning\n  lightweight LLM on weak labels", "abstract": "  Despite significant progress in applying large language models (LLMs) to the\nmedical domain, several limitations still prevent them from practical\napplications. Among these are the constraints on model size and the lack of\ncohort-specific labeled datasets. In this work, we investigated the potential\nof improving a lightweight LLM, such as Llama 3.1-8B, through fine-tuning with\ndatasets using synthetic labels. Two tasks are jointly trained by combining\ntheir respective instruction datasets. When the quality of the task-specific\nsynthetic labels is relatively high (e.g., generated by GPT4- o), Llama 3.1-8B\nachieves satisfactory performance on the open-ended disease detection task,\nwith a micro F1 score of 0.91. Conversely, when the quality of the\ntask-relevant synthetic labels is relatively low (e.g., from the MIMIC-CXR\ndataset), fine-tuned Llama 3.1-8B is able to surpass its noisy teacher labels\n(micro F1 score of 0.67 v.s. 0.63) when calibrated against curated labels,\nindicating the strong inherent underlying capability of the model. These\nfindings demonstrate the potential of fine-tuning LLMs with synthetic labels,\noffering a promising direction for future research on LLM specialization in the\nmedical domain.\n", "date": "2024-09-25", "categories": []}
{"id": "2409.16603", "title": "Overview of the First Shared Task on Clinical Text Generation: RRG24 and\n  \"Discharge Me!\"", "abstract": "  Recent developments in natural language generation have tremendous\nimplications for healthcare. For instance, state-of-the-art systems could\nautomate the generation of sections in clinical reports to alleviate physician\nworkload and streamline hospital documentation. To explore these applications,\nwe present a shared task consisting of two subtasks: (1) Radiology Report\nGeneration (RRG24) and (2) Discharge Summary Generation (\"Discharge Me!\").\nRRG24 involves generating the 'Findings' and 'Impression' sections of radiology\nreports given chest X-rays. \"Discharge Me!\" involves generating the 'Brief\nHospital Course' and 'Discharge Instructions' sections of discharge summaries\nfor patients admitted through the emergency department. \"Discharge Me!\"\nsubmissions were subsequently reviewed by a team of clinicians. Both tasks\nemphasize the goal of reducing clinician burnout and repetitive workloads by\ngenerating documentation. We received 201 submissions from across 8 teams for\nRRG24, and 211 submissions from across 16 teams for \"Discharge Me!\".\n", "date": "2024-09-25", "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": "2409.15188", "title": "PALLM: Evaluating and Enhancing PALLiative Care Conversations with Large\n  Language Models", "abstract": "  Effective patient-provider communication is crucial in clinical care,\ndirectly impacting patient outcomes and quality of life. Traditional evaluation\nmethods, such as human ratings, patient feedback, and provider\nself-assessments, are often limited by high costs and scalability issues.\nAlthough existing natural language processing (NLP) techniques show promise,\nthey struggle with the nuances of clinical communication and require sensitive\nclinical data for training, reducing their effectiveness in real-world\napplications. Emerging large language models (LLMs) offer a new approach to\nassessing complex communication metrics, with the potential to advance the\nfield through integration into passive sensing and just-in-time intervention\nsystems. This study explores LLMs as evaluators of palliative care\ncommunication quality, leveraging their linguistic, in-context learning, and\nreasoning capabilities. Specifically, using simulated scripts crafted and\nlabeled by healthcare professionals, we test proprietary models (e.g., GPT-4)\nand fine-tune open-source LLMs (e.g., LLaMA2) with a synthetic dataset\ngenerated by GPT-4 to evaluate clinical conversations, to identify key metrics\nsuch as `understanding' and `empathy'. Our findings demonstrated LLMs' superior\nperformance in evaluating clinical communication, providing actionable feedback\nwith reasoning, and demonstrating the feasibility and practical viability of\ndeveloping in-house LLMs. This research highlights LLMs' potential to enhance\npatient-provider interactions and lays the groundwork for downstream steps in\ndeveloping LLM-empowered clinical health systems.\n", "date": "2024-09-23", "categories": []}
{"id": "2409.15368", "title": "MedCodER: A Generative AI Assistant for Medical Coding", "abstract": "  Medical coding is essential for standardizing clinical data and communication\nbut is often time-consuming and prone to errors. Traditional Natural Language\nProcessing (NLP) methods struggle with automating coding due to the large label\nspace, lengthy text inputs, and the absence of supporting evidence annotations\nthat justify code selection. Recent advancements in Generative Artificial\nIntelligence (AI) offer promising solutions to these challenges. In this work,\nwe introduce MedCodER, a Generative AI framework for automatic medical coding\nthat leverages extraction, retrieval, and re-ranking techniques as core\ncomponents. MedCodER achieves a micro-F1 score of 0.60 on International\nClassification of Diseases (ICD) code prediction, significantly outperforming\nstate-of-the-art methods. Additionally, we present a new dataset containing\nmedical records annotated with disease diagnoses, ICD codes, and supporting\nevidence texts (https://doi.org/10.5281/zenodo.13308316). Ablation tests\nconfirm that MedCodER's performance depends on the integration of each of its\naforementioned components, as performance declines when these components are\nevaluated in isolation.\n", "date": "2024-09-18", "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": "2409.14357", "title": "Using Natural Language Processing to find Indication for Burnout with\n  Text Classification: From Online Data to Real-World Data", "abstract": "  Burnout, classified as a syndrome in the ICD-11, arises from chronic\nworkplace stress that has not been effectively managed. It is characterized by\nexhaustion, cynicism, and reduced professional efficacy, and estimates of its\nprevalence vary significantly due to inconsistent measurement methods. Recent\nadvancements in Natural Language Processing (NLP) and machine learning offer\npromising tools for detecting burnout through textual data analysis, with\nstudies demonstrating high predictive accuracy. This paper contributes to\nburnout detection in German texts by: (a) collecting an anonymous real-world\ndataset including free-text answers and Oldenburg Burnout Inventory (OLBI)\nresponses; (b) demonstrating the limitations of a GermanBERT-based classifier\ntrained on online data; (c) presenting two versions of a curated\nBurnoutExpressions dataset, which yielded models that perform well in\nreal-world applications; and (d) providing qualitative insights from an\ninterdisciplinary focus group on the interpretability of AI models used for\nburnout detection. Our findings emphasize the need for greater collaboration\nbetween AI researchers and clinical experts to refine burnout detection models.\nAdditionally, more real-world data is essential to validate and enhance the\neffectiveness of current AI methods developed in NLP research, which are often\nbased on data automatically scraped from online sources and not evaluated in a\nreal-world context. This is essential for ensuring AI tools are well suited for\npractical applications.\n", "date": "2024-09-22", "categories": []}
{"id": "2409.14579", "title": "Medical Concept Normalization in a Low-Resource Setting", "abstract": "  In the field of biomedical natural language processing, medical concept\nnormalization is a crucial task for accurately mapping mentions of concepts to\na large knowledge base. However, this task becomes even more challenging in\nlow-resource settings, where limited data and resources are available. In this\nthesis, I explore the challenges of medical concept normalization in a\nlow-resource setting. Specifically, I investigate the shortcomings of current\nmedical concept normalization methods applied to German lay texts. Since there\nis no suitable dataset available, a dataset consisting of posts from a German\nmedical online forum is annotated with concepts from the Unified Medical\nLanguage System. The experiments demonstrate that multilingual\nTransformer-based models are able to outperform string similarity methods. The\nuse of contextual information to improve the normalization of lay mentions is\nalso examined, but led to inferior results. Based on the results of the best\nperforming model, I present a systematic error analysis and lay out potential\nimprovements to mitigate frequent errors.\n", "date": "2024-09-06", "categories": []}
{"id": "2409.14907", "title": "Knowledge Planning in Large Language Models for Domain-Aligned\n  Counseling Summarization", "abstract": "  In mental health counseling, condensing dialogues into concise and relevant\nsummaries (aka counseling notes) holds pivotal significance. Large Language\nModels (LLMs) exhibit remarkable capabilities in various generative tasks;\nhowever, their adaptation to domain-specific intricacies remains challenging,\nespecially within mental health contexts. Unlike standard LLMs, mental health\nexperts first plan to apply domain knowledge in writing summaries. Our work\nenhances LLMs' ability by introducing a novel planning engine to orchestrate\nstructuring knowledge alignment. To achieve high-order planning, we divide\nknowledge encapsulation into two major phases: (i) holding dialogue structure\nand (ii) incorporating domain-specific knowledge. We employ a planning engine\non Llama-2, resulting in a novel framework, PIECE. Our proposed system employs\nknowledge filtering-cum-scaffolding to encapsulate domain knowledge.\nAdditionally, PIECE leverages sheaf convolution learning to enhance its\nunderstanding of the dialogue's structural nuances. We compare PIECE with 14\nbaseline methods and observe a significant improvement across ROUGE and Bleurt\nscores. Further, expert evaluation and analyses validate the generation quality\nto be effective, sometimes even surpassing the gold standard. We further\nbenchmark PIECE with other LLMs and report improvement, including Llama-2\n(+2.72%), Mistral (+2.04%), and Zephyr (+1.59%), to justify the\ngeneralizability of the planning engine.\n", "date": "2024-09-23", "categories": []}
{"id": "2409.14988", "title": "Beyond Fine-tuning: Unleashing the Potential of Continuous Pretraining\n  for Clinical LLMs", "abstract": "  Large Language Models (LLMs) have demonstrated significant potential in\ntransforming clinical applications. In this study, we investigate the efficacy\nof four techniques in adapting LLMs for clinical use-cases: continuous\npretraining, instruct fine-tuning, NEFTune, and prompt engineering. We employ\nthese methods on Mistral 7B and Mixtral 8x7B models, leveraging a large-scale\nclinical pretraining dataset of 50 billion tokens and an instruct fine-tuning\ndataset of 500 million tokens. Our evaluation across various clinical tasks\nreveals the impact of each technique. While continuous pretraining beyond 250\nbillion tokens yields marginal improvements on its own, it establishes a strong\nfoundation for instruct fine-tuning. Notably, NEFTune, designed primarily to\nenhance generation quality, surprisingly demonstrates additional gains on our\nbenchmark. Complex prompt engineering methods further enhance performance.\nThese findings show the importance of tailoring fine-tuning strategies and\nexploring innovative techniques to optimize LLM performance in the clinical\ndomain.\n", "date": "2024-09-23", "categories": []}
{"id": "2409.15027", "title": "Generative LLM Powered Conversational AI Application for Personalized\n  Risk Assessment: A Case Study in COVID-19", "abstract": "  Large language models (LLMs) have shown remarkable capabilities in various\nnatural language tasks and are increasingly being applied in healthcare\ndomains. This work demonstrates a new LLM-powered disease risk assessment\napproach via streaming human-AI conversation, eliminating the need for\nprogramming required by traditional machine learning approaches. In a COVID-19\nseverity risk assessment case study, we fine-tune pre-trained generative LLMs\n(e.g., Llama2-7b and Flan-t5-xl) using a few shots of natural language\nexamples, comparing their performance with traditional classifiers (i.e.,\nLogistic Regression, XGBoost, Random Forest) that are trained de novo using\ntabular data across various experimental settings. We develop a mobile\napplication that uses these fine-tuned LLMs as its generative AI (GenAI) core\nto facilitate real-time interaction between clinicians and patients, providing\nno-code risk assessment through conversational interfaces. This integration not\nonly allows for the use of streaming Questions and Answers (QA) as inputs but\nalso offers personalized feature importance analysis derived from the LLM's\nattention layers, enhancing the interpretability of risk assessments. By\nachieving high Area Under the Curve (AUC) scores with a limited number of\nfine-tuning samples, our results demonstrate the potential of generative LLMs\nto outperform discriminative classification methods in low-data regimes,\nhighlighting their real-world adaptability and effectiveness. This work aims to\nfill the existing gap in leveraging generative LLMs for interactive no-code\nrisk assessment and to encourage further research in this emerging field.\n", "date": "2024-09-23", "categories": []}
{"id": "2409.12541", "title": "Profiling Patient Transcript Using Large Language Model Reasoning\n  Augmentation for Alzheimer's Disease Detection", "abstract": "  Alzheimer's disease (AD) stands as the predominant cause of dementia,\ncharacterized by a gradual decline in speech and language capabilities. Recent\ndeep-learning advancements have facilitated automated AD detection through\nspontaneous speech. However, common transcript-based detection methods directly\nmodel text patterns in each utterance without a global view of the patient's\nlinguistic characteristics, resulting in limited discriminability and\ninterpretability. Despite the enhanced reasoning abilities of large language\nmodels (LLMs), there remains a gap in fully harnessing the reasoning ability to\nfacilitate AD detection and model interpretation. Therefore, we propose a\npatient-level transcript profiling framework leveraging LLM-based reasoning\naugmentation to systematically elicit linguistic deficit attributes. The\nsummarized embeddings of the attributes are integrated into an Albert model for\nAD detection. The framework achieves 8.51\\% ACC and 8.34\\% F1 improvements on\nthe ADReSS dataset compared to the baseline without reasoning augmentation. Our\nfurther analysis shows the effectiveness of our identified linguistic deficit\nattributes and the potential to use LLM for AD detection interpretation.\n", "date": "2024-09-19", "categories": []}
{"id": "2409.10576", "title": "Language Models and Retrieval Augmented Generation for Automated\n  Structured Data Extraction from Diagnostic Reports", "abstract": "  Purpose: To develop and evaluate an automated system for extracting\nstructured clinical information from unstructured radiology and pathology\nreports using open-weights large language models (LMs) and retrieval augmented\ngeneration (RAG), and to assess the effects of model configuration variables on\nextraction performance. Methods and Materials: The study utilized two datasets:\n7,294 radiology reports annotated for Brain Tumor Reporting and Data System\n(BT-RADS) scores and 2,154 pathology reports annotated for isocitrate\ndehydrogenase (IDH) mutation status. An automated pipeline was developed to\nbenchmark the performance of various LMs and RAG configurations. The impact of\nmodel size, quantization, prompting strategies, output formatting, and\ninference parameters was systematically evaluated. Results: The best performing\nmodels achieved over 98% accuracy in extracting BT-RADS scores from radiology\nreports and over 90% for IDH mutation status extraction from pathology reports.\nThe top model being medical fine-tuned llama3. Larger, newer, and domain\nfine-tuned models consistently outperformed older and smaller models. Model\nquantization had minimal impact on performance. Few-shot prompting\nsignificantly improved accuracy. RAG improved performance for complex pathology\nreports but not for shorter radiology reports. Conclusions: Open LMs\ndemonstrate significant potential for automated extraction of structured\nclinical data from unstructured clinical reports with local privacy-preserving\napplication. Careful model selection, prompt engineering, and semi-automated\noptimization using annotated data are critical for optimal performance. These\napproaches could be reliable enough for practical use in research workflows,\nhighlighting the potential for human-machine collaboration in healthcare data\nextraction.\n", "date": "2024-09-15", "categories": []}
{"id": "2409.10829", "title": "ReXErr: Synthesizing Clinically Meaningful Errors in Diagnostic\n  Radiology Reports", "abstract": "  Accurately interpreting medical images and writing radiology reports is a\ncritical but challenging task in healthcare. Both human-written and\nAI-generated reports can contain errors, ranging from clinical inaccuracies to\nlinguistic mistakes. To address this, we introduce ReXErr, a methodology that\nleverages Large Language Models to generate representative errors within chest\nX-ray reports. Working with board-certified radiologists, we developed error\ncategories that capture common mistakes in both human and AI-generated reports.\nOur approach uses a novel sampling scheme to inject diverse errors while\nmaintaining clinical plausibility. ReXErr demonstrates consistency across error\ncategories and produces errors that closely mimic those found in real-world\nscenarios. This method has the potential to aid in the development and\nevaluation of report correction algorithms, potentially enhancing the quality\nand reliability of radiology reporting.\n", "date": "2024-09-17", "categories": []}
{"id": "2401.13512", "title": "Can GPT-3.5 Generate and Code Discharge Summaries?", "abstract": "  Objective: To investigate GPT-3.5 in generating and coding medical documents\nwith ICD-10 codes for data augmentation on low-resources labels.\n  Materials and Methods: Employing GPT-3.5 we generated and coded 9,606\ndischarge summaries based on lists of ICD-10 code descriptions of patients with\ninfrequent (generation) codes within the MIMIC-IV dataset. Combined with the\nbaseline training set, this formed an augmented training set. Neural coding\nmodels were trained on baseline and augmented data and evaluated on a MIMIC-IV\ntest set. We report micro- and macro-F1 scores on the full codeset, generation\ncodes, and their families. Weak Hierarchical Confusion Matrices were employed\nto determine within-family and outside-of-family coding errors in the latter\ncodesets. The coding performance of GPT-3.5 was evaluated both on prompt-guided\nself-generated data and real MIMIC-IV data. Clinical professionals evaluated\nthe clinical acceptability of the generated documents.\n  Results: Augmentation slightly hinders the overall performance of the models\nbut improves performance for the generation candidate codes and their families,\nincluding one unseen in the baseline training data. Augmented models display\nlower out-of-family error rates. GPT-3.5 can identify ICD-10 codes by the\nprompted descriptions, but performs poorly on real data. Evaluators note the\ncorrectness of generated concepts while suffering in variety, supporting\ninformation, and narrative.\n  Discussion and Conclusion: GPT-3.5 alone is unsuitable for ICD-10 coding.\nAugmentation positively affects generation code families but mainly benefits\ncodes with existing examples. Augmentation reduces out-of-family errors.\nDischarge summaries generated by GPT-3.5 state prompted concepts correctly but\nlack variety, and authenticity in narratives. They are unsuitable for clinical\npractice.\n", "date": "2024-01-24", "categories": []}
{"id": "2409.08523", "title": "Eir: Thai Medical Large Language Models", "abstract": "  We present Eir-8B, a large language model with 8 billion parameters,\nspecifically designed to enhance the accuracy of handling medical tasks in the\nThai language. This model focuses on providing clear and easy-to-understand\nanswers for both healthcare professionals and patients, thereby improving the\nefficiency of diagnosis and treatment processes. Human evaluation was conducted\nto ensure that the model adheres to care standards and provides unbiased\nanswers.\n  To prioritize data security, the model is deployed within the hospital's\ninternal network, ensuring both high security and faster processing speeds. The\ninternal API connection is secured with encryption and strict authentication\nmeasures to prevent data leaks and unauthorized access.\n  We evaluated several open-source large language models with 8 billion\nparameters on four medical benchmarks: MedQA, MedMCQA, PubMedQA, and the\nmedical subset of MMLU. The best-performing baselines were used to develop\nEir-8B. Our evaluation employed multiple questioning strategies, including\nzero-shot, few-shot, chain-of-thought reasoning, and ensemble/self-consistency\nvoting methods. Our model outperformed commercially available Thai-language\nlarge language models by more than 10%. In addition, we developed enhanced\nmodel testing tailored for clinical use in Thai across 18 clinical tasks, where\nour model exceeded GPT-4o performance by more than 11%.\n", "date": "2024-09-13", "categories": []}
{"id": "2409.09501", "title": "Synthetic4Health: Generating Annotated Synthetic Clinical Letters", "abstract": "  Since clinical letters contain sensitive information, clinical-related\ndatasets can not be widely applied in model training, medical research, and\nteaching. This work aims to generate reliable, various, and de-identified\nsynthetic clinical letters. To achieve this goal, we explored different\npre-trained language models (PLMs) for masking and generating text. After that,\nwe worked on Bio\\_ClinicalBERT, a high-performing model, and experimented with\ndifferent masking strategies. Both qualitative and quantitative methods were\nused for evaluation. Additionally, a downstream task, Named Entity Recognition\n(NER), was also implemented to assess the usability of these synthetic letters.\n  The results indicate that 1) encoder-only models outperform encoder-decoder\nmodels. 2) Among encoder-only models, those trained on general corpora perform\ncomparably to those trained on clinical data when clinical information is\npreserved. 3) Additionally, preserving clinical entities and document structure\nbetter aligns with our objectives than simply fine-tuning the model. 4)\nFurthermore, different masking strategies can impact the quality of synthetic\nclinical letters. Masking stopwords has a positive impact, while masking nouns\nor verbs has a negative effect. 5) For evaluation, BERTScore should be the\nprimary quantitative evaluation metric, with other metrics serving as\nsupplementary references. 6) Contextual information does not significantly\nimpact the models' understanding, so the synthetic clinical letters have the\npotential to replace the original ones in downstream tasks.\n", "date": "2024-09-14", "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": "2409.10403", "title": "A Knowledge-Enhanced Disease Diagnosis Method Based on Prompt Learning\n  and BERT Integration", "abstract": "  This paper proposes a knowledge-enhanced disease diagnosis method based on a\nprompt learning framework. The method retrieves structured knowledge from\nexternal knowledge graphs related to clinical cases, encodes it, and injects it\ninto the prompt templates to enhance the language model's understanding and\nreasoning capabilities for the task.We conducted experiments on three public\ndatasets: CHIP-CTC, IMCS-V2-NER, and KUAKE-QTR. The results show that the\nproposed method significantly outperforms existing models across multiple\nevaluation metrics, with an F1 score improvement of 2.4% on the CHIP-CTC\ndataset, 3.1% on the IMCS-V2-NER dataset,and 4.2% on the KUAKE-QTR dataset.\nAdditionally,ablation studies confirmed the critical role of the knowledge\ninjection module,as the removal of this module resulted in a significant drop\nin F1 score. The experimental results demonstrate that the proposed method not\nonly effectively improves the accuracy of disease diagnosis but also enhances\nthe interpretability of the predictions, providing more reliable support and\nevidence for clinical diagnosis.\n", "date": "2024-09-16", "categories": []}
{"id": "2409.08483", "title": "A BERT-Based Summarization approach for depression detection", "abstract": "  Depression is a globally prevalent mental disorder with potentially severe\nrepercussions if not addressed, especially in individuals with recurrent\nepisodes. Prior research has shown that early intervention has the potential to\nmitigate or alleviate symptoms of depression. However, implementing such\ninterventions in a real-world setting may pose considerable challenges. A\npromising strategy involves leveraging machine learning and artificial\nintelligence to autonomously detect depression indicators from diverse data\nsources. One of the most widely available and informative data sources is text,\nwhich can reveal a person's mood, thoughts, and feelings. In this context,\nvirtual agents programmed to conduct interviews using clinically validated\nquestionnaires, such as those found in the DAIC-WOZ dataset, offer a robust\nmeans for depression detection through linguistic analysis. Utilizing\nBERT-based models, which are powerful and versatile yet use fewer resources\nthan contemporary large language models, to convert text into numerical\nrepresentations significantly enhances the precision of depression diagnosis.\nThese models adeptly capture complex semantic and syntactic nuances, improving\nthe detection accuracy of depressive symptoms. Given the inherent limitations\nof these models concerning text length, our study proposes text summarization\nas a preprocessing technique to diminish the length and intricacies of input\ntexts. Implementing this method within our uniquely developed framework for\nfeature extraction and classification yielded an F1-score of 0.67 on the test\nset surpassing all prior benchmarks and 0.81 on the validation set exceeding\nmost previous results on the DAIC-WOZ dataset. Furthermore, we have devised a\ndepression lexicon to assess summary quality and relevance. This lexicon\nconstitutes a valuable asset for ongoing research in depression detection.\n", "date": "2024-09-13", "categories": []}
{"id": "2409.06164", "title": "Deep Learning and Large Language Models for Audio and Text Analysis in\n  Predicting Suicidal Acts in Chinese Psychological Support Hotlines", "abstract": "  Suicide is a pressing global issue, demanding urgent and effective preventive\ninterventions. Among the various strategies in place, psychological support\nhotlines had proved as a potent intervention method. Approximately two million\npeople in China attempt suicide annually, with many individuals making multiple\nattempts. Prompt identification and intervention for high-risk individuals are\ncrucial to preventing tragedies. With the rapid advancement of artificial\nintelligence (AI), especially the development of large-scale language models\n(LLMs), new technological tools have been introduced to the field of mental\nhealth. This study included 1284 subjects, and was designed to validate whether\ndeep learning models and LLMs, using audio and transcribed text from support\nhotlines, can effectively predict suicide risk. We proposed a simple LLM-based\npipeline that first summarizes transcribed text from approximately one hour of\nspeech to extract key features, and then predict suicidial bahaviours in the\nfuture. We compared our LLM-based method with the traditional manual scale\napproach in a clinical setting and with five advanced deep learning models.\nSurprisingly, the proposed simple LLM pipeline achieved strong performance on a\ntest set of 46 subjects, with an F1 score of 76\\% when combined with manual\nscale rating. This is 7\\% higher than the best speech-based deep learning\nmodels and represents a 27.82\\% point improvement in F1 score compared to using\nthe manual scale apporach alone. Our study explores new applications of LLMs\nand demonstrates their potential for future use in suicide prevention efforts.\n", "date": "2024-09-10", "categories": []}
{"id": "2405.02664", "title": "MedPromptExtract (Medical Data Extraction Tool): Anonymization and\n  Hi-fidelity Automated data extraction using NLP and prompt engineering", "abstract": "  Introduction: The labour-intensive nature of data extraction from sources\nlike discharge summaries (DS) poses significant obstacles to the digitisation\nof medical records particularly for low- and middle-income countries (LMICs).\nIn this paper we present a completely automated method MedPromptExtract to\nefficiently extract data from DS while maintaining confidentiality. Methods:\nThe source of data was Discharge Summaries (DS) from Kokilaben Dhirubhai Ambani\nHospital (KDAH) of patients having Acute Kidney Injury (AKI). A pre-existing\ntool EIGEN which leverages semi-supervised learning techniques for\nhigh-fidelity information extraction was used to anonymize the DS, Natural\nLanguage Processing (NLP) was used to extract data from regular fields. We used\nPrompt Engineering and Large Language Model(LLM) to extract custom clinical\ninformation from free flowing text describing the patients stay in the\nhospital. Twelve features associated with occurrence of AKI were extracted. The\nLLM responses were validated against clinicians annotations. Results: The\nMedPromptExtracttool first subjected DS to the anonymization pipeline which\ntook three seconds per summary. Successful anonymization was verified by\nclinicians, thereafter NLP pipeline extracted structured text from the\nanonymized pdfs at the rate of 0.2 seconds per summary with 100%\naccuracy.Finally DS were analysed by the LLM pipeline using Gemini Pro for the\ntwelve features. Accuracy metrics were calculated by comparing model responses\nto clinicians annotations with seven features achieving AUCs above 0.9,\nindicating high fidelity of the extraction process. Conclusion:\nMedPromptExtract serves as an automated adaptable tool for efficient data\nextraction from medical records with a dynamic user interface. Keywords:\nDigitizing Medical Records, Automated Anonymisation, Information Retrieval,\nLarge Language Models, Prompt Engineering\n", "date": "2024-05-04", "categories": []}
{"id": "2409.03905", "title": "CACER: Clinical Concept Annotations for Cancer Events and Relations", "abstract": "  Clinical notes contain unstructured representations of patient histories,\nincluding the relationships between medical problems and prescription drugs. To\ninvestigate the relationship between cancer drugs and their associated symptom\nburden, we extract structured, semantic representations of medical problem and\ndrug information from the clinical narratives of oncology notes. We present\nClinical Concept Annotations for Cancer Events and Relations (CACER), a novel\ncorpus with fine-grained annotations for over 48,000 medical problems and drug\nevents and 10,000 drug-problem and problem-problem relations. Leveraging CACER,\nwe develop and evaluate transformer-based information extraction (IE) models\nsuch as BERT, Flan-T5, Llama3, and GPT-4 using fine-tuning and in-context\nlearning (ICL). In event extraction, the fine-tuned BERT and Llama3 models\nachieved the highest performance at 88.2-88.0 F1, which is comparable to the\ninter-annotator agreement (IAA) of 88.4 F1. In relation extraction, the\nfine-tuned BERT, Flan-T5, and Llama3 achieved the highest performance at\n61.8-65.3 F1. GPT-4 with ICL achieved the worst performance across both tasks.\nThe fine-tuned models significantly outperformed GPT-4 in ICL, highlighting the\nimportance of annotated training data and model optimization. Furthermore, the\nBERT models performed similarly to Llama3. For our task, LLMs offer no\nperformance advantage over the smaller BERT models. The results emphasize the\nneed for annotated training data to optimize models. Multiple fine-tuned\ntransformer models achieved performance comparable to IAA for several\nextraction tasks.\n", "date": "2024-09-05", "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": "2409.03238", "title": "Preserving Empirical Probabilities in BERT for Small-sample Clinical\n  Entity Recognition", "abstract": "  Named Entity Recognition (NER) encounters the challenge of unbalanced labels,\nwhere certain entity types are overrepresented while others are\nunderrepresented in real-world datasets. This imbalance can lead to biased\nmodels that perform poorly on minority entity classes, impeding accurate and\nequitable entity recognition. This paper explores the effects of unbalanced\nentity labels of the BERT-based pre-trained model. We analyze the different\nmechanisms of loss calculation and loss propagation for the task of token\nclassification on randomized datasets. Then we propose ways to improve the\ntoken classification for the highly imbalanced task of clinical entity\nrecognition.\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": "2409.03440", "title": "Rx Strategist: Prescription Verification using LLM Agents System", "abstract": "  To protect patient safety, modern pharmaceutical complexity demands strict\nprescription verification. We offer a new approach - Rx Strategist - that makes\nuse of knowledge graphs and different search strategies to enhance the power of\nLarge Language Models (LLMs) inside an agentic framework. This multifaceted\ntechnique allows for a multi-stage LLM pipeline and reliable information\nretrieval from a custom-built active ingredient database. Different facets of\nprescription verification, such as indication, dose, and possible drug\ninteractions, are covered in each stage of the pipeline. We alleviate the\ndrawbacks of monolithic LLM techniques by spreading reasoning over these\nstages, improving correctness and reliability while reducing memory demands.\nOur findings demonstrate that Rx Strategist surpasses many current LLMs,\nachieving performance comparable to that of a highly experienced clinical\npharmacist. In the complicated world of modern medications, this combination of\nLLMs with organized knowledge and sophisticated search methods presents a\nviable avenue for reducing prescription errors and enhancing patient outcomes.\n", "date": "2024-09-05", "categories": []}
{"id": "2408.02056", "title": "MedSyn: LLM-based Synthetic Medical Text Generation Framework", "abstract": "  Generating synthetic text addresses the challenge of data availability in\nprivacy-sensitive domains such as healthcare. This study explores the\napplicability of synthetic data in real-world medical settings. We introduce\nMedSyn, a novel medical text generation framework that integrates large\nlanguage models with a Medical Knowledge Graph (MKG). We use MKG to sample\nprior medical information for the prompt and generate synthetic clinical notes\nwith GPT-4 and fine-tuned LLaMA models. We assess the benefit of synthetic data\nthrough application in the ICD code prediction task. Our research indicates\nthat synthetic data can increase the classification accuracy of vital and\nchallenging codes by up to 17.8% compared to settings without synthetic data.\nFurthermore, to provide new data for further research in the healthcare domain,\nwe present the largest open-source synthetic dataset of clinical notes for the\nRussian language, comprising over 41k samples covering 219 ICD-10 codes.\n", "date": "2024-08-04", "categories": []}
{"id": "2409.00164", "title": "Facilitating phenotyping from clinical texts: the medkit library", "abstract": "  Phenotyping consists in applying algorithms to identify individuals\nassociated with a specific, potentially complex, trait or condition, typically\nout of a collection of Electronic Health Records (EHRs). Because a lot of the\nclinical information of EHRs are lying in texts, phenotyping from text takes an\nimportant role in studies that rely on the secondary use of EHRs. However, the\nheterogeneity and highly specialized aspect of both the content and form of\nclinical texts makes this task particularly tedious, and is the source of time\nand cost constraints in observational studies. To facilitate the development,\nevaluation and reproductibility of phenotyping pipelines, we developed an\nopen-source Python library named medkit. It enables composing data processing\npipelines made of easy-to-reuse software bricks, named medkit operations. In\naddition to the core of the library, we share the operations and pipelines we\nalready developed and invite the phenotyping community for their reuse and\nenrichment. medkit is available at https://github.com/medkit-lib/medkit\n", "date": "2024-08-30", "categories": []}
{"id": "2409.02608", "title": "A Medical Multimodal Large Language Model for Pediatric Pneumonia", "abstract": "  Pediatric pneumonia is the leading cause of death among children under five\nyears worldwide, imposing a substantial burden on affected families. Currently,\nthere are three significant hurdles in diagnosing and treating pediatric\npneumonia. Firstly, pediatric pneumonia shares similar symptoms with other\nrespiratory diseases, making rapid and accurate differential diagnosis\nchallenging. Secondly, primary hospitals often lack sufficient medical\nresources and experienced doctors. Lastly, providing personalized diagnostic\nreports and treatment recommendations is labor-intensive and time-consuming. To\ntackle these challenges, we proposed a Medical Multimodal Large Language Model\nfor Pediatric Pneumonia (P2Med-MLLM). It was capable of handling diverse\nclinical tasks, such as generating free-text radiology reports and medical\nrecords within a unified framework. Specifically, P2Med-MLLM can process both\npure text and image-text data, trained on an extensive and large-scale dataset\n(P2Med-MD), including real clinical information from 163,999 outpatient and\n8,684 inpatient cases. This dataset comprised 2D chest X-ray images, 3D chest\nCT images, corresponding radiology reports, and outpatient and inpatient\nrecords. We designed a three-stage training strategy to enable P2Med-MLLM to\ncomprehend medical knowledge and follow instructions for various clinical\ntasks. To rigorously evaluate P2Med-MLLM's performance, we developed\nP2Med-MBench, a benchmark consisting of 642 meticulously verified samples by\npediatric pulmonology specialists, covering six clinical decision-support tasks\nand a balanced variety of diseases. The automated scoring results demonstrated\nthe superiority of P2Med-MLLM. This work plays a crucial role in assisting\nprimary care doctors with prompt disease diagnosis and treatment planning,\nreducing severe symptom mortality rates, and optimizing the allocation of\nmedical resources.\n", "date": "2024-09-04", "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": "2404.11132", "title": "A Novel ICD Coding Method Based on Associated and Hierarchical Code\n  Description Distillation", "abstract": "  ICD(International Classification of Diseases) coding involves assigning ICD\ncodes to patients visit based on their medical notes. ICD coding is a\nchallenging multilabel text classification problem due to noisy medical\ndocument inputs. Recent advancements in automated ICD coding have enhanced\nperformance by integrating additional data and knowledge bases with the\nencoding of medical notes and codes. However, most of them ignore the code\nhierarchy, leading to improper code assignments. To address these problems, we\npropose a novel framework based on associated and hierarchical code description\ndistillation (AHDD) for better code representation learning and avoidance of\nimproper code assignment.we utilize the code description and the hierarchical\nstructure inherent to the ICD codes. Therefore, in this paper, we leverage the\ncode description and the hierarchical structure inherent to the ICD codes. The\ncode description is also applied to aware the attention layer and output layer.\nExperimental results on the benchmark dataset show the superiority of the\nproposed framework over several state-of-the-art baselines.\n", "date": "2024-04-17", "categories": []}
{"id": "2409.01941", "title": "Towards Leveraging Large Language Models for Automated Medical Q&A\n  Evaluation", "abstract": "  This paper explores the potential of using Large Language Models (LLMs) to\nautomate the evaluation of responses in medical Question and Answer (Q\\&A)\nsystems, a crucial form of Natural Language Processing. Traditionally, human\nevaluation has been indispensable for assessing the quality of these responses.\nHowever, manual evaluation by medical professionals is time-consuming and\ncostly. Our study examines whether LLMs can reliably replicate human\nevaluations by using questions derived from patient data, thereby saving\nvaluable time for medical experts. While the findings suggest promising\nresults, further research is needed to address more specific or complex\nquestions that were beyond the scope of this initial investigation.\n", "date": "2024-09-03", "categories": []}
{"id": "2403.04261", "title": "Advancing Chinese biomedical text mining with community challenges", "abstract": "  Objective: This study aims to review the recent advances in community\nchallenges for biomedical text mining in China. Methods: We collected\ninformation of evaluation tasks released in community challenges of biomedical\ntext mining, including task description, dataset description, data source, task\ntype and related links. A systematic summary and comparative analysis were\nconducted on various biomedical natural language processing tasks, such as\nnamed entity recognition, entity normalization, attribute extraction, relation\nextraction, event extraction, text classification, text similarity, knowledge\ngraph construction, question answering, text generation, and large language\nmodel evaluation. Results: We identified 39 evaluation tasks from 6 community\nchallenges that spanned from 2017 to 2023. Our analysis revealed the diverse\nrange of evaluation task types and data sources in biomedical text mining. We\nexplored the potential clinical applications of these community challenge tasks\nfrom a translational biomedical informatics perspective. We compared with their\nEnglish counterparts, and discussed the contributions, limitations, lessons and\nguidelines of these community challenges, while highlighting future directions\nin the era of large language models. Conclusion: Community challenge evaluation\ncompetitions have played a crucial role in promoting technology innovation and\nfostering interdisciplinary collaboration in the field of biomedical text\nmining. These challenges provide valuable platforms for researchers to develop\nstate-of-the-art solutions.\n", "date": "2024-03-07", "categories": []}
{"id": "2403.04785", "title": "Large Language Multimodal Models for 5-Year Chronic Disease Cohort\n  Prediction Using EHR Data", "abstract": "  Chronic diseases such as diabetes are the leading causes of morbidity and\nmortality worldwide. Numerous research studies have been attempted with various\ndeep learning models in diagnosis. However, most previous studies had certain\nlimitations, including using publicly available datasets (e.g. MIMIC), and\nimbalanced data. In this study, we collected five-year electronic health\nrecords (EHRs) from the Taiwan hospital database, including 1,420,596 clinical\nnotes, 387,392 laboratory test results, and more than 1,505 laboratory test\nitems, focusing on research pre-training large language models. We proposed a\nnovel Large Language Multimodal Models (LLMMs) framework incorporating\nmultimodal data from clinical notes and laboratory test results for the\nprediction of chronic disease risk. Our method combined a text embedding\nencoder and multi-head attention layer to learn laboratory test values,\nutilizing a deep neural network (DNN) module to merge blood features with\nchronic disease semantics into a latent space. In our experiments, we observe\nthat clinicalBERT and PubMed-BERT, when combined with attention fusion, can\nachieve an accuracy of 73% in multiclass chronic diseases and diabetes\nprediction. By transforming laboratory test values into textual descriptions\nand employing the Flan T-5 model, we achieved a 76% Area Under the ROC Curve\n(AUROC), demonstrating the effectiveness of leveraging numerical text data for\ntraining and inference in language models. This approach significantly improves\nthe accuracy of early-stage diabetes prediction.\n", "date": "2024-03-02", "categories": []}
{"id": "2408.17181", "title": "Improving Extraction of Clinical Event Contextual Properties from\n  Electronic Health Records: A Comparative Study", "abstract": "  Electronic Health Records are large repositories of valuable clinical data,\nwith a significant portion stored in unstructured text format. This textual\ndata includes clinical events (e.g., disorders, symptoms, findings, medications\nand procedures) in context that if extracted accurately at scale can unlock\nvaluable downstream applications such as disease prediction. Using an existing\nNamed Entity Recognition and Linking methodology, MedCAT, these identified\nconcepts need to be further classified (contextualised) for their relevance to\nthe patient, and their temporal and negated status for example, to be useful\ndownstream. This study performs a comparative analysis of various natural\nlanguage models for medical text classification. Extensive experimentation\nreveals the effectiveness of transformer-based language models, particularly\nBERT. When combined with class imbalance mitigation techniques, BERT\noutperforms Bi-LSTM models by up to 28% and the baseline BERT model by up to\n16% for recall of the minority classes. The method has been implemented as part\nof CogStack/MedCAT framework and made available to the community for further\nresearch.\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": "2406.19526", "title": "TocBERT: Medical Document Structure Extraction Using Bidirectional\n  Transformers", "abstract": "  Text segmentation holds paramount importance in the field of Natural Language\nProcessing (NLP). It plays an important role in several NLP downstream tasks\nlike information retrieval and document summarization. In this work, we propose\na new solution, namely TocBERT, for segmenting texts using bidirectional\ntransformers. TocBERT represents a supervised solution trained on the detection\nof titles and sub-titles from their semantic representations. This task was\nformulated as a named entity recognition (NER) problem. The solution has been\napplied on a medical text segmentation use-case where the Bio-ClinicalBERT\nmodel is fine-tuned to segment discharge summaries of the MIMIC-III dataset.\nThe performance of TocBERT has been evaluated on a human-labeled ground truth\ncorpus of 250 notes. It achieved an F1-score of 84.6% when evaluated on a\nlinear text segmentation problem and 72.8% on a hierarchical text segmentation\nproblem. It outperformed a carefully designed rule-based solution, particularly\nin distinguishing titles from subtitles.\n", "date": "2024-06-27", "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": "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": "2305.04928", "title": "From Zero to Hero: Harnessing Transformers for Biomedical Named Entity\n  Recognition in Zero- and Few-shot Contexts", "abstract": "  Supervised named entity recognition (NER) in the biomedical domain depends on\nlarge sets of annotated texts with the given named entities. The creation of\nsuch datasets can be time-consuming and expensive, while extraction of new\nentities requires additional annotation tasks and retraining the model. To\naddress these challenges, this paper proposes a method for zero- and few-shot\nNER in the biomedical domain. The method is based on transforming the task of\nmulti-class token classification into binary token classification and\npre-training on a large amount of datasets and biomedical entities, which allow\nthe model to learn semantic relations between the given and potentially novel\nnamed entity labels. We have achieved average F1 scores of 35.44% for zero-shot\nNER, 50.10% for one-shot NER, 69.94% for 10-shot NER, and 79.51% for 100-shot\nNER on 9 diverse evaluated biomedical entities with fine-tuned PubMedBERT-based\nmodel. The results demonstrate the effectiveness of the proposed method for\nrecognizing new biomedical entities with no or limited number of examples,\noutperforming previous transformer-based methods, and being comparable to\nGPT3-based models using models with over 1000 times fewer parameters. We make\nmodels and developed code publicly available.\n", "date": "2023-05-05", "categories": []}
{"id": "2408.12095", "title": "uMedSum: A Unified Framework for Advancing Medical Abstractive\n  Summarization", "abstract": "  Medical abstractive summarization faces the challenge of balancing\nfaithfulness and informativeness. Current methods often sacrifice key\ninformation for faithfulness or introduce confabulations when prioritizing\ninformativeness. While recent advancements in techniques like in-context\nlearning (ICL) and fine-tuning have improved medical summarization, they often\noverlook crucial aspects such as faithfulness and informativeness without\nconsidering advanced methods like model reasoning and self-improvement.\nMoreover, the field lacks a unified benchmark, hindering systematic evaluation\ndue to varied metrics and datasets. This paper addresses these gaps by\npresenting a comprehensive benchmark of six advanced abstractive summarization\nmethods across three diverse datasets using five standardized metrics. Building\non these findings, we propose uMedSum, a modular hybrid summarization framework\nthat introduces novel approaches for sequential confabulation removal followed\nby key missing information addition, ensuring both faithfulness and\ninformativeness. Our work improves upon previous GPT-4-based state-of-the-art\n(SOTA) medical summarization methods, significantly outperforming them in both\nquantitative metrics and qualitative domain expert evaluations. Notably, we\nachieve an average relative performance improvement of 11.8% in reference-free\nmetrics over the previous SOTA. Doctors prefer uMedSum's summaries 6 times more\nthan previous SOTA in difficult cases where there are chances of confabulations\nor missing information. These results highlight uMedSum's effectiveness and\ngeneralizability across various datasets and metrics, marking a significant\nadvancement in medical summarization.\n", "date": "2024-08-22", "categories": []}
{"id": "2408.13501", "title": "Utilizing Large Language Models for Named Entity Recognition in\n  Traditional Chinese Medicine against COVID-19 Literature: Comparative Study", "abstract": "  Objective: To explore and compare the performance of ChatGPT and other\nstate-of-the-art LLMs on domain-specific NER tasks covering different entity\ntypes and domains in TCM against COVID-19 literature. Methods: We established a\ndataset of 389 articles on TCM against COVID-19, and manually annotated 48 of\nthem with 6 types of entities belonging to 3 domains as the ground truth,\nagainst which the NER performance of LLMs can be assessed. We then performed\nNER tasks for the 6 entity types using ChatGPT (GPT-3.5 and GPT-4) and 4\nstate-of-the-art BERT-based question-answering (QA) models (RoBERTa, MiniLM,\nPubMedBERT and SciBERT) without prior training on the specific task. A domain\nfine-tuned model (GSAP-NER) was also applied for a comprehensive comparison.\nResults: The overall performance of LLMs varied significantly in exact match\nand fuzzy match. In the fuzzy match, ChatGPT surpassed BERT-based QA models in\n5 out of 6 tasks, while in exact match, BERT-based QA models outperformed\nChatGPT in 5 out of 6 tasks but with a smaller F-1 difference. GPT-4 showed a\nsignificant advantage over other models in fuzzy match, especially on the\nentity type of TCM formula and the Chinese patent drug (TFD) and ingredient\n(IG). Although GPT-4 outperformed BERT-based models on entity type of herb,\ntarget, and research method, none of the F-1 scores exceeded 0.5. GSAP-NER,\noutperformed GPT-4 in terms of F-1 by a slight margin on RM. ChatGPT achieved\nconsiderably higher recalls than precisions, particularly in the fuzzy match.\nConclusions: The NER performance of LLMs is highly dependent on the entity\ntype, and their performance varies across application scenarios. ChatGPT could\nbe a good choice for scenarios where high recall is favored. However, for\nknowledge acquisition in rigorous scenarios, neither ChatGPT nor BERT-based QA\nmodels are off-the-shelf tools for professional practitioners.\n", "date": "2024-08-24", "categories": []}
{"id": "2408.12980", "title": "MedDec: A Dataset for Extracting Medical Decisions from Discharge\n  Summaries", "abstract": "  Medical decisions directly impact individuals' health and well-being.\nExtracting decision spans from clinical notes plays a crucial role in\nunderstanding medical decision-making processes. In this paper, we develop a\nnew dataset called \"MedDec\", which contains clinical notes of eleven different\nphenotypes (diseases) annotated by ten types of medical decisions. We introduce\nthe task of medical decision extraction, aiming to jointly extract and classify\ndifferent types of medical decisions within clinical notes. We provide a\ncomprehensive analysis of the dataset, develop a span detection model as a\nbaseline for this task, evaluate recent span detection approaches, and employ a\nfew metrics to measure the complexity of data samples. Our findings shed light\non the complexities inherent in clinical decision extraction and enable future\nwork in this area of research. The dataset and code are available through\nhttps://github.com/CLU-UML/MedDec.\n", "date": "2024-08-23", "categories": []}
{"id": "2408.11334", "title": "BURExtract-Llama: An LLM for Clinical Concept Extraction in Breast\n  Ultrasound Reports", "abstract": "  Breast ultrasound is essential for detecting and diagnosing abnormalities,\nwith radiology reports summarizing key findings like lesion characteristics and\nmalignancy assessments. Extracting this critical information is challenging due\nto the unstructured nature of these reports, with varied linguistic styles and\ninconsistent formatting. While proprietary LLMs like GPT-4 are effective, they\nare costly and raise privacy concerns when handling protected health\ninformation. This study presents a pipeline for developing an in-house LLM to\nextract clinical information from radiology reports. We first use GPT-4 to\ncreate a small labeled dataset, then fine-tune a Llama3-8B model on it.\nEvaluated on clinician-annotated reports, our model achieves an average F1\nscore of 84.6%, which is on par with GPT-4. Our findings demonstrate the\nfeasibility of developing an in-house LLM that not only matches GPT-4's\nperformance but also offers cost reductions and enhanced data privacy.\n", "date": "2024-08-21", "categories": []}
{"id": "2408.11344", "title": "Clinical Context-aware Radiology Report Generation from Medical Images\n  using Transformers", "abstract": "  Recent developments in the field of Natural Language Processing, especially\nlanguage models such as the transformer have brought state-of-the-art results\nin language understanding and language generation. In this work, we investigate\nthe use of the transformer model for radiology report generation from chest\nX-rays. We also highlight limitations in evaluating radiology report generation\nusing only the standard language generation metrics. We then applied a\ntransformer based radiology report generation architecture, and also compare\nthe performance of a transformer based decoder with the recurrence based\ndecoder. Experiments were performed using the IU-CXR dataset, showing superior\nresults to its LSTM counterpart and being significantly faster. Finally, we\nidentify the need of evaluating radiology report generation system using both\nlanguage generation metrics and classification metrics, which helps to provide\nrobust measure of generated reports in terms of their coherence and diagnostic\nvalue.\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": "2408.09043", "title": "Improving VTE Identification through Language Models from Radiology\n  Reports: A Comparative Study of Mamba, Phi-3 Mini, and BERT", "abstract": "  Venous thromboembolism (VTE) is a critical cardiovascular condition,\nencompassing deep vein thrombosis (DVT) and pulmonary embolism (PE). Accurate\nand timely identification of VTE is essential for effective medical care. This\nstudy builds upon our previous work, which addressed VTE detection using deep\nlearning methods for DVT and a hybrid approach combining deep learning and\nrule-based classification for PE. Our earlier approaches, while effective, had\ntwo major limitations: they were complex and required expert involvement for\nfeature engineering of the rule set. To overcome these challenges, we utilize\nthe Mamba architecture-based classifier. This model achieves remarkable\nresults, with a 97\\% accuracy and F1 score on the DVT dataset and a 98\\%\naccuracy and F1 score on the PE dataset. In contrast to the previous hybrid\nmethod on PE identification, the Mamba classifier eliminates the need for\nhand-engineered rules, significantly reducing model complexity while\nmaintaining comparable performance. Additionally, we evaluated a lightweight\nLarge Language Model (LLM), Phi-3 Mini, in detecting VTE. While this model\ndelivers competitive results, outperforming the baseline BERT models, it proves\nto be computationally intensive due to its larger parameter set. Our evaluation\nshows that the Mamba-based model demonstrates superior performance and\nefficiency in VTE identification, offering an effective solution to the\nlimitations of previous approaches.\n", "date": "2024-08-16", "categories": []}
{"id": "2408.09193", "title": "AI Managed Emergency Documentation with a Pretrained Model", "abstract": "  This study investigates the use of a large language model system to improve\nefficiency and quality in emergency department (ED) discharge letter writing.\nTime constraints and infrastructural deficits make compliance with current\ndischarge letter targets difficult. We explored potential efficiencies from an\nartificial intelligence software in the generation of ED discharge letters and\nthe attitudes of doctors toward this technology. The evaluated system leverages\nadvanced techniques to fine-tune a model to generate discharge summaries from\nshort-hand inputs, including voice, text, and electronic health record data.\nNineteen physicians with emergency medicine experience evaluated the system\ntext and voice-to-text interfaces against manual typing. The results showed\nsignificant time savings with MedWrite LLM interfaces compared to manual\nmethods.\n", "date": "2024-08-17", "categories": []}
{"id": "2408.08624", "title": "RealMedQA: A pilot biomedical question answering dataset containing\n  realistic clinical questions", "abstract": "  Clinical question answering systems have the potential to provide clinicians\nwith relevant and timely answers to their questions. Nonetheless, despite the\nadvances that have been made, adoption of these systems in clinical settings\nhas been slow. One issue is a lack of question-answering datasets which reflect\nthe real-world needs of health professionals. In this work, we present\nRealMedQA, a dataset of realistic clinical questions generated by humans and an\nLLM. We describe the process for generating and verifying the QA pairs and\nassess several QA models on BioASQ and RealMedQA to assess the relative\ndifficulty of matching answers to questions. We show that the LLM is more\ncost-efficient for generating \"ideal\" QA pairs. Additionally, we achieve a\nlower lexical similarity between questions and answers than BioASQ which\nprovides an additional challenge to the top two QA models, as per the results.\nWe release our code and our dataset publicly to encourage further research.\n", "date": "2024-08-16", "categories": []}
{"id": "2408.07971", "title": "Predicting Lung Cancer Patient Prognosis with Large Language Models", "abstract": "  Prognosis prediction is crucial for determining optimal treatment plans for\nlung cancer patients. Traditionally, such predictions relied on models\ndeveloped from retrospective patient data. Recently, large language models\n(LLMs) have gained attention for their ability to process and generate text\nbased on extensive learned knowledge. In this study, we evaluate the potential\nof GPT-4o mini and GPT-3.5 in predicting the prognosis of lung cancer patients.\nWe collected two prognosis datasets, i.e., survival and post-operative\ncomplication datasets, and designed multiple tasks to assess the models'\nperformance comprehensively. Logistic regression models were also developed as\nbaselines for comparison. The experimental results demonstrate that LLMs can\nachieve competitive, and in some tasks superior, performance in lung cancer\nprognosis prediction compared to data-driven logistic regression models despite\nnot using additional patient data. These findings suggest that LLMs can be\neffective tools for prognosis prediction in lung cancer, particularly when\npatient data is limited or unavailable.\n", "date": "2024-08-15", "categories": []}
{"id": "2406.15363", "title": "Exploring LLM Multi-Agents for ICD Coding", "abstract": "  To address the limitations of Large Language Models (LLMs) in the\nInternational Classification of Diseases (ICD) coding task, where they often\nproduce inaccurate and incomplete prediction results due to the\nhigh-dimensional and skewed distribution of the ICD codes, and often lack\ninterpretability and reliability as well. We introduce an innovative\nmulti-agent approach for ICD coding which mimics the ICD coding assignment\nprocedure in real-world settings, comprising five distinct agents: the patient,\nphysician, coder, reviewer, and adjuster. Each agent utilizes an LLM-based\nmodel tailored to their specific role within the coding process. We also\nintegrate the system with Electronic Health Record (HER)'s SOAP (subjective,\nobjective, assessment and plan) structure to boost the performances. We compare\nour method with a system of agents designed solely by LLMs and other strong\nbaselines and evaluate it using the Medical Information Mart for Intensive Care\nIII (MIMIC-III) dataset. Our multi-agent coding framework significantly\noutperforms Zero-shot Chain of Thought (CoT) prompting and self-consistency\nwith CoT (CoT-SC) in coding common and rare ICD codes. An ablation study\nvalidates the effectiveness of the designated agent roles. it also outperforms\nthe LLM-designed agent system. Moreover, our method achieves comparable results\nto state-of-the-art ICD coding methods that require extensive pre-training or\nfine-tuning, and outperforms them in rare code accuracy, and explainability.\nAdditionally, we demonstrate the method's practical applicability by presenting\nits performance in scenarios not limited by the common or rare ICD code\nconstraints.The proposed multi-agent method for ICD coding effectively mimics\nthe real-world coding process and improves performance on both common and rare\ncodes.\n", "date": "2024-04-01", "categories": []}
{"id": "2407.17638", "title": "Time Matters: Examine Temporal Effects on Biomedical Language Models", "abstract": "  Time roots in applying language models for biomedical applications: models\nare trained on historical data and will be deployed for new or future data,\nwhich may vary from training data. While increasing biomedical tasks have\nemployed state-of-the-art language models, there are very few studies have\nexamined temporal effects on biomedical models when data usually shifts across\ndevelopment and deployment. This study fills the gap by statistically probing\nrelations between language model performance and data shifts across three\nbiomedical tasks. We deploy diverse metrics to evaluate model performance,\ndistance methods to measure data drifts, and statistical methods to quantify\ntemporal effects on biomedical language models. Our study shows that time\nmatters for deploying biomedical language models, while the degree of\nperformance degradation varies by biomedical tasks and statistical\nquantification approaches. We believe this study can establish a solid\nbenchmark to evaluate and assess temporal effects on deploying biomedical\nlanguage models.\n", "date": "2024-07-24", "categories": []}
{"id": "2408.05555", "title": "Large Language Model-based Role-Playing for Personalized Medical Jargon\n  Extraction", "abstract": "  Previous studies reveal that Electronic Health Records (EHR), which have been\nwidely adopted in the U.S. to allow patients to access their personal medical\ninformation, do not have high readability to patients due to the prevalence of\nmedical jargon. Tailoring medical notes to individual comprehension by\nidentifying jargon that is difficult for each person will enhance the utility\nof generative models. We present the first quantitative analysis to measure the\nimpact of role-playing in LLM in medical term extraction. By comparing the\nresults of Mechanical Turk workers over 20 sentences, our study demonstrates\nthat LLM role-playing improves F1 scores in 95% of cases across 14 different\nsocio-demographic backgrounds. Furthermore, applying role-playing with\nin-context learning outperformed the previous state-of-the-art models. Our\nresearch showed that ChatGPT can improve traditional medical term extraction\nsystems by utilizing role-play to deliver personalized patient education, a\npotential that previous models had not achieved.\n", "date": "2024-08-10", "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": "2408.06285", "title": "Synthetic Patient-Physician Dialogue Generation from Clinical Notes\n  Using LLM", "abstract": "  Medical dialogue systems (MDS) enhance patient-physician communication,\nimprove healthcare accessibility, and reduce costs. However, acquiring suitable\ndata to train these systems poses significant challenges. Privacy concerns\nprevent the use of real conversations, necessitating synthetic alternatives.\nSynthetic dialogue generation from publicly available clinical notes offers a\npromising solution to this issue, providing realistic data while safeguarding\nprivacy. Our approach, SynDial, uses a single LLM iteratively with zero-shot\nprompting and a feedback loop to generate and refine high-quality synthetic\ndialogues. The feedback consists of weighted evaluation scores for similarity\nand extractiveness. The iterative process ensures dialogues meet predefined\nthresholds, achieving superior extractiveness as a result of the feedback loop.\nAdditionally, evaluation shows that the generated dialogues excel in factuality\nmetric compared to the baselines and has comparable diversity scores with GPT4.\n", "date": "2024-08-12", "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": "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": "2408.04121", "title": "Can Rule-Based Insights Enhance LLMs for Radiology Report\n  Classification? Introducing the RadPrompt Methodology", "abstract": "  Developing imaging models capable of detecting pathologies from chest X-rays\ncan be cost and time-prohibitive for large datasets as it requires supervision\nto attain state-of-the-art performance. Instead, labels extracted from\nradiology reports may serve as distant supervision since these are routinely\ngenerated as part of clinical practice. Despite their widespread use, current\nrule-based methods for label extraction rely on extensive rule sets that are\nlimited in their robustness to syntactic variability. To alleviate these\nlimitations, we introduce RadPert, a rule-based system that integrates an\nuncertainty-aware information schema with a streamlined set of rules, enhancing\nperformance. Additionally, we have developed RadPrompt, a multi-turn prompting\nstrategy that leverages RadPert to bolster the zero-shot predictive\ncapabilities of large language models, achieving a statistically significant\nimprovement in weighted average F1 score over GPT-4 Turbo. Most notably,\nRadPrompt surpasses both its underlying models, showcasing the synergistic\npotential of LLMs with rule-based models. We have evaluated our methods on two\nEnglish Corpora: the MIMIC-CXR gold-standard test set and a gold-standard\ndataset collected from the Cambridge University Hospitals.\n", "date": "2024-08-07", "categories": []}
{"id": "2408.03622", "title": "Improving the quality of Persian clinical text with a novel spelling\n  correction system", "abstract": "  Background: The accuracy of spelling in Electronic Health Records (EHRs) is a\ncritical factor for efficient clinical care, research, and ensuring patient\nsafety. The Persian language, with its abundant vocabulary and complex\ncharacteristics, poses unique challenges for real-word error correction. This\nresearch aimed to develop an innovative approach for detecting and correcting\nspelling errors in Persian clinical text.\n  Methods: Our strategy employs a state-of-the-art pre-trained model that has\nbeen meticulously fine-tuned specifically for the task of spelling correction\nin the Persian clinical domain. This model is complemented by an innovative\northographic similarity matching algorithm, PERTO, which uses visual similarity\nof characters for ranking correction candidates.\n  Results: The evaluation of our approach demonstrated its robustness and\nprecision in detecting and rectifying word errors in Persian clinical text. In\nterms of non-word error correction, our model achieved an F1-Score of 90.0%\nwhen the PERTO algorithm was employed. For real-word error detection, our model\ndemonstrated its highest performance, achieving an F1-Score of 90.6%.\nFurthermore, the model reached its highest F1-Score of 91.5% for real-word\nerror correction when the PERTO algorithm was employed.\n  Conclusions: Despite certain limitations, our method represents a substantial\nadvancement in the field of spelling error detection and correction for Persian\nclinical text. By effectively addressing the unique challenges posed by the\nPersian language, our approach paves the way for more accurate and efficient\nclinical documentation, contributing to improved patient care and safety.\nFuture research could explore its use in other areas of the Persian medical\ndomain, enhancing its impact and utility.\n", "date": "2024-08-07", "categories": []}
{"id": "2408.03874", "title": "Personalized Clinical Note Generation from Doctor-Patient Conversations", "abstract": "  In this work, we present a novel technique to improve the quality of draft\nclinical notes for physicians. This technique is concentrated on the ability to\nmodel implicit physician conversation styles and note preferences. We also\nintroduce a novel technique for the enrollment of new physicians when a limited\nnumber of clinical notes paired with conversations are available for that\nphysician, without the need to re-train a model to support them. We show that\nour technique outperforms the baseline model by improving the ROUGE-2 score of\nthe History of Present Illness section by 13.8%, the Physical Examination\nsection by 88.6%, and the Assessment & Plan section by 50.8%.\n", "date": "2024-08-07", "categories": []}
{"id": "2408.03127", "title": "Lisbon Computational Linguists at SemEval-2024 Task 2: Using A Mistral\n  7B Model and Data Augmentation", "abstract": "  This paper describes our approach to the SemEval-2024 safe biomedical Natural\nLanguage Inference for Clinical Trials (NLI4CT) task, which concerns\nclassifying statements about Clinical Trial Reports (CTRs). We explored the\ncapabilities of Mistral-7B, a generalist open-source Large Language Model\n(LLM). We developed a prompt for the NLI4CT task, and fine-tuned a quantized\nversion of the model using an augmented version of the training dataset. The\nexperimental results show that this approach can produce notable results in\nterms of the macro F1-score, while having limitations in terms of faithfulness\nand consistency. All the developed code is publicly available on a GitHub\nrepository\n", "date": "2024-08-06", "categories": []}
{"id": "2408.00122", "title": "A Course Shared Task on Evaluating LLM Output for Clinical Questions", "abstract": "  This paper presents a shared task that we organized at the Foundations of\nLanguage Technology (FoLT) course in 2023/2024 at the Technical University of\nDarmstadt, which focuses on evaluating the output of Large Language Models\n(LLMs) in generating harmful answers to health-related clinical questions. We\ndescribe the task design considerations and report the feedback we received\nfrom the students. We expect the task and the findings reported in this paper\nto be relevant for instructors teaching natural language processing (NLP) and\ndesigning course assignments.\n", "date": "2024-07-31", "categories": []}
{"id": "2405.11407", "title": "Can Public LLMs be used for Self-Diagnosis of Medical Conditions ?", "abstract": "  Advancements in deep learning have generated a large-scale interest in the\ndevelopment of foundational deep learning models. The development of Large\nLanguage Models (LLM) has evolved as a transformative paradigm in\nconversational tasks, which has led to its integration and extension even in\nthe critical domain of healthcare. With LLMs becoming widely popular and their\npublic access through open-source models and integration with other\napplications, there is a need to investigate their potential and limitations.\nOne such crucial task where LLMs are applied but require a deeper understanding\nis that of self-diagnosis of medical conditions based on bias-validating\nsymptoms in the interest of public health. The widespread integration of Gemini\nwith Google search and GPT-4.0 with Bing search has led to a shift in the trend\nof self-diagnosis using search engines to conversational LLM models. Owing to\nthe critical nature of the task, it is prudent to investigate and understand\nthe potential and limitations of public LLMs in the task of self-diagnosis. In\nthis study, we prepare a prompt engineered dataset of 10000 samples and test\nthe performance on the general task of self-diagnosis. We compared the\nperformance of both the state-of-the-art GPT-4.0 and the fee Gemini model on\nthe task of self-diagnosis and recorded contrasting accuracies of 63.07% and\n6.01%, respectively. We also discuss the challenges, limitations, and potential\nof both Gemini and GPT-4.0 for the task of self-diagnosis to facilitate future\nresearch and towards the broader impact of general public knowledge.\nFurthermore, we demonstrate the potential and improvement in performance for\nthe task of self-diagnosis using Retrieval Augmented Generation.\n", "date": "2024-05-18", "categories": []}
{"id": "2407.17477", "title": "Toward Automated Detection of Biased Social Signals from the Content of\n  Clinical Conversations", "abstract": "  Implicit bias can impede patient-provider interactions and lead to inequities\nin care. Raising awareness is key to reducing such bias, but its manifestations\nin the social dynamics of patient-provider communication are difficult to\ndetect. In this study, we used automated speech recognition (ASR) and natural\nlanguage processing (NLP) to identify social signals in patient-provider\ninteractions. We built an automated pipeline to predict social signals from\naudio recordings of 782 primary care visits that achieved 90.1% average\naccuracy across codes, and exhibited fairness in its predictions for white and\nnon-white patients. Applying this pipeline, we identified statistically\nsignificant differences in provider communication behavior toward white versus\nnon-white patients. In particular, providers expressed more patient-centered\nbehaviors towards white patients including more warmth, engagement, and\nattentiveness. Our study underscores the potential of automated tools in\nidentifying subtle communication signals that may be linked with bias and\nimpact healthcare quality and equity.\n", "date": "2024-07-01", "categories": []}
{"id": "2407.21638", "title": "Quality Control for Radiology Report Generation Models via Auxiliary\n  Auditing Components", "abstract": "  Automation of medical image interpretation could alleviate bottlenecks in\ndiagnostic workflows, and has become of particular interest in recent years due\nto advancements in natural language processing. Great strides have been made\ntowards automated radiology report generation via AI, yet ensuring clinical\naccuracy in generated reports is a significant challenge, hindering deployment\nof such methods in clinical practice. In this work we propose a quality control\nframework for assessing the reliability of AI-generated radiology reports with\nrespect to semantics of diagnostic importance using modular auxiliary auditing\ncomponents (AC). Evaluating our pipeline on the MIMIC-CXR dataset, our findings\nshow that incorporating ACs in the form of disease-classifiers can enable\nauditing that identifies more reliable reports, resulting in higher F1 scores\ncompared to unfiltered generated reports. Additionally, leveraging the\nconfidence of the AC labels further improves the audit's effectiveness.\n", "date": "2024-07-31", "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": "2403.00821", "title": "Social Media as a Sensor: Analyzing Twitter Data for Breast Cancer\n  Medication Effects Using Natural Language Processing", "abstract": "  Breast cancer is a significant public health concern and is the leading cause\nof cancer-related deaths among women. Despite advances in breast cancer\ntreatments, medication non-adherence remains a major problem. As electronic\nhealth records do not typically capture patient-reported outcomes that may\nreveal information about medication-related experiences, social media presents\nan attractive resource for enhancing our understanding of the patients'\ntreatment experiences. In this paper, we developed natural language processing\n(NLP) based methodologies to study information posted by an automatically\ncurated breast cancer cohort from social media. We employed a transformer-based\nclassifier to identify breast cancer patients/survivors on X (Twitter) based on\ntheir self-reported information, and we collected longitudinal data from their\nprofiles. We then designed a multi-layer rule-based model to develop a breast\ncancer therapy-associated side effect lexicon and detect patterns of medication\nusage and associated side effects among breast cancer patients. 1,454,637 posts\nwere available from 583,962 unique users, of which 62,042 were detected as\nbreast cancer members using our transformer-based model. 198 cohort members\nmentioned breast cancer medications with tamoxifen as the most common. Our side\neffect lexicon identified well-known side effects of hormone and chemotherapy.\nFurthermore, it discovered a subject feeling towards cancer and medications,\nwhich may suggest a pre-clinical phase of side effects or emotional distress.\nThis analysis highlighted not only the utility of NLP techniques in\nunstructured social media data to identify self-reported breast cancer posts,\nmedication usage patterns, and treatment side effects but also the richness of\nsocial data on such clinical questions.\n", "date": "2024-02-26", "categories": []}
{"id": "2407.19256", "title": "Stochastic Parrots or ICU Experts? Large Language Models in Critical\n  Care Medicine: A Scoping Review", "abstract": "  With the rapid development of artificial intelligence (AI), large language\nmodels (LLMs) have shown strong capabilities in natural language understanding,\nreasoning, and generation, attracting amounts of research interest in applying\nLLMs to health and medicine. Critical care medicine (CCM) provides diagnosis\nand treatment for critically ill patients who often require intensive\nmonitoring and interventions in intensive care units (ICUs). Can LLMs be\napplied to CCM? Are LLMs just like stochastic parrots or ICU experts in\nassisting clinical decision-making? This scoping review aims to provide a\npanoramic portrait of the application of LLMs in CCM. Literature in seven\ndatabases, including PubMed, Embase, Scopus, Web of Science, CINAHL, IEEE\nXplore, and ACM Digital Library, were searched from January 1, 2019, to June\n10, 2024. Peer-reviewed journal and conference articles that discussed the\napplication of LLMs in critical care settings were included. From an initial\n619 articles, 24 were selected for final review. This review grouped\napplications of LLMs in CCM into three categories: clinical decision support,\nmedical documentation and reporting, and medical education and doctor-patient\ncommunication. LLMs have advantages in handling unstructured data and do not\nrequire manual feature engineering. Meanwhile, applying LLMs to CCM faces\nchallenges, including hallucinations, poor interpretability, bias and alignment\nchallenges, and privacy and ethics issues. Future research should enhance model\nreliability and interpretability, integrate up-to-date medical knowledge, and\nstrengthen privacy and ethical guidelines. As LLMs evolve, they could become\nkey tools in CCM to help improve patient outcomes and optimize healthcare\ndelivery. This study is the first review of LLMs in CCM, aiding researchers,\nclinicians, and policymakers to understand the current status and future\npotentials of LLMs in CCM.\n", "date": "2024-07-27", "categories": []}
{"id": "2307.05560", "title": "Automatic Coding at Scale: Design and Deployment of a Nationwide System\n  for Normalizing Referrals in the Chilean Public Healthcare System", "abstract": "  The disease coding task involves assigning a unique identifier from a\ncontrolled vocabulary to each disease mentioned in a clinical document. This\ntask is relevant since it allows information extraction from unstructured data\nto perform, for example, epidemiological studies about the incidence and\nprevalence of diseases in a determined context. However, the manual coding\nprocess is subject to errors as it requires medical personnel to be competent\nin coding rules and terminology. In addition, this process consumes a lot of\ntime and energy, which could be allocated to more clinically relevant tasks.\nThese difficulties can be addressed by developing computational systems that\nautomatically assign codes to diseases. In this way, we propose a two-step\nsystem for automatically coding diseases in referrals from the Chilean public\nhealthcare system. Specifically, our model uses a state-of-the-art NER model\nfor recognizing disease mentions and a search engine system based on\nElasticsearch for assigning the most relevant codes associated with these\ndisease mentions. The system's performance was evaluated on referrals manually\ncoded by clinical experts. Our system obtained a MAP score of 0.63 for the\nsubcategory level and 0.83 for the category level, close to the best-performing\nmodels in the literature. This system could be a support tool for health\nprofessionals, optimizing the coding and management process. Finally, to\nguarantee reproducibility, we publicly release the code of our models and\nexperiments.\n", "date": "2023-07-09", "categories": []}
{"id": "2405.01686", "title": "Automatically Extracting Numerical Results from Randomized Controlled\n  Trials with Large Language Models", "abstract": "  Meta-analyses statistically aggregate the findings of different randomized\ncontrolled trials (RCTs) to assess treatment effectiveness. Because this yields\nrobust estimates of treatment effectiveness, results from meta-analyses are\nconsidered the strongest form of evidence. However, rigorous evidence syntheses\nare time-consuming and labor-intensive, requiring manual extraction of data\nfrom individual trials to be synthesized. Ideally, language technologies would\npermit fully automatic meta-analysis, on demand. This requires accurately\nextracting numerical results from individual trials, which has been beyond the\ncapabilities of natural language processing (NLP) models to date. In this work,\nwe evaluate whether modern large language models (LLMs) can reliably perform\nthis task. We annotate (and release) a modest but granular evaluation dataset\nof clinical trial reports with numerical findings attached to interventions,\ncomparators, and outcomes. Using this dataset, we evaluate the performance of\nseven LLMs applied zero-shot for the task of conditionally extracting numerical\nfindings from trial reports. We find that massive LLMs that can accommodate\nlengthy inputs are tantalizingly close to realizing fully automatic\nmeta-analysis, especially for dichotomous (binary) outcomes (e.g., mortality).\nHowever, LLMs -- including ones trained on biomedical texts -- perform poorly\nwhen the outcome measures are complex and tallying the results requires\ninference. This work charts a path toward fully automatic meta-analysis of RCTs\nvia LLMs, while also highlighting the limitations of existing models for this\naim.\n", "date": "2024-05-02", "categories": []}
{"id": "2407.17636", "title": "IgnitionInnovators at \"Discharge Me!\": Chain-of-Thought Instruction\n  Finetuning Large Language Models for Discharge Summaries", "abstract": "  This paper presents our proposed approach to the Discharge Me! shared task,\ncollocated with the 23th Workshop on Biomedical Natural Language Processing\n(BioNLP). In this work, we develop an LLM-based framework for solving the\nDischarge Summary Documentation (DSD) task, i.e., generating the two critical\ntarget sections `Brief Hospital Course' and `Discharge Instructions' in the\ndischarge summary. By streamlining the recent instruction-finetuning process on\nLLMs, we explore several prompting strategies for optimally adapting LLMs to\nspecific generation task of DSD. Experimental results show that providing a\nclear output structure, complimented by a set of comprehensive\nChain-of-Thoughts (CoT) questions, effectively improves the model's reasoning\ncapability, and thereby, enhancing the structural correctness and faithfulness\nof clinical information in the generated text. Source code is available at:\nhttps://github.com/antangrocket1312/Discharge_LLM\n", "date": "2024-07-24", "categories": []}
{"id": "2407.16905", "title": "Assessing the role of clinical summarization and patient chart review\n  within communications, medical management, and diagnostics", "abstract": "  Effective summarization of unstructured patient data in electronic health\nrecords (EHRs) is crucial for accurate diagnosis and efficient patient care,\nyet clinicians often struggle with information overload and time constraints.\nThis review dives into recent literature and case studies on both the\nsignificant impacts and outstanding issues of patient chart review on\ncommunications, diagnostics, and management. It also discusses recent efforts\nto integrate artificial intelligence (AI) into clinical summarization tasks,\nand its transformative impact on the clinician's potential, including but not\nlimited to reductions of administrative burden and improved patient-centered\ncare.\n", "date": "2024-06-24", "categories": []}
{"id": "2407.17126", "title": "SDoH-GPT: Using Large Language Models to Extract Social Determinants of\n  Health (SDoH)", "abstract": "  Extracting social determinants of health (SDoH) from unstructured medical\nnotes depends heavily on labor-intensive annotations, which are typically\ntask-specific, hampering reusability and limiting sharing. In this study we\nintroduced SDoH-GPT, a simple and effective few-shot Large Language Model (LLM)\nmethod leveraging contrastive examples and concise instructions to extract SDoH\nwithout relying on extensive medical annotations or costly human intervention.\nIt achieved tenfold and twentyfold reductions in time and cost respectively,\nand superior consistency with human annotators measured by Cohen's kappa of up\nto 0.92. The innovative combination of SDoH-GPT and XGBoost leverages the\nstrengths of both, ensuring high accuracy and computational efficiency while\nconsistently maintaining 0.90+ AUROC scores. Testing across three distinct\ndatasets has confirmed its robustness and accuracy. This study highlights the\npotential of leveraging LLMs to revolutionize medical note classification,\ndemonstrating their capability to achieve highly accurate classifications with\nsignificantly reduced time and cost.\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": "2210.12777", "title": "Retrieval-Augmented and Knowledge-Grounded Language Models for Faithful\n  Clinical Medicine", "abstract": "  Language models (LMs), including large language models (such as ChatGPT),\nhave the potential to assist clinicians in generating various clinical notes.\nHowever, LMs are prone to produce ``hallucinations'', i.e., generated content\nthat is not aligned with facts and knowledge. In this paper, we propose the\nRe$^3$Writer method with retrieval-augmented generation and knowledge-grounded\nreasoning to enable LMs to generate faithful clinical texts. We demonstrate the\neffectiveness of our method in generating patient discharge instructions. It\nrequires the LMs not to only understand the patients' long clinical documents,\ni.e., the health records during hospitalization, but also to generate critical\ninstructional information provided both to carers and to the patient at the\ntime of discharge. The proposed Re$^3$Writer imitates the working patterns of\nphysicians to first \\textbf{re}trieve related working experience from\nhistorical instructions written by physicians, then \\textbf{re}ason related\nmedical knowledge. Finally, it \\textbf{re}fines the retrieved working\nexperience and reasoned medical knowledge to extract useful information, which\nis used to generate the discharge instructions for previously-unseen patients.\nOur experiments show that, using our method, the performance of five\nrepresentative LMs can be substantially boosted across all metrics. Meanwhile,\nwe show results from human evaluations to measure the effectiveness in terms of\nfluency, faithfulness, and comprehensiveness.\n", "date": "2022-10-23", "categories": []}
{"id": "2311.05112", "title": "A Survey of Large Language Models in Medicine: Progress, Application,\n  and Challenge", "abstract": "  Large language models (LLMs), such as ChatGPT, have received substantial\nattention due to their capabilities for understanding and generating human\nlanguage. While there has been a burgeoning trend in research focusing on the\nemployment of LLMs in supporting different medical tasks (e.g., enhancing\nclinical diagnostics and providing medical education), a review of these\nefforts, particularly their development, practical applications, and outcomes\nin medicine, remains scarce. Therefore, this review aims to provide a detailed\noverview of the development and deployment of LLMs in medicine, including the\nchallenges and opportunities they face. In terms of development, we provide a\ndetailed introduction to the principles of existing medical LLMs, including\ntheir basic model structures, number of parameters, and sources and scales of\ndata used for model development. It serves as a guide for practitioners in\ndeveloping medical LLMs tailored to their specific needs. In terms of\ndeployment, we offer a comparison of the performance of different LLMs across\nvarious medical tasks, and further compare them with state-of-the-art\nlightweight models, aiming to provide an understanding of the advantages and\nlimitations of LLMs in medicine. Overall, in this review, we address the\nfollowing questions: 1) What are the practices for developing medical LLMs 2)\nHow to measure the medical task performance of LLMs in a medical setting? 3)\nHow have medical LLMs been employed in real-world practice? 4) What challenges\narise from the use of medical LLMs? and 5) How to more effectively develop and\ndeploy medical LLMs? By answering these questions, this review aims to provide\ninsights into the opportunities for LLMs in medicine and serve as a practical\nresource. We also maintain a regularly updated list of practical guides on\nmedical LLMs at https://github.com/AI-in-Health/MedLLMsPracticalGuide\n", "date": "2023-11-09", "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": "2407.14000", "title": "Clinical Reading Comprehension with Encoder-Decoder Models Enhanced by\n  Direct Preference Optimization", "abstract": "  Extractive question answering over clinical text is a crucial need to help\ndeal with the deluge of clinical text generated in hospitals. While encoder\nmodels (e.g., BERT) have been popular for this reading comprehension task,\nrecently encoder-decoder models (e.g., T5) are on the rise. There is also the\nemergence of preference optimization techniques to align decoder-only LLMs with\nhuman preferences. In this paper, we combine encoder-decoder models with the\ndirect preference optimization (DPO) method to improve over prior state of the\nart for the RadQA radiology question answering task by 12-15 F1 points. To the\nbest of our knowledge, this effort is the first to show that DPO method also\nworks for reading comprehension via novel heuristics to generate preference\ndata without human inputs.\n", "date": "2024-07-19", "categories": []}
{"id": "2407.12849", "title": "Large language models are good medical coders, if provided with tools", "abstract": "  This study presents a novel two-stage Retrieve-Rank system for automated\nICD-10-CM medical coding, comparing its performance against a Vanilla Large\nLanguage Model (LLM) approach. Evaluating both systems on a dataset of 100\nsingle-term medical conditions, the Retrieve-Rank system achieved 100% accuracy\nin predicting correct ICD-10-CM codes, significantly outperforming the Vanilla\nLLM (GPT-3.5-turbo), which achieved only 6% accuracy. Our analysis demonstrates\nthe Retrieve-Rank system's superior precision in handling various medical terms\nacross different specialties. While these results are promising, we acknowledge\nthe limitations of using simplified inputs and the need for further testing on\nmore complex, realistic medical cases. This research contributes to the ongoing\neffort to improve the efficiency and accuracy of medical coding, highlighting\nthe importance of retrieval-based approaches.\n", "date": "2024-07-06", "categories": []}
{"id": "2407.13228", "title": "Evaluating Large Language Models for Anxiety and Depression\n  Classification using Counseling and Psychotherapy Transcripts", "abstract": "  We aim to evaluate the efficacy of traditional machine learning and large\nlanguage models (LLMs) in classifying anxiety and depression from long\nconversational transcripts. We fine-tune both established transformer models\n(BERT, RoBERTa, Longformer) and more recent large models (Mistral-7B), trained\na Support Vector Machine with feature engineering, and assessed GPT models\nthrough prompting. We observe that state-of-the-art models fail to enhance\nclassification outcomes compared to traditional machine learning methods.\n", "date": "2024-07-18", "categories": []}
{"id": "2407.13463", "title": "End-To-End Clinical Trial Matching with Large Language Models", "abstract": "  Matching cancer patients to clinical trials is essential for advancing\ntreatment and patient care. However, the inconsistent format of medical free\ntext documents and complex trial eligibility criteria make this process\nextremely challenging and time-consuming for physicians. We investigated\nwhether the entire trial matching process - from identifying relevant trials\namong 105,600 oncology-related clinical trials on clinicaltrials.gov to\ngenerating criterion-level eligibility matches - could be automated using Large\nLanguage Models (LLMs). Using GPT-4o and a set of 51 synthetic Electronic\nHealth Records (EHRs), we demonstrate that our approach identifies relevant\ncandidate trials in 93.3% of cases and achieves a preliminary accuracy of 88.0%\nwhen matching patient-level information at the criterion level against a\nbaseline defined by human experts. Utilizing LLM feedback reveals that 39.3%\ncriteria that were initially considered incorrect are either ambiguous or\ninaccurately annotated, leading to a total model accuracy of 92.7% after\nrefining our human baseline. In summary, we present an end-to-end pipeline for\nclinical trial matching using LLMs, demonstrating high precision in screening\nand matching trials to individual patients, even outperforming the performance\nof qualified medical doctors. Our fully end-to-end pipeline can operate\nautonomously or with human supervision and is not restricted to oncology,\noffering a scalable solution for enhancing patient-trial matching in real-world\nsettings.\n", "date": "2024-07-18", "categories": []}
{"id": "2407.13638", "title": "A Comparative Study on Automatic Coding of Medical Letters with\n  Explainability", "abstract": "  This study aims to explore the implementation of Natural Language Processing\n(NLP) and machine learning (ML) techniques to automate the coding of medical\nletters with visualised explainability and light-weighted local computer\nsettings. Currently in clinical settings, coding is a manual process that\ninvolves assigning codes to each condition, procedure, and medication in a\npatient's paperwork (e.g., 56265001 heart disease using SNOMED CT code). There\nare preliminary research on automatic coding in this field using\nstate-of-the-art ML models; however, due to the complexity and size of the\nmodels, the real-world deployment is not achieved. To further facilitate the\npossibility of automatic coding practice, we explore some solutions in a local\ncomputer setting; in addition, we explore the function of explainability for\ntransparency of AI models. We used the publicly available MIMIC-III database\nand the HAN/HLAN network models for ICD code prediction purposes. We also\nexperimented with the mapping between ICD and SNOMED CT knowledge bases. In our\nexperiments, the models provided useful information for 97.98\\% of codes. The\nresult of this investigation can shed some light on implementing automatic\nclinical coding in practice, such as in hospital settings, on the local\ncomputers used by clinicians , project page\n\\url{https://github.com/Glenj01/Medical-Coding}.\n", "date": "2024-07-18", "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": "2407.12626", "title": "Domain-specific or Uncertainty-aware models: Does it really make a\n  difference for biomedical text classification?", "abstract": "  The success of pretrained language models (PLMs) across a spate of use-cases\nhas led to significant investment from the NLP community towards building\ndomain-specific foundational models. On the other hand, in mission critical\nsettings such as biomedical applications, other aspects also factor in-chief of\nwhich is a model's ability to produce reasonable estimates of its own\nuncertainty. In the present study, we discuss these two desiderata through the\nlens of how they shape the entropy of a model's output probability\ndistribution. We find that domain specificity and uncertainty awareness can\noften be successfully combined, but the exact task at hand weighs in much more\nstrongly.\n", "date": "2024-07-17", "categories": []}
{"id": "2311.06025", "title": "ChiMed-GPT: A Chinese Medical Large Language Model with Full Training\n  Regime and Better Alignment to Human Preferences", "abstract": "  Recently, the increasing demand for superior medical services has highlighted\nthe discrepancies in the medical infrastructure. With big data, especially\ntexts, forming the foundation of medical services, there is an exigent need for\neffective natural language processing (NLP) solutions tailored to the\nhealthcare domain. Conventional approaches leveraging pre-trained models\npresent promising results in this domain and current large language models\n(LLMs) offer advanced foundation for medical text processing. However, most\nmedical LLMs are trained only with supervised fine-tuning (SFT), even though it\nefficiently empowers LLMs to understand and respond to medical instructions but\nis ineffective in learning domain knowledge and aligning with human preference.\nIn this work, we propose ChiMed-GPT, a new benchmark LLM designed explicitly\nfor Chinese medical domain, and undergoes a comprehensive training regime with\npre-training, SFT, and RLHF. Evaluations on tasks including information\nextraction, question answering, and dialogue generation demonstrate\nChiMed-GPT's superior performance over general domain LLMs. Furthermore, we\nanalyze possible biases through prompting ChiMed-GPT to perform attitude scales\nregarding discrimination of patients, so as to contribute to further\nresponsible development of LLMs in the medical domain. The code and model are\nreleased at https://github.com/synlp/ChiMed-GPT.\n", "date": "2023-11-10", "categories": []}
{"id": "2407.11007", "title": "Panacea: A foundation model for clinical trial search, summarization,\n  design, and recruitment", "abstract": "  Clinical trials are fundamental in developing new drugs, medical devices, and\ntreatments. However, they are often time-consuming and have low success rates.\nAlthough there have been initial attempts to create large language models\n(LLMs) for clinical trial design and patient-trial matching, these models\nremain task-specific and not adaptable to diverse clinical trial tasks. To\naddress this challenge, we propose a clinical trial foundation model named\nPanacea, designed to handle multiple tasks, including trial search, trial\nsummarization, trial design, and patient-trial matching. We also assemble a\nlarge-scale dataset, named TrialAlign, of 793,279 trial documents and 1,113,207\ntrial-related scientific papers, to infuse clinical knowledge into the model by\npre-training. We further curate TrialInstruct, which has 200,866 of instruction\ndata for fine-tuning. These resources enable Panacea to be widely applicable\nfor a range of clinical trial tasks based on user requirements.\n  We evaluated Panacea on a new benchmark, named TrialPanorama, which covers\neight clinical trial tasks. Our method performed the best on seven of the eight\ntasks compared to six cutting-edge generic or medicine-specific LLMs.\nSpecifically, Panacea showed great potential to collaborate with human experts\nin crafting the design of eligibility criteria, study arms, and outcome\nmeasures, in multi-round conversations. In addition, Panacea achieved 14.42%\nimprovement in patient-trial matching, 41.78% to 52.02% improvement in trial\nsearch, and consistently ranked at the top for five aspects of trial\nsummarization. Our approach demonstrates the effectiveness of Panacea in\nclinical trials and establishes a comprehensive resource, including training\ndata, model, and benchmark, for developing clinical trial foundation models,\npaving the path for AI-based clinical trial development.\n", "date": "2024-06-25", "categories": []}
{"id": "2407.10021", "title": "Document-level Clinical Entity and Relation Extraction via Knowledge\n  Base-Guided Generation", "abstract": "  Generative pre-trained transformer (GPT) models have shown promise in\nclinical entity and relation extraction tasks because of their precise\nextraction and contextual understanding capability. In this work, we further\nleverage the Unified Medical Language System (UMLS) knowledge base to\naccurately identify medical concepts and improve clinical entity and relation\nextraction at the document level. Our framework selects UMLS concepts relevant\nto the text and combines them with prompts to guide language models in\nextracting entities. Our experiments demonstrate that this initial concept\nmapping and the inclusion of these mapped concepts in the prompts improves\nextraction results compared to few-shot extraction tasks on generic language\nmodels that do not leverage UMLS. Further, our results show that this approach\nis more effective than the standard Retrieval Augmented Generation (RAG)\ntechnique, where retrieved data is compared with prompt embeddings to generate\nresults. Overall, we find that integrating UMLS concepts with GPT models\nsignificantly improves entity and relation identification, outperforming the\nbaseline and RAG models. By combining the precise concept mapping capability of\nknowledge-based approaches like UMLS with the contextual understanding\ncapability of GPT, our method highlights the potential of these approaches in\nspecialized domains like healthcare.\n", "date": "2024-07-13", "categories": []}
{"id": "2407.10453", "title": "Enhancing Medication Recommendation with LLM Text Representation", "abstract": "  Most of the existing medication recommendation models are predicted with only\nstructured data such as medical codes, with the remaining other large amount of\nunstructured or semi-structured data underutilization. To increase the\nutilization effectively, we proposed a method of enhancing medication\nrecommendation with Large Language Model (LLM) text representation. LLM\nharnesses powerful language understanding and generation capabilities, enabling\nthe extraction of information from complex and lengthy unstructured data such\nas clinical notes which contain complex terminology. This method can be applied\nto several existing base models we selected and improve medication\nrecommendation performance with the combination representation of text and\nmedical codes experiments on two different datasets. LLM text representation\nalone can even demonstrate a comparable ability to the medical code\nrepresentation alone. Overall, this is a general method that can be applied to\nother models for improved recommendations.\n", "date": "2024-07-15", "categories": []}
{"id": "2407.09429", "title": "Open (Clinical) LLMs are Sensitive to Instruction Phrasings", "abstract": "  Instruction-tuned Large Language Models (LLMs) can perform a wide range of\ntasks given natural language instructions to do so, but they are sensitive to\nhow such instructions are phrased. This issue is especially concerning in\nhealthcare, as clinicians are unlikely to be experienced prompt engineers and\nthe potential consequences of inaccurate outputs are heightened in this domain.\n  This raises a practical question: How robust are instruction-tuned LLMs to\nnatural variations in the instructions provided for clinical NLP tasks? We\ncollect prompts from medical doctors across a range of tasks and quantify the\nsensitivity of seven LLMs -- some general, others specialized -- to natural\n(i.e., non-adversarial) instruction phrasings. We find that performance varies\nsubstantially across all models, and that -- perhaps surprisingly --\ndomain-specific models explicitly trained on clinical data are especially\nbrittle, compared to their general domain counterparts. Further, arbitrary\nphrasing differences can affect fairness, e.g., valid but distinct instructions\nfor mortality prediction yield a range both in overall performance, and in\nterms of differences between demographic groups.\n", "date": "2024-07-12", "categories": []}
{"id": "2404.07376", "title": "LLMs in Biomedicine: A study on clinical Named Entity Recognition", "abstract": "  Large Language Models (LLMs) demonstrate remarkable versatility in various\nNLP tasks but encounter distinct challenges in biomedical due to the\ncomplexities of language and data scarcity. This paper investigates LLMs\napplication in the biomedical domain by exploring strategies to enhance their\nperformance for the NER task. Our study reveals the importance of meticulously\ndesigned prompts in the biomedical. Strategic selection of in-context examples\nyields a marked improvement, offering ~15-20\\% increase in F1 score across all\nbenchmark datasets for biomedical few-shot NER. Additionally, our results\nindicate that integrating external biomedical knowledge via prompting\nstrategies can enhance the proficiency of general-purpose LLMs to meet the\nspecialized needs of biomedical NER. Leveraging a medical knowledge base, our\nproposed method, DiRAG, inspired by Retrieval-Augmented Generation (RAG), can\nboost the zero-shot F1 score of LLMs for biomedical NER. Code is released at\n\\url{https://github.com/masoud-monajati/LLM_Bio_NER}\n", "date": "2024-04-10", "categories": []}
{"id": "2407.08322", "title": "Intelligent Multi-Document Summarisation for Extracting Insights on\n  Racial Inequalities from Maternity Incident Investigation Reports", "abstract": "  In healthcare, thousands of safety incidents occur every year, but learning\nfrom these incidents is not effectively aggregated. Analysing incident reports\nusing AI could uncover critical insights to prevent harm by identifying\nrecurring patterns and contributing factors. To aggregate and extract valuable\ninformation, natural language processing (NLP) and machine learning techniques\ncan be employed to summarise and mine unstructured data, potentially surfacing\nsystemic issues and priority areas for improvement. This paper presents\nI-SIRch:CS, a framework designed to facilitate the aggregation and analysis of\nsafety incident reports while ensuring traceability throughout the process. The\nframework integrates concept annotation using the Safety Intelligence Research\n(SIRch) taxonomy with clustering, summarisation, and analysis capabilities.\nUtilising a dataset of 188 anonymised maternity investigation reports annotated\nwith 27 SIRch human factors concepts, I-SIRch:CS groups the annotated sentences\ninto clusters using sentence embeddings and k-means clustering, maintaining\ntraceability via file and sentence IDs. Summaries are generated for each\ncluster using offline state-of-the-art abstractive summarisation models (BART,\nDistilBART, T5), which are evaluated and compared using metrics assessing\nsummary quality attributes. The generated summaries are linked back to the\noriginal file and sentence IDs, ensuring traceability and allowing for\nverification of the summarised information. Results demonstrate BART's\nstrengths in creating informative and concise summaries.\n", "date": "2024-07-11", "categories": []}
{"id": "2407.08328", "title": "Unveiling Disparities in Maternity Care: A Topic Modelling Approach to\n  Analysing Maternity Incident Investigation Reports", "abstract": "  This study applies Natural Language Processing techniques, including Latent\nDirichlet Allocation, to analyse anonymised maternity incident investigation\nreports from the Healthcare Safety Investigation Branch. The reports underwent\npreprocessing, annotation using the Safety Intelligence Research taxonomy, and\ntopic modelling to uncover prevalent topics and detect differences in maternity\ncare across ethnic groups. A combination of offline and online methods was\nutilised to ensure data protection whilst enabling advanced analysis, with\noffline processing for sensitive data and online processing for non-sensitive\ndata using the `Claude 3 Opus' language model. Interactive topic analysis and\nsemantic network visualisation were employed to extract and display thematic\ntopics and visualise semantic relationships among keywords. The analysis\nrevealed disparities in care among different ethnic groups, with distinct focus\nareas for the Black, Asian, and White British ethnic groups. The study\ndemonstrates the effectiveness of topic modelling and NLP techniques in\nanalysing maternity incident investigation reports and highlighting disparities\nin care. The findings emphasise the crucial role of advanced data analysis in\nimproving maternity care quality and equity.\n", "date": "2024-07-11", "categories": []}
{"id": "2403.18938", "title": "Reshaping Free-Text Radiology Notes Into Structured Reports With\n  Generative Transformers", "abstract": "  BACKGROUND: Radiology reports are typically written in a free-text format,\nmaking clinical information difficult to extract and use. Recently the adoption\nof structured reporting (SR) has been recommended by various medical societies\nthanks to the advantages it offers, e.g. standardization, completeness and\ninformation retrieval. We propose a pipeline to extract information from\nfree-text radiology reports, that fits with the items of the reference SR\nregistry proposed by a national society of interventional and medical\nradiology, focusing on CT staging of patients with lymphoma. METHODS: Our work\naims to leverage the potential of Natural Language Processing (NLP) and\nTransformer-based models to deal with automatic SR registry filling. With the\navailability of 174 radiology reports, we investigate a rule-free generative\nQuestion Answering approach based on a domain-specific version of T5 (IT5). Two\nstrategies (batch-truncation and ex-post combination) are implemented to comply\nwith the model's context length limitations. Performance is evaluated in terms\nof strict accuracy, F1, and format accuracy, and compared with the widely used\nGPT-3.5 Large Language Model. A 5-point Likert scale questionnaire is used to\ncollect human-expert feedback on the similarity between medical annotations and\ngenerated answers. RESULTS: The combination of fine-tuning and batch splitting\nallows IT5 to achieve notable results; it performs on par with GPT-3.5 albeit\nits size being a thousand times smaller in terms of parameters. Human-based\nassessment scores show a high correlation (Spearman's correlation\ncoefficients>0.88, p-values<0.001) with AI performance metrics (F1) and confirm\nthe superior ability of LLMs (i.e., GPT-3.5, 175B of parameters) in generating\nplausible human-like statements.\n", "date": "2024-03-27", "categories": []}
{"id": "2404.17977", "title": "Advancing Healthcare Automation: Multi-Agent System for Medical\n  Necessity Justification", "abstract": "  Prior Authorization delivers safe, appropriate, and cost-effective care that\nis medically justified with evidence-based guidelines. However, the process\noften requires labor-intensive manual comparisons between patient medical\nrecords and clinical guidelines, that is both repetitive and time-consuming.\nRecent developments in Large Language Models (LLMs) have shown potential in\naddressing complex medical NLP tasks with minimal supervision. This paper\nexplores the application of Multi-Agent System (MAS) that utilize specialized\nLLM agents to automate Prior Authorization task by breaking them down into\nsimpler and manageable sub-tasks. Our study systematically investigates the\neffects of various prompting strategies on these agents and benchmarks the\nperformance of different LLMs. We demonstrate that GPT-4 achieves an accuracy\nof 86.2% in predicting checklist item-level judgments with evidence, and 95.6%\nin determining overall checklist judgment. Additionally, we explore how these\nagents can contribute to explainability of steps taken in the process, thereby\nenhancing trust and transparency in the system.\n", "date": "2024-04-27", "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": "2407.05213", "title": "BadCLM: Backdoor Attack in Clinical Language Models for Electronic\n  Health Records", "abstract": "  The advent of clinical language models integrated into electronic health\nrecords (EHR) for clinical decision support has marked a significant\nadvancement, leveraging the depth of clinical notes for improved\ndecision-making. Despite their success, the potential vulnerabilities of these\nmodels remain largely unexplored. This paper delves into the realm of backdoor\nattacks on clinical language models, introducing an innovative attention-based\nbackdoor attack method, BadCLM (Bad Clinical Language Models). This technique\nclandestinely embeds a backdoor within the models, causing them to produce\nincorrect predictions when a pre-defined trigger is present in inputs, while\nfunctioning accurately otherwise. We demonstrate the efficacy of BadCLM through\nan in-hospital mortality prediction task with MIMIC III dataset, showcasing its\npotential to compromise model integrity. Our findings illuminate a significant\nsecurity risk in clinical decision support systems and pave the way for future\nendeavors in fortifying clinical language models against such vulnerabilities.\n", "date": "2024-07-06", "categories": []}
{"id": "2407.05480", "title": "Biomedical Nested NER with Large Language Model and UMLS Heuristics", "abstract": "  In this paper, we present our system for the BioNNE English track, which aims\nto extract 8 types of biomedical nested named entities from biomedical text. We\nuse a large language model (Mixtral 8x7B instruct) and ScispaCy NER model to\nidentify entities in an article and build custom heuristics based on unified\nmedical language system (UMLS) semantic types to categorize the entities. We\ndiscuss the results and limitations of our system and propose future\nimprovements. Our system achieved an F1 score of 0.39 on the BioNNE validation\nset and 0.348 on the test set.\n", "date": "2024-07-07", "categories": []}
{"id": "2407.05887", "title": "Generation and De-Identification of Indian Clinical Discharge Summaries\n  using LLMs", "abstract": "  The consequences of a healthcare data breach can be devastating for the\npatients, providers, and payers. The average financial impact of a data breach\nin recent months has been estimated to be close to USD 10 million. This is\nespecially significant for healthcare organizations in India that are managing\nrapid digitization while still establishing data governance procedures that\nalign with the letter and spirit of the law. Computer-based systems for\nde-identification of personal information are vulnerable to data drift, often\nrendering them ineffective in cross-institution settings. Therefore, a rigorous\nassessment of existing de-identification against local health datasets is\nimperative to support the safe adoption of digital health initiatives in India.\nUsing a small set of de-identified patient discharge summaries provided by an\nIndian healthcare institution, in this paper, we report the nominal performance\nof de-identification algorithms (based on language models) trained on publicly\navailable non-Indian datasets, pointing towards a lack of cross-institutional\ngeneralization. Similarly, experimentation with off-the-shelf de-identification\nsystems reveals potential risks associated with the approach. To overcome data\nscarcity, we explore generating synthetic clinical reports (using publicly\navailable and Indian summaries) by performing in-context learning over Large\nLanguage Models (LLMs). Our experiments demonstrate the use of generated\nreports as an effective strategy for creating high-performing de-identification\nsystems with good generalization capabilities.\n", "date": "2024-07-08", "categories": []}
{"id": "2311.09684", "title": "Do Physicians Know How to Prompt? The Need for Automatic Prompt\n  Optimization Help in Clinical Note Generation", "abstract": "  This study examines the effect of prompt engineering on the performance of\nLarge Language Models (LLMs) in clinical note generation. We introduce an\nAutomatic Prompt Optimization (APO) framework to refine initial prompts and\ncompare the outputs of medical experts, non-medical experts, and APO-enhanced\nGPT3.5 and GPT4. Results highlight GPT4 APO's superior performance in\nstandardizing prompt quality across clinical note sections. A human-in-the-loop\napproach shows that experts maintain content quality post-APO, with a\npreference for their own modifications, suggesting the value of expert\ncustomization. We recommend a two-phase optimization process, leveraging\nAPO-GPT4 for consistency and expert input for personalization.\n", "date": "2023-11-16", "categories": []}
{"id": "2407.04466", "title": "Using LLMs to label medical papers according to the CIViC evidence model", "abstract": "  We introduce the sequence classification problem CIViC Evidence to the field\nof medical NLP. CIViC Evidence denotes the multi-label classification problem\nof assigning labels of clinical evidence to abstracts of scientific papers\nwhich have examined various combinations of genomic variants, cancer types, and\ntreatment approaches. We approach CIViC Evidence using different language\nmodels: We fine-tune pretrained checkpoints of BERT and RoBERTa on the CIViC\nEvidence dataset and challenge their performance with models of the same\narchitecture which have been pretrained on domain-specific text. In this\ncontext, we find that BiomedBERT and BioLinkBERT can outperform BERT on CIViC\nEvidence (+0.8% and +0.9% absolute improvement in class-support weighted F1\nscore). All transformer-based models show a clear performance edge when\ncompared to a logistic regression trained on bigram tf-idf scores (+1.5 - 2.7%\nimproved F1 score). We compare the aforementioned BERT-like models to OpenAI's\nGPT-4 in a few-shot setting (on a small subset of our original test dataset),\ndemonstrating that, without additional prompt-engineering or fine-tuning, GPT-4\nperforms worse on CIViC Evidence than our six fine-tuned models (66.1% weighted\nF1 score compared to 71.8% for the best fine-tuned model). However, performance\ngets reasonably close to the benchmark of a logistic regression model trained\non bigram tf-idf scores (67.7% weighted F1 score).\n", "date": "2024-07-05", "categories": []}
{"id": "2407.02723", "title": "e-Health CSIRO at \"Discharge Me!\" 2024: Generating Discharge Summary\n  Sections with Fine-tuned Language Models", "abstract": "  Clinical documentation is an important aspect of clinicians' daily work and\noften demands a significant amount of time. The BioNLP 2024 Shared Task on\nStreamlining Discharge Documentation (Discharge Me!) aims to alleviate this\ndocumentation burden by automatically generating discharge summary sections,\nincluding brief hospital course and discharge instruction, which are often\ntime-consuming to synthesize and write manually. We approach the generation\ntask by fine-tuning multiple open-sourced language models (LMs), including both\ndecoder-only and encoder-decoder LMs, with various configurations on input\ncontext. We also examine different setups for decoding algorithms, model\nensembling or merging, and model specialization. Our results show that\nconditioning on the content of discharge summary prior to the target sections\nis effective for the generation task. Furthermore, we find that smaller\nencoder-decoder LMs can work as well or even slightly better than larger\ndecoder based LMs fine-tuned through LoRA. The model checkpoints from our team\n(aehrc) are openly available.\n", "date": "2024-07-03", "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": "2407.01948", "title": "Extracting and Encoding: Leveraging Large Language Models and Medical\n  Knowledge to Enhance Radiological Text Representation", "abstract": "  Advancing representation learning in specialized fields like medicine remains\nchallenging due to the scarcity of expert annotations for text and images. To\ntackle this issue, we present a novel two-stage framework designed to extract\nhigh-quality factual statements from free-text radiology reports in order to\nimprove the representations of text encoders and, consequently, their\nperformance on various downstream tasks. In the first stage, we propose a\n\\textit{Fact Extractor} that leverages large language models (LLMs) to identify\nfactual statements from well-curated domain-specific datasets. In the second\nstage, we introduce a \\textit{Fact Encoder} (CXRFE) based on a BERT model\nfine-tuned with objective functions designed to improve its representations\nusing the extracted factual data. Our framework also includes a new\nembedding-based metric (CXRFEScore) for evaluating chest X-ray text generation\nsystems, leveraging both stages of our approach. Extensive evaluations show\nthat our fact extractor and encoder outperform current state-of-the-art methods\nin tasks such as sentence ranking, natural language inference, and label\nextraction from radiology reports. Additionally, our metric proves to be more\nrobust and effective than existing metrics commonly used in the radiology\nreport generation literature. The code of this project is available at\n\\url{https://github.com/PabloMessina/CXR-Fact-Encoder}.\n", "date": "2024-07-02", "categories": []}
{"id": "2407.02432", "title": "Evaluating the Robustness of Adverse Drug Event Classification Models\n  Using Templates", "abstract": "  An adverse drug effect (ADE) is any harmful event resulting from medical drug\ntreatment. Despite their importance, ADEs are often under-reported in official\nchannels. Some research has therefore turned to detecting discussions of ADEs\nin social media. Impressive results have been achieved in various attempts to\ndetect ADEs. In a high-stakes domain such as medicine, however, an in-depth\nevaluation of a model's abilities is crucial. We address the issue of thorough\nperformance evaluation in English-language ADE detection with hand-crafted\ntemplates for four capabilities: Temporal order, negation, sentiment, and\nbeneficial effect. We find that models with similar performance on held-out\ntest sets have varying results on these capabilities.\n", "date": "2024-07-02", "categories": []}
{"id": "2407.00242", "title": "EHRmonize: A Framework for Medical Concept Abstraction from Electronic\n  Health Records using Large Language Models", "abstract": "  Electronic health records (EHRs) contain vast amounts of complex data, but\nharmonizing and processing this information remains a challenging and costly\ntask requiring significant clinical expertise. While large language models\n(LLMs) have shown promise in various healthcare applications, their potential\nfor abstracting medical concepts from EHRs remains largely unexplored. We\nintroduce EHRmonize, a framework leveraging LLMs to abstract medical concepts\nfrom EHR data. Our study uses medication data from two real-world EHR databases\nto evaluate five LLMs on two free-text extraction and six binary classification\ntasks across various prompting strategies. GPT-4o's with 10-shot prompting\nachieved the highest performance in all tasks, accompanied by Claude-3.5-Sonnet\nin a subset of tasks. GPT-4o achieved an accuracy of 97% in identifying generic\nroute names, 82% for generic drug names, and 100% in performing binary\nclassification of antibiotics. While EHRmonize significantly enhances\nefficiency, reducing annotation time by an estimated 60%, we emphasize that\nclinician oversight remains essential. Our framework, available as a Python\npackage, offers a promising tool to assist clinicians in EHR data abstraction,\npotentially accelerating healthcare research and improving data harmonization\nprocesses.\n", "date": "2024-06-28", "categories": []}
{"id": "2308.12568", "title": "A Small and Fast BERT for Chinese Medical Punctuation Restoration", "abstract": "  In clinical dictation, utterances after automatic speech recognition (ASR)\nwithout explicit punctuation marks may lead to the misunderstanding of dictated\nreports. To give a precise and understandable clinical report with ASR,\nautomatic punctuation restoration is required. Considering a practical\nscenario, we propose a fast and light pre-trained model for Chinese medical\npunctuation restoration based on 'pretraining and fine-tuning' paradigm. In\nthis work, we distill pre-trained models by incorporating supervised\ncontrastive learning and a novel auxiliary pre-training task (Punctuation Mark\nPrediction) to make it well-suited for punctuation restoration. Our experiments\non various distilled models reveal that our model can achieve 95% performance\nwhile 10% model size relative to state-of-the-art Chinese RoBERTa.\n", "date": "2023-08-24", "categories": []}
{"id": "2306.12656", "title": "Identifying and Extracting Rare Disease Phenotypes with Large Language\n  Models", "abstract": "  Rare diseases (RDs) are collectively common and affect 300 million people\nworldwide. Accurate phenotyping is critical for informing diagnosis and\ntreatment, but RD phenotypes are often embedded in unstructured text and\ntime-consuming to extract manually. While natural language processing (NLP)\nmodels can perform named entity recognition (NER) to automate extraction, a\nmajor bottleneck is the development of a large, annotated corpus for model\ntraining. Recently, prompt learning emerged as an NLP paradigm that can lead to\nmore generalizable results without any (zero-shot) or few labeled samples\n(few-shot). Despite growing interest in ChatGPT, a revolutionary large language\nmodel capable of following complex human prompts and generating high-quality\nresponses, none have studied its NER performance for RDs in the zero- and\nfew-shot settings. To this end, we engineered novel prompts aimed at extracting\nRD phenotypes and, to the best of our knowledge, are the first the establish a\nbenchmark for evaluating ChatGPT's performance in these settings. We compared\nits performance to the traditional fine-tuning approach and conducted an\nin-depth error analysis. Overall, fine-tuning BioClinicalBERT resulted in\nhigher performance (F1 of 0.689) than ChatGPT (F1 of 0.472 and 0.591 in the\nzero- and few-shot settings, respectively). Despite this, ChatGPT achieved\nsimilar or higher accuracy for certain entities (i.e., rare diseases and signs)\nin the one-shot setting (F1 of 0.776 and 0.725). This suggests that with\nappropriate prompt engineering, ChatGPT has the potential to match or\noutperform fine-tuned language models for certain entity types with just one\nlabeled sample. While the proliferation of large language models may provide\nopportunities for supporting RD diagnosis and treatment, researchers and\nclinicians should critically evaluate model outputs and be well-informed of\ntheir limitations.\n", "date": "2023-06-22", "categories": []}
{"id": "2406.00041", "title": "QUB-Cirdan at \"Discharge Me!\": Zero shot discharge letter generation by\n  open-source LLM", "abstract": "  The BioNLP ACL'24 Shared Task on Streamlining Discharge Documentation aims to\nreduce the administrative burden on clinicians by automating the creation of\ncritical sections of patient discharge letters. This paper presents our\napproach using the Llama3 8B quantized model to generate the \"Brief Hospital\nCourse\" and \"Discharge Instructions\" sections. We employ a zero-shot method\ncombined with Retrieval-Augmented Generation (RAG) to produce concise,\ncontextually accurate summaries. Our contributions include the development of a\ncurated template-based approach to ensure reliability and consistency, as well\nas the integration of RAG for word count prediction. We also describe several\nunsuccessful experiments to provide insights into our pathway for the\ncompetition. Our results demonstrate the effectiveness and efficiency of our\napproach, achieving high scores across multiple evaluation metrics.\n", "date": "2024-05-27", "categories": []}
{"id": "2406.18859", "title": "Two-Pronged Human Evaluation of ChatGPT Self-Correction in Radiology\n  Report Simplification", "abstract": "  Radiology reports are highly technical documents aimed primarily at\ndoctor-doctor communication. There has been an increasing interest in sharing\nthose reports with patients, necessitating providing them patient-friendly\nsimplifications of the original reports. This study explores the suitability of\nlarge language models in automatically generating those simplifications. We\nexamine the usefulness of chain-of-thought and self-correction prompting\nmechanisms in this domain. We also propose a new evaluation protocol that\nemploys radiologists and laypeople, where radiologists verify the factual\ncorrectness of simplifications, and laypeople assess simplicity and\ncomprehension. Our experimental results demonstrate the effectiveness of\nself-correction prompting in producing high-quality simplifications. Our\nfindings illuminate the preferences of radiologists and laypeople regarding\ntext simplification, informing future research on this topic.\n", "date": "2024-06-27", "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": "2406.18094", "title": "Shimo Lab at \"Discharge Me!\": Discharge Summarization by Prompt-Driven\n  Concatenation of Electronic Health Record Sections", "abstract": "  In this paper, we present our approach to the shared task \"Discharge Me!\" at\nthe BioNLP Workshop 2024. The primary goal of this task is to reduce the time\nand effort clinicians spend on writing detailed notes in the electronic health\nrecord (EHR). Participants develop a pipeline to generate the \"Brief Hospital\nCourse\" and \"Discharge Instructions\" sections from the EHR. Our approach\ninvolves a first step of extracting the relevant sections from the EHR. We then\nadd explanatory prompts to these sections and concatenate them with separate\ntokens to create the input text. To train a text generation model, we perform\nLoRA fine-tuning on the ClinicalT5-large model. On the final test data, our\napproach achieved a ROUGE-1 score of $0.394$, which is comparable to the top\nsolutions.\n", "date": "2024-06-26", "categories": []}
{"id": "2406.15963", "title": "Effectiveness of ChatGPT in explaining complex medical reports to\n  patients", "abstract": "  Electronic health records contain detailed information about the medical\ncondition of patients, but they are difficult for patients to understand even\nif they have access to them. We explore whether ChatGPT (GPT 4) can help\nexplain multidisciplinary team (MDT) reports to colorectal and prostate cancer\npatients. These reports are written in dense medical language and assume\nclinical knowledge, so they are a good test of the ability of ChatGPT to\nexplain complex medical reports to patients. We asked clinicians and lay people\n(not patients) to review explanations and responses of ChatGPT. We also ran\nthree focus groups (including cancer patients, caregivers, computer scientists,\nand clinicians) to discuss output of ChatGPT. Our studies highlighted issues\nwith inaccurate information, inappropriate language, limited personalization,\nAI distrust, and challenges integrating large language models (LLMs) into\nclinical workflow. These issues will need to be resolved before LLMs can be\nused to explain complex personal medical information to patients.\n", "date": "2024-06-23", "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": "2307.09758", "title": "Longitudinal Data and a Semantic Similarity Reward for Chest X-Ray\n  Report Generation", "abstract": "  Radiologists face high burnout rates, partially due to the increasing volume\nof Chest X-rays (CXRs) requiring interpretation and reporting. Automated CXR\nreport generation holds promise for reducing this burden and improving patient\ncare. While current models show potential, their diagnostic accuracy is\nlimited. Our proposed CXR report generator integrates elements of the\nradiologist workflow and introduces a novel reward for reinforcement learning.\nOur approach leverages longitudinal data from a patient's prior CXR study and\neffectively handles cases where no prior study exist, thus mirroring the\nradiologist's workflow. In contrast, existing models typically lack this\nflexibility, often requiring prior studies for the model to function optimally.\nOur approach also incorporates all CXRs from a patient's study and\ndistinguishes between report sections through section embeddings. Our reward\nfor reinforcement learning leverages CXR-BERT, which forces our model to learn\nthe clinical semantics of radiology reporting. We conduct experiments on\npublicly available datasets -- MIMIC-CXR and Open-i IU X-ray -- with metrics\nshown to more closely correlate with radiologists' assessment of reporting.\nResults from our study demonstrate that the proposed model generates reports\nthat are more aligned with radiologists' reports than state-of-the-art models,\nsuch as those utilising large language models, reinforcement learning, and\nmulti-task learning. The proposed model improves the diagnostic accuracy of CXR\nreport generation, which could one day reduce radiologists' workload and\nenhance patient care. Our Hugging Face checkpoint\n(https://huggingface.co/aehrc/cxrmate) and code\n(https://github.com/aehrc/cxrmate) are publicly available.\n", "date": "2023-07-19", "categories": []}
{"id": "2310.07059", "title": "DKEC: Domain Knowledge Enhanced Multi-Label Classification for Diagnosis\n  Prediction", "abstract": "  Multi-label text classification (MLTC) tasks in the medical domain often face\nthe long-tail label distribution problem. Prior works have explored\nhierarchical label structures to find relevant information for few-shot\nclasses, but mostly neglected to incorporate external knowledge from medical\nguidelines. This paper presents DKEC, Domain Knowledge Enhanced Classification\nfor diagnosis prediction with two innovations: (1) automated construction of\nheterogeneous knowledge graphs from external sources to capture semantic\nrelations among diverse medical entities, (2) incorporating the heterogeneous\nknowledge graphs in few-shot classification using a label-wise attention\nmechanism. We construct DKEC using three online medical knowledge sources and\nevaluate it on a real-world Emergency Medical Services (EMS) dataset and a\npublic electronic health record (EHR) dataset. Results show that DKEC\noutperforms the state-of-the-art label-wise attention networks and transformer\nmodels of different sizes, particularly for the few-shot classes. More\nimportantly, it helps the smaller language models achieve comparable\nperformance to large language models.\n", "date": "2023-10-10", "categories": []}
{"id": "2406.00014", "title": "KU-DMIS at EHRSQL 2024:Generating SQL query via question templatization\n  in EHR", "abstract": "  Transforming natural language questions into SQL queries is crucial for\nprecise data retrieval from electronic health record (EHR) databases. A\nsignificant challenge in this process is detecting and rejecting unanswerable\nquestions that request information beyond the database's scope or exceed the\nsystem's capabilities. In this paper, we introduce a novel text-to-SQL\nframework that robustly handles out-of-domain questions and verifies the\ngenerated queries with query execution.Our framework begins by standardizing\nthe structure of questions into a templated format. We use a powerful large\nlanguage model (LLM), fine-tuned GPT-3.5 with detailed prompts involving the\ntable schemas of the EHR database system. Our experimental results demonstrate\nthe effectiveness of our framework on the EHRSQL-2024 benchmark benchmark, a\nshared task in the ClinicalNLP workshop. Although a straightforward fine-tuning\nof GPT shows promising results on the development set, it struggled with the\nout-of-domain questions in the test set. With our framework, we improve our\nsystem's adaptability and achieve competitive performances in the official\nleaderboard of the EHRSQL-2024 challenge.\n", "date": "2024-05-22", "categories": []}
{"id": "2406.12066", "title": "Language Models are Surprisingly Fragile to Drug Names in Biomedical\n  Benchmarks", "abstract": "  Medical knowledge is context-dependent and requires consistent reasoning\nacross various natural language expressions of semantically equivalent phrases.\nThis is particularly crucial for drug names, where patients often use brand\nnames like Advil or Tylenol instead of their generic equivalents. To study\nthis, we create a new robustness dataset, RABBITS, to evaluate performance\ndifferences on medical benchmarks after swapping brand and generic drug names\nusing physician expert annotations.\n  We assess both open-source and API-based LLMs on MedQA and MedMCQA, revealing\na consistent performance drop ranging from 1-10\\%. Furthermore, we identify a\npotential source of this fragility as the contamination of test data in widely\nused pre-training datasets. All code is accessible at\nhttps://github.com/BittermanLab/RABBITS, and a HuggingFace leaderboard is\navailable at https://huggingface.co/spaces/AIM-Harvard/rabbits-leaderboard.\n", "date": "2024-06-17", "categories": []}
{"id": "2406.14312", "title": "Infusing clinical knowledge into tokenisers for language models", "abstract": "  This study introduces a novel knowledge enhanced tokenisation mechanism,\nK-Tokeniser, for clinical text processing. Technically, at initialisation\nstage, K-Tokeniser populates global representations of tokens based on semantic\ntypes of domain concepts (such as drugs or diseases) from either a domain\nontology like Unified Medical Language System or the training data of the task\nrelated corpus. At training or inference stage, sentence level localised\ncontext will be utilised for choosing the optimal global token representation\nto realise the semantic-based tokenisation. To avoid pretraining using the new\ntokeniser, an embedding initialisation approach is proposed to generate\nrepresentations for new tokens. Using three transformer-based language models,\na comprehensive set of experiments are conducted on four real-world datasets\nfor evaluating K-Tokeniser in a wide range of clinical text analytics tasks\nincluding clinical concept and relation extraction, automated clinical coding,\nclinical phenotype identification, and clinical research article\nclassification. Overall, our models demonstrate consistent improvements over\ntheir counterparts in all tasks. In particular, substantial improvements are\nobserved in the automated clinical coding task with 13\\% increase on Micro\n$F_1$ score. Furthermore, K-Tokeniser also shows significant capacities in\nfacilitating quicker converge of language models. Specifically, using\nK-Tokeniser, the language models would only require 50\\% of the training data\nto achieve the best performance of the baseline tokeniser using all training\ndata in the concept extraction task and less than 20\\% of the data for the\nautomated coding task. It is worth mentioning that all these improvements\nrequire no pre-training process, making the approach generalisable.\n", "date": "2024-06-20", "categories": []}
{"id": "2406.12687", "title": "Using LLMs to Aid Annotation and Collection of Clinically-Enriched Data\n  in Bipolar Disorder and Schizophrenia", "abstract": "  NLP in mental health has been primarily social media focused. Real world\npractitioners also have high case loads and often domain specific variables, of\nwhich modern LLMs lack context. We take a dataset made by recruiting 644\nparticipants, including individuals diagnosed with Bipolar Disorder (BD),\nSchizophrenia (SZ), and Healthy Controls (HC). Participants undertook tasks\nderived from a standardized mental health instrument, and the resulting data\nwere transcribed and annotated by experts across five clinical variables. This\npaper demonstrates the application of contemporary language models in\nsequence-to-sequence tasks to enhance mental health research. Specifically, we\nillustrate how these models can facilitate the deployment of mental health\ninstruments, data collection, and data annotation with high accuracy and\nscalability. We show that small models are capable of annotation for\ndomain-specific clinical variables, data collection for mental-health\ninstruments, and perform better then commercial large models.\n", "date": "2024-06-18", "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": "2406.11486", "title": "Analysing zero-shot temporal relation extraction on clinical notes using\n  temporal consistency", "abstract": "  This paper presents the first study for temporal relation extraction in a\nzero-shot setting focusing on biomedical text. We employ two types of prompts\nand five LLMs (GPT-3.5, Mixtral, Llama 2, Gemma, and PMC-LLaMA) to obtain\nresponses about the temporal relations between two events. Our experiments\ndemonstrate that LLMs struggle in the zero-shot setting performing worse than\nfine-tuned specialized models in terms of F1 score, showing that this is a\nchallenging task for LLMs. We further contribute a novel comprehensive temporal\nanalysis by calculating consistency scores for each LLM. Our findings reveal\nthat LLMs face challenges in providing responses consistent to the temporal\nproperties of uniqueness and transitivity. Moreover, we study the relation\nbetween the temporal consistency of an LLM and its accuracy and whether the\nlatter can be improved by solving temporal inconsistencies. Our analysis shows\nthat even when temporal consistency is achieved, the predictions can remain\ninaccurate.\n", "date": "2024-06-17", "categories": []}
{"id": "2311.09066", "title": "Identifying Self-Disclosures of Use, Misuse and Addiction in\n  Community-based Social Media Posts", "abstract": "  In the last decade, the United States has lost more than 500,000 people from\nan overdose involving prescription and illicit opioids making it a national\npublic health emergency (USDHHS, 2017). Medical practitioners require robust\nand timely tools that can effectively identify at-risk patients.\nCommunity-based social media platforms such as Reddit allow self-disclosure for\nusers to discuss otherwise sensitive drug-related behaviors. We present a\nmoderate size corpus of 2500 opioid-related posts from various subreddits\nlabeled with six different phases of opioid use: Medical Use, Misuse,\nAddiction, Recovery, Relapse, Not Using. For every post, we annotate span-level\nextractive explanations and crucially study their role both in annotation\nquality and model development. We evaluate several state-of-the-art models in a\nsupervised, few-shot, or zero-shot setting. Experimental results and error\nanalysis show that identifying the phases of opioid use disorder is highly\ncontextual and challenging. However, we find that using explanations during\nmodeling leads to a significant boost in classification accuracy demonstrating\ntheir beneficial role in a high-stakes domain such as studying the opioid use\ndisorder continuum.\n", "date": "2023-11-15", "categories": []}
{"id": "2406.06455", "title": "A Large Language Model Pipeline for Breast Cancer Oncology", "abstract": "  Large language models (LLMs) have demonstrated potential in the innovation of\nmany disciplines. However, how they can best be developed for oncology remains\nunderdeveloped. State-of-the-art OpenAI models were fine-tuned on a clinical\ndataset and clinical guidelines text corpus for two important cancer treatment\nfactors, adjuvant radiation therapy and chemotherapy, using a novel Langchain\nprompt engineering pipeline. A high accuracy (0.85+) was achieved in the\nclassification of adjuvant radiation therapy and chemotherapy for breast cancer\npatients. Furthermore, a confidence interval was formed from observational data\non the quality of treatment from human oncologists to estimate the proportion\nof scenarios in which the model must outperform the original oncologist in its\ntreatment prediction to be a better solution overall as 8.2% to 13.3%. Due to\nindeterminacy in the outcomes of cancer treatment decisions, future\ninvestigation, potentially a clinical trial, would be required to determine if\nthis threshold was met by the models. Nevertheless, with 85% of U.S. cancer\npatients receiving treatment at local community facilities, these kinds of\nmodels could play an important part in expanding access to quality care with\noutcomes that lie, at minimum, close to a human oncologist.\n", "date": "2024-06-10", "categories": []}
{"id": "2406.10040", "title": "FZI-WIM at SemEval-2024 Task 2: Self-Consistent CoT for Complex NLI in\n  Biomedical Domain", "abstract": "  This paper describes the inference system of FZI-WIM at the SemEval-2024 Task\n2: Safe Biomedical Natural Language Inference for Clinical Trials. Our system\nutilizes the chain of thought (CoT) paradigm to tackle this complex reasoning\nproblem and further improves the CoT performance with self-consistency. Instead\nof greedy decoding, we sample multiple reasoning chains with the same prompt\nand make the final verification with majority voting. The self-consistent CoT\nsystem achieves a baseline F1 score of 0.80 (1st), faithfulness score of 0.90\n(3rd), and consistency score of 0.73 (12th). We release the code and data\npublicly https://github.com/jens5588/FZI-WIM-NLI4CT.\n", "date": "2024-06-14", "categories": []}
{"id": "2306.01931", "title": "Simple Data Augmentation Techniques for Chinese Disease Normalization", "abstract": "  Disease name normalization is an important task in the medical domain. It\nclassifies disease names written in various formats into standardized names,\nserving as a fundamental component in smart healthcare systems for various\ndisease-related functions. Nevertheless, the most significant obstacle to\nexisting disease name normalization systems is the severe shortage of training\ndata. Consequently, we present a novel data augmentation approach that includes\na series of data augmentation techniques and some supporting modules to help\nmitigate the problem. Our proposed methods rely on the Structural Invariance\nproperty of disease names and the Hierarchy property of the disease\nclassification system. The goal is to equip the models with extensive\nunderstanding of the disease names and the hierarchical structure of the\ndisease name classification system. Through extensive experimentation, we\nillustrate that our proposed approach exhibits significant performance\nimprovements across various baseline models and training objectives,\nparticularly in scenarios with limited training data.\n", "date": "2023-06-02", "categories": []}
{"id": "2406.08881", "title": "No perspective, no perception!! Perspective-aware Healthcare Answer\n  Summarization", "abstract": "  Healthcare Community Question Answering (CQA) forums offer an accessible\nplatform for individuals seeking information on various healthcare-related\ntopics. People find such platforms suitable for self-disclosure, seeking\nmedical opinions, finding simplified explanations for their medical conditions,\nand answering others' questions. However, answers on these forums are typically\ndiverse and prone to off-topic discussions. It can be challenging for readers\nto sift through numerous answers and extract meaningful insights, making answer\nsummarization a crucial task for CQA forums. While several efforts have been\nmade to summarize the community answers, most of them are limited to the open\ndomain and overlook the different perspectives offered by these answers. To\naddress this problem, this paper proposes a novel task of perspective-specific\nanswer summarization. We identify various perspectives, within\nhealthcare-related responses and frame a perspective-driven abstractive summary\ncovering all responses. To achieve this, we annotate 3167 CQA threads with 6193\nperspective-aware summaries in our PUMA dataset. Further, we propose PLASMA, a\nprompt-driven controllable summarization model. To encapsulate the\nperspective-specific conditions, we design an energy-controlled loss function\nfor the optimization. We also leverage the prefix tuner to learn the\nintricacies of the health-care perspective summarization. Our evaluation\nagainst five baselines suggests the superior performance of PLASMA by a margin\nof 1.5-21% improvement. We supplement our experiments with ablation and\nqualitative analysis.\n", "date": "2024-06-13", "categories": []}
{"id": "2406.09103", "title": "Chain-of-Though (CoT) prompting strategies for medical error detection\n  and correction", "abstract": "  This paper describes our submission to the MEDIQA-CORR 2024 shared task for\nautomatically detecting and correcting medical errors in clinical notes. We\nreport results for three methods of few-shot In-Context Learning (ICL)\naugmented with Chain-of-Thought (CoT) and reason prompts using a large language\nmodel (LLM). In the first method, we manually analyse a subset of train and\nvalidation dataset to infer three CoT prompts by examining error types in the\nclinical notes. In the second method, we utilise the training dataset to prompt\nthe LLM to deduce reasons about their correctness or incorrectness. The\nconstructed CoTs and reasons are then augmented with ICL examples to solve the\ntasks of error detection, span identification, and error correction. Finally,\nwe combine the two methods using a rule-based ensemble method. Across the three\nsub-tasks, our ensemble method achieves a ranking of 3rd for both sub-task 1\nand 2, while securing 7th place in sub-task 3 among all submissions.\n", "date": "2024-06-13", "categories": []}
{"id": "2406.07922", "title": "Automated Information Extraction from Thyroid Operation Narrative: A\n  Comparative Study of GPT-4 and Fine-tuned KoELECTRA", "abstract": "  In the rapidly evolving field of healthcare, the integration of artificial\nintelligence (AI) has become a pivotal component in the automation of clinical\nworkflows, ushering in a new era of efficiency and accuracy. This study focuses\non the transformative capabilities of the fine-tuned KoELECTRA model in\ncomparison to the GPT-4 model, aiming to facilitate automated information\nextraction from thyroid operation narratives. The current research landscape is\ndominated by traditional methods heavily reliant on regular expressions, which\noften face challenges in processing free-style text formats containing critical\ndetails of operation records, including frozen biopsy reports. Addressing this,\nthe study leverages advanced natural language processing (NLP) techniques to\nfoster a paradigm shift towards more sophisticated data processing systems.\nThrough this comparative study, we aspire to unveil a more streamlined,\nprecise, and efficient approach to document processing in the healthcare\ndomain, potentially revolutionizing the way medical data is handled and\nanalyzed.\n", "date": "2024-06-12", "categories": []}
{"id": "2309.04550", "title": "Retrieving Evidence from EHRs with LLMs: Possibilities and Challenges", "abstract": "  Unstructured data in Electronic Health Records (EHRs) often contains critical\ninformation -- complementary to imaging -- that could inform radiologists'\ndiagnoses. But the large volume of notes often associated with patients\ntogether with time constraints renders manually identifying relevant evidence\npractically infeasible. In this work we propose and evaluate a zero-shot\nstrategy for using LLMs as a mechanism to efficiently retrieve and summarize\nunstructured evidence in patient EHR relevant to a given query. Our method\nentails tasking an LLM to infer whether a patient has, or is at risk of, a\nparticular condition on the basis of associated notes; if so, we ask the model\nto summarize the supporting evidence. Under expert evaluation, we find that\nthis LLM-based approach provides outputs consistently preferred to a pre-LLM\ninformation retrieval baseline. Manual evaluation is expensive, so we also\npropose and validate a method using an LLM to evaluate (other) LLM outputs for\nthis task, allowing us to scale up evaluation. Our findings indicate the\npromise of LLMs as interfaces to EHR, but also highlight the outstanding\nchallenge posed by \"hallucinations\". In this setting, however, we show that\nmodel confidence in outputs strongly correlates with faithful summaries,\noffering a practical means to limit confabulations.\n", "date": "2023-09-08", "categories": []}
{"id": "2406.06569", "title": "Enhancing Clinical Documentation with Synthetic Data: Leveraging\n  Generative Models for Improved Accuracy", "abstract": "  Accurate and comprehensive clinical documentation is crucial for delivering\nhigh-quality healthcare, facilitating effective communication among providers,\nand ensuring compliance with regulatory requirements. However, manual\ntranscription and data entry processes can be time-consuming, error-prone, and\nsusceptible to inconsistencies, leading to incomplete or inaccurate medical\nrecords. This paper proposes a novel approach to augment clinical documentation\nby leveraging synthetic data generation techniques to generate realistic and\ndiverse clinical transcripts. We present a methodology that combines\nstate-of-the-art generative models, such as Generative Adversarial Networks\n(GANs) and Variational Autoencoders (VAEs), with real-world clinical transcript\nand other forms of clinical data to generate synthetic transcripts. These\nsynthetic transcripts can then be used to supplement existing documentation\nworkflows, providing additional training data for natural language processing\nmodels and enabling more accurate and efficient transcription processes.\nThrough extensive experiments on a large dataset of anonymized clinical\ntranscripts, we demonstrate the effectiveness of our approach in generating\nhigh-quality synthetic transcripts that closely resemble real-world data.\nQuantitative evaluation metrics, including perplexity scores and BLEU scores,\nas well as qualitative assessments by domain experts, validate the fidelity and\nutility of the generated synthetic transcripts. Our findings highlight\nsynthetic data generation's potential to address clinical documentation\nchallenges, improving patient care, reducing administrative burdens, and\nenhancing healthcare system efficiency.\n", "date": "2024-06-03", "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": "2307.12114", "title": "A Zero-shot and Few-shot Study of Instruction-Finetuned Large Language\n  Models Applied to Clinical and Biomedical Tasks", "abstract": "  We evaluate four state-of-the-art instruction-tuned large language models\n(LLMs) -- ChatGPT, Flan-T5 UL2, Tk-Instruct, and Alpaca -- on a set of 13\nreal-world clinical and biomedical natural language processing (NLP) tasks in\nEnglish, such as named-entity recognition (NER), question-answering (QA),\nrelation extraction (RE), etc. Our overall results demonstrate that the\nevaluated LLMs begin to approach performance of state-of-the-art models in\nzero- and few-shot scenarios for most tasks, and particularly well for the QA\ntask, even though they have never seen examples from these tasks before.\nHowever, we observed that the classification and RE tasks perform below what\ncan be achieved with a specifically trained model for the medical field, such\nas PubMedBERT. Finally, we noted that no LLM outperforms all the others on all\nthe studied tasks, with some models being better suited for certain tasks than\nothers.\n", "date": "2023-07-22", "categories": []}
{"id": "2402.13432", "title": "DrBenchmark: A Large Language Understanding Evaluation Benchmark for\n  French Biomedical Domain", "abstract": "  The biomedical domain has sparked a significant interest in the field of\nNatural Language Processing (NLP), which has seen substantial advancements with\npre-trained language models (PLMs). However, comparing these models has proven\nchallenging due to variations in evaluation protocols across different models.\nA fair solution is to aggregate diverse downstream tasks into a benchmark,\nallowing for the assessment of intrinsic PLMs qualities from various\nperspectives. Although still limited to few languages, this initiative has been\nundertaken in the biomedical field, notably English and Chinese. This\nlimitation hampers the evaluation of the latest French biomedical models, as\nthey are either assessed on a minimal number of tasks with non-standardized\nprotocols or evaluated using general downstream tasks. To bridge this research\ngap and account for the unique sensitivities of French, we present the\nfirst-ever publicly available French biomedical language understanding\nbenchmark called DrBenchmark. It encompasses 20 diversified tasks, including\nnamed-entity recognition, part-of-speech tagging, question-answering, semantic\ntextual similarity, and classification. We evaluate 8 state-of-the-art\npre-trained masked language models (MLMs) on general and biomedical-specific\ndata, as well as English specific MLMs to assess their cross-lingual\ncapabilities. Our experiments reveal that no single model excels across all\ntasks, while generalist models are sometimes still competitive.\n", "date": "2024-02-20", "categories": []}
{"id": "2402.15010", "title": "How Important Is Tokenization in French Medical Masked Language Models?", "abstract": "  Subword tokenization has become the prevailing standard in the field of\nnatural language processing (NLP) over recent years, primarily due to the\nwidespread utilization of pre-trained language models. This shift began with\nByte-Pair Encoding (BPE) and was later followed by the adoption of\nSentencePiece and WordPiece. While subword tokenization consistently\noutperforms character and word-level tokenization, the precise factors\ncontributing to its success remain unclear. Key aspects such as the optimal\nsegmentation granularity for diverse tasks and languages, the influence of data\nsources on tokenizers, and the role of morphological information in\nIndo-European languages remain insufficiently explored. This is particularly\npertinent for biomedical terminology, characterized by specific rules governing\nmorpheme combinations. Despite the agglutinative nature of biomedical\nterminology, existing language models do not explicitly incorporate this\nknowledge, leading to inconsistent tokenization strategies for common terms. In\nthis paper, we seek to delve into the complexities of subword tokenization in\nFrench biomedical domain across a variety of NLP tasks and pinpoint areas where\nfurther enhancements can be made. We analyze classical tokenization algorithms,\nincluding BPE and SentencePiece, and introduce an original tokenization\nstrategy that integrates morpheme-enriched word segmentation into existing\ntokenization methods.\n", "date": "2024-02-22", "categories": []}
{"id": "2406.05307", "title": "DeviceBERT: Applied Transfer Learning With Targeted Annotations and\n  Vocabulary Enrichment to Identify Medical Device and Component Terminology in\n  FDA Recall Summaries", "abstract": "  FDA Medical Device recalls are critical and time-sensitive events, requiring\nswift identification of impacted devices to inform the public of a recall event\nand ensure patient safety. The OpenFDA device recall dataset contains valuable\ninformation about ongoing device recall actions, but manually extracting\nrelevant device information from the recall action summaries is a\ntime-consuming task. Named Entity Recognition (NER) is a task in Natural\nLanguage Processing (NLP) that involves identifying and categorizing named\nentities in unstructured text. Existing NER models, including domain-specific\nmodels like BioBERT, struggle to correctly identify medical device trade names,\npart numbers and component terms within these summaries. To address this, we\npropose DeviceBERT, a medical device annotation, pre-processing and enrichment\npipeline, which builds on BioBERT to identify and label medical device\nterminology in the device recall summaries with improved accuracy. Furthermore,\nwe demonstrate that our approach can be applied effectively for performing\nentity recognition tasks where training data is limited or sparse.\n", "date": "2024-06-08", "categories": []}
{"id": "2406.05505", "title": "I-SIRch: AI-Powered Concept Annotation Tool For Equitable Extraction And\n  Analysis Of Safety Insights From Maternity Investigations", "abstract": "  Maternity care is a complex system involving treatments and interactions\nbetween patients, providers, and the care environment. To improve patient\nsafety and outcomes, understanding the human factors (e.g. individuals\ndecisions, local facilities) influencing healthcare delivery is crucial.\nHowever, most current tools for analysing healthcare data focus only on\nbiomedical concepts (e.g. health conditions, procedures and tests), overlooking\nthe importance of human factors. We developed a new approach called I-SIRch,\nusing artificial intelligence to automatically identify and label human factors\nconcepts in maternity healthcare investigation reports describing adverse\nmaternity incidents produced by England's Healthcare Safety Investigation\nBranch (HSIB). These incident investigation reports aim to identify\nopportunities for learning and improving maternal safety across the entire\nhealthcare system. I-SIRch was trained using real data and tested on both real\nand simulated data to evaluate its performance in identifying human factors\nconcepts. When applied to real reports, the model achieved a high level of\naccuracy, correctly identifying relevant concepts in 90\\% of the sentences from\n97 reports. Applying I-SIRch to analyse these reports revealed that certain\nhuman factors disproportionately affected mothers from different ethnic groups.\nOur work demonstrates the potential of using automated tools to identify human\nfactors concepts in maternity incident investigation reports, rather than\nfocusing solely on biomedical concepts. This approach opens up new\npossibilities for understanding the complex interplay between social,\ntechnical, and organisational factors influencing maternal safety and\npopulation health outcomes. By taking a more comprehensive view of maternal\nhealthcare delivery, we can develop targeted interventions to address\ndisparities and improve maternal outcomes.\n", "date": "2024-06-08", "categories": []}
{"id": "2406.05995", "title": "A Dual-View Approach to Classifying Radiology Reports by Co-Training", "abstract": "  Radiology report analysis provides valuable information that can aid with\npublic health initiatives, and has been attracting increasing attention from\nthe research community. In this work, we present a novel insight that the\nstructure of a radiology report (namely, the Findings and Impression sections)\noffers different views of a radiology scan. Based on this intuition, we further\npropose a co-training approach, where two machine learning models are built\nupon the Findings and Impression sections, respectively, and use each other's\ninformation to boost performance with massive unlabeled data in a\nsemi-supervised manner. We conducted experiments in a public health\nsurveillance study, and results show that our co-training approach is able to\nimprove performance using the dual views and surpass competing supervised and\nsemi-supervised methods.\n", "date": "2024-06-10", "categories": []}
{"id": "2310.19975", "title": "BioInstruct: Instruction Tuning of Large Language Models for Biomedical\n  Natural Language Processing", "abstract": "  To enhance the performance of large language models (LLMs) in biomedical\nnatural language processing (BioNLP) by introducing a domain-specific\ninstruction dataset and examining its impact when combined with multi-task\nlearning principles. We created the BioInstruct, comprising 25,005 instructions\nto instruction-tune LLMs(LLaMA 1 & 2, 7B & 13B version). The instructions were\ncreated by prompting the GPT-4 language model with three-seed samples randomly\ndrawn from an 80 human curated instructions. We employed Low-Rank\nAdaptation(LoRA) for parameter-efficient fine-tuning. We then evaluated these\ninstruction-tuned LLMs on several BioNLP tasks, which can be grouped into three\nmajor categories: question answering(QA), information extraction(IE), and text\ngeneration(GEN). We also examined whether categories(e.g., QA, IE, and\ngeneration) of instructions impact model performance. Comparing with LLMs\nwithout instruction-tuned, our instruction-tuned LLMs demonstrated marked\nperformance gains: 17.3% in QA, 5.7% in IE, and 96% in Generation tasks. Our\n7B-parameter instruction-tuned LLaMA 1 model was competitive or even surpassed\nother LLMs in the biomedical domain that were also fine-tuned from LLaMA 1 with\nvast domain-specific data or a variety of tasks. Our results also show that the\nperformance gain is significantly higher when instruction fine-tuning is\nconducted with closely related tasks. Our findings align with the observations\nof multi-task learning, suggesting the synergies between two tasks. The\nBioInstruct dataset serves as a valuable resource and instruction tuned LLMs\nlead to the best performing BioNLP applications.\n", "date": "2023-10-30", "categories": []}
{"id": "2402.11456", "title": "FactPICO: Factuality Evaluation for Plain Language Summarization of\n  Medical Evidence", "abstract": "  Plain language summarization with LLMs can be useful for improving textual\naccessibility of technical content. But how factual are these summaries in a\nhigh-stakes domain like medicine? This paper presents FactPICO, a factuality\nbenchmark for plain language summarization of medical texts describing\nrandomized controlled trials (RCTs), which are the basis of evidence-based\nmedicine and can directly inform patient treatment. FactPICO consists of 345\nplain language summaries of RCT abstracts generated from three LLMs (i.e.,\nGPT-4, Llama-2, and Alpaca), with fine-grained evaluation and natural language\nrationales from experts. We assess the factuality of critical elements of RCTs\nin those summaries: Populations, Interventions, Comparators, Outcomes (PICO),\nas well as the reported findings concerning these. We also evaluate the\ncorrectness of the extra information (e.g., explanations) added by LLMs. Using\nFactPICO, we benchmark a range of existing factuality metrics, including the\nnewly devised ones based on LLMs. We find that plain language summarization of\nmedical evidence is still challenging, especially when balancing between\nsimplicity and factuality, and that existing metrics correlate poorly with\nexpert judgments on the instance level.\n", "date": "2024-02-18", "categories": []}
{"id": "2406.03062", "title": "RadBARTsum: Domain Specific Adaption of Denoising Sequence-to-Sequence\n  Models for Abstractive Radiology Report Summarization", "abstract": "  Radiology report summarization is a crucial task that can help doctors\nquickly identify clinically significant findings without the need to review\ndetailed sections of reports. This study proposes RadBARTsum, a domain-specific\nand ontology facilitated adaptation of the BART model for abstractive radiology\nreport summarization. The approach involves two main steps: 1) re-training the\nBART model on a large corpus of radiology reports using a novel entity masking\nstrategy to improving biomedical domain knowledge learning, and 2) fine-tuning\nthe model for the summarization task using the Findings and Background sections\nto predict the Impression section. Experiments are conducted using different\nmasking strategies. Results show that the re-training process with domain\nknowledge facilitated masking improves performances consistently across various\nsettings. This work contributes a domain-specific generative language model for\nradiology report summarization and a method for utilising medical knowledge to\nrealise entity masking language model. The proposed approach demonstrates a\npromising direction of enhancing the efficiency of language models by deepening\nits understanding of clinical knowledge in radiology reports.\n", "date": "2024-06-05", "categories": []}
{"id": "2406.00062", "title": "Unlocking the Potential of Large Language Models for Clinical Text\n  Anonymization: A Comparative Study", "abstract": "  Automated clinical text anonymization has the potential to unlock the\nwidespread sharing of textual health data for secondary usage while assuring\npatient privacy and safety. Despite the proposal of many complex and\ntheoretically successful anonymization solutions in literature, these\ntechniques remain flawed. As such, clinical institutions are still reluctant to\napply them for open access to their data. Recent advances in developing Large\nLanguage Models (LLMs) pose a promising opportunity to further the field, given\ntheir capability to perform various tasks. This paper proposes six new\nevaluation metrics tailored to the challenges of generative anonymization with\nLLMs. Moreover, we present a comparative study of LLM-based methods, testing\nthem against two baseline techniques. Our results establish LLM-based models as\na reliable alternative to common approaches, paving the way toward trustworthy\nanonymization of clinical text.\n", "date": "2024-05-29", "categories": []}
{"id": "2406.00015", "title": "Use of natural language processing to extract and classify papillary\n  thyroid cancer features from surgical pathology reports", "abstract": "  Background We aim to use Natural Language Processing (NLP) to automate the\nextraction and classification of thyroid cancer risk factors from pathology\nreports. Methods We analyzed 1,410 surgical pathology reports from adult\npapillary thyroid cancer patients at Mayo Clinic, Rochester, MN, from 2010 to\n2019. Structured and non-structured reports were used to create a\nconsensus-based ground truth dictionary and categorized them into modified\nrecurrence risk levels. Non-structured reports were narrative, while structured\nreports followed standardized formats. We then developed ThyroPath, a\nrule-based NLP pipeline, to extract and classify thyroid cancer features into\nrisk categories. Training involved 225 reports (150 structured, 75\nunstructured), with testing on 170 reports (120 structured, 50 unstructured)\nfor evaluation. The pipeline's performance was assessed using both strict and\nlenient criteria for accuracy, precision, recall, and F1-score. Results In\nextraction tasks, ThyroPath achieved overall strict F-1 scores of 93% for\nstructured reports and 90 for unstructured reports, covering 18 thyroid cancer\npathology features. In classification tasks, ThyroPath-extracted information\ndemonstrated an overall accuracy of 93% in categorizing reports based on their\ncorresponding guideline-based risk of recurrence: 76.9% for high-risk, 86.8%\nfor intermediate risk, and 100% for both low and very low-risk cases. However,\nThyroPath achieved 100% accuracy across all thyroid cancer risk categories with\nhuman-extracted pathology information. Conclusions ThyroPath shows promise in\nautomating the extraction and risk recurrence classification of thyroid\npathology reports at large scale. It offers a solution to laborious manual\nreviews and advancing virtual registries. However, it requires further\nvalidation before implementation.\n", "date": "2024-05-22", "categories": []}
{"id": "2406.00016", "title": "Exploration of Attention Mechanism-Enhanced Deep Learning Models in the\n  Mining of Medical Textual Data", "abstract": "  The research explores the utilization of a deep learning model employing an\nattention mechanism in medical text mining. It targets the challenge of\nanalyzing unstructured text information within medical data. This research\nseeks to enhance the model's capability to identify essential medical\ninformation by incorporating deep learning and attention mechanisms. This paper\nreviews the basic principles and typical model architecture of attention\nmechanisms and shows the effectiveness of their application in the tasks of\ndisease prediction, drug side effect monitoring, and entity relationship\nextraction. Aiming at the particularity of medical texts, an adaptive attention\nmodel integrating domain knowledge is proposed, and its ability to understand\nmedical terms and process complex contexts is optimized. The experiment\nverifies the model's effectiveness in improving task accuracy and robustness,\nespecially when dealing with long text. The future research path of enhancing\nmodel interpretation, realizing cross-domain knowledge transfer, and adapting\nto low-resource scenarios is discussed in the research outlook, which provides\na new perspective and method support for intelligent medical information\nprocessing and clinical decision assistance. Finally, cross-domain knowledge\ntransfer and adaptation strategies for low-resource scenarios, providing\ntheoretical basis and technical reference for promoting the development of\nintelligent medical information processing and clinical decision support\nsystems.\n", "date": "2024-05-23", "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": "2309.16150", "title": "AE-GPT: Using Large Language Models to Extract Adverse Events from\n  Surveillance Reports-A Use Case with Influenza Vaccine Adverse Events", "abstract": "  Though Vaccines are instrumental in global health, mitigating infectious\ndiseases and pandemic outbreaks, they can occasionally lead to adverse events\n(AEs). Recently, Large Language Models (LLMs) have shown promise in effectively\nidentifying and cataloging AEs within clinical reports. Utilizing data from the\nVaccine Adverse Event Reporting System (VAERS) from 1990 to 2016, this study\nparticularly focuses on AEs to evaluate LLMs' capability for AE extraction. A\nvariety of prevalent LLMs, including GPT-2, GPT-3 variants, GPT-4, and Llama 2,\nwere evaluated using Influenza vaccine as a use case. The fine-tuned GPT 3.5\nmodel (AE-GPT) stood out with a 0.704 averaged micro F1 score for strict match\nand 0.816 for relaxed match. The encouraging performance of the AE-GPT\nunderscores LLMs' potential in processing medical data, indicating a\nsignificant stride towards advanced AE detection, thus presumably generalizable\nto other AE extraction tasks.\n", "date": "2023-09-28", "categories": []}
{"id": "2405.19084", "title": "Auxiliary Knowledge-Induced Learning for Automatic Multi-Label Medical\n  Document Classification", "abstract": "  The International Classification of Diseases (ICD) is an authoritative\nmedical classification system of different diseases and conditions for clinical\nand management purposes. ICD indexing assigns a subset of ICD codes to a\nmedical record. Since human coding is labour-intensive and error-prone, many\nstudies employ machine learning to automate the coding process. ICD coding is a\nchallenging task, as it needs to assign multiple codes to each medical document\nfrom an extremely large hierarchically organized collection. In this paper, we\npropose a novel approach for ICD indexing that adopts three ideas: (1) we use a\nmulti-level deep dilated residual convolution encoder to aggregate the\ninformation from the clinical notes and learn document representations across\ndifferent lengths of the texts; (2) we formalize the task of ICD classification\nwith auxiliary knowledge of the medical records, which incorporates not only\nthe clinical texts but also different clinical code terminologies and drug\nprescriptions for better inferring the ICD codes; and (3) we introduce a graph\nconvolutional network to leverage the co-occurrence patterns among ICD codes,\naiming to enhance the quality of label representations. Experimental results\nshow the proposed method achieves state-of-the-art performance on a number of\nmeasures.\n", "date": "2024-05-29", "categories": []}
{"id": "2405.19093", "title": "Multi-stage Retrieve and Re-rank Model for Automatic Medical Coding\n  Recommendation", "abstract": "  The International Classification of Diseases (ICD) serves as a definitive\nmedical classification system encompassing a wide range of diseases and\nconditions. The primary objective of ICD indexing is to allocate a subset of\nICD codes to a medical record, which facilitates standardized documentation and\nmanagement of various health conditions. Most existing approaches have suffered\nfrom selecting the proper label subsets from an extremely large ICD collection\nwith a heavy long-tailed label distribution. In this paper, we leverage a\nmulti-stage ``retrieve and re-rank'' framework as a novel solution to ICD\nindexing, via a hybrid discrete retrieval method, and re-rank retrieved\ncandidates with contrastive learning that allows the model to make more\naccurate predictions from a simplified label space. The retrieval model is a\nhybrid of auxiliary knowledge of the electronic health records (EHR) and a\ndiscrete retrieval method (BM25), which efficiently collects high-quality\ncandidates. In the last stage, we propose a label co-occurrence guided\ncontrastive re-ranking model, which re-ranks the candidate labels by pulling\ntogether the clinical notes with positive ICD codes. Experimental results show\nthe proposed method achieves state-of-the-art performance on a number of\nmeasures on the MIMIC-III benchmark.\n", "date": "2024-05-29", "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": "2405.18028", "title": "Edinburgh Clinical NLP at MEDIQA-CORR 2024: Guiding Large Language\n  Models with Hints", "abstract": "  The MEDIQA-CORR 2024 shared task aims to assess the ability of Large Language\nModels (LLMs) to identify and correct medical errors in clinical notes. In this\nstudy, we evaluate the capability of general LLMs, specifically GPT-3.5 and\nGPT-4, to identify and correct medical errors with multiple prompting\nstrategies. Recognising the limitation of LLMs in generating accurate\ncorrections only via prompting strategies, we propose incorporating error-span\npredictions from a smaller, fine-tuned model in two ways: 1) by presenting it\nas a hint in the prompt and 2) by framing it as multiple-choice questions from\nwhich the LLM can choose the best correction. We found that our proposed\nprompting strategies significantly improve the LLM's ability to generate\ncorrections. Our best-performing solution with 8-shot + CoT + hints ranked\nsixth in the shared task leaderboard. Additionally, our comprehensive analyses\nshow the impact of the location of the error sentence, the prompted role, and\nthe position of the multiple-choice option on the accuracy of the LLM. This\nprompts further questions about the readiness of LLM to be implemented in\nreal-world clinical settings.\n", "date": "2024-05-28", "categories": []}
{"id": "2405.18346", "title": "Intelligent Clinical Documentation: Harnessing Generative AI for\n  Patient-Centric Clinical Note Generation", "abstract": "  Comprehensive clinical documentation is crucial for effective healthcare\ndelivery, yet it poses a significant burden on healthcare professionals,\nleading to burnout, increased medical errors, and compromised patient safety.\nThis paper explores the potential of generative AI (Artificial Intelligence) to\nstreamline the clinical documentation process, specifically focusing on\ngenerating SOAP (Subjective, Objective, Assessment, Plan) and BIRP (Behavior,\nIntervention, Response, Plan) notes. We present a case study demonstrating the\napplication of natural language processing (NLP) and automatic speech\nrecognition (ASR) technologies to transcribe patient-clinician interactions,\ncoupled with advanced prompting techniques to generate draft clinical notes\nusing large language models (LLMs). The study highlights the benefits of this\napproach, including time savings, improved documentation quality, and enhanced\npatient-centered care. Additionally, we discuss ethical considerations, such as\nmaintaining patient confidentiality and addressing model biases, underscoring\nthe need for responsible deployment of generative AI in healthcare settings.\nThe findings suggest that generative AI has the potential to revolutionize\nclinical documentation practices, alleviating administrative burdens and\nenabling healthcare professionals to focus more on direct patient care.\n", "date": "2024-05-28", "categories": []}
{"id": "2405.16115", "title": "SNOBERT: A Benchmark for clinical notes entity linking in the SNOMED CT\n  clinical terminology", "abstract": "  The extraction and analysis of insights from medical data, primarily stored\nin free-text formats by healthcare workers, presents significant challenges due\nto its unstructured nature. Medical coding, a crucial process in healthcare,\nremains minimally automated due to the complexity of medical ontologies and\nrestricted access to medical texts for training Natural Language Processing\nmodels. In this paper, we proposed a method, \"SNOBERT,\" of linking text spans\nin clinical notes to specific concepts in the SNOMED CT using BERT-based\nmodels. The method consists of two stages: candidate selection and candidate\nmatching. The models were trained on one of the largest publicly available\ndataset of labeled clinical notes. SNOBERT outperforms other classical methods\nbased on deep learning, as confirmed by the results of a challenge in which it\nwas applied.\n", "date": "2024-05-25", "categories": []}
{"id": "2405.15122", "title": "Generalizable and Scalable Multistage Biomedical Concept Normalization\n  Leveraging Large Language Models", "abstract": "  Background: Biomedical entity normalization is critical to biomedical\nresearch because the richness of free-text clinical data, such as progress\nnotes, can often be fully leveraged only after translating words and phrases\ninto structured and coded representations suitable for analysis. Large Language\nModels (LLMs), in turn, have shown great potential and high performance in a\nvariety of natural language processing (NLP) tasks, but their application for\nnormalization remains understudied.\n  Methods: We applied both proprietary and open-source LLMs in combination with\nseveral rule-based normalization systems commonly used in biomedical research.\nWe used a two-step LLM integration approach, (1) using an LLM to generate\nalternative phrasings of a source utterance, and (2) to prune candidate UMLS\nconcepts, using a variety of prompting methods. We measure results by\n$F_{\\beta}$, where we favor recall over precision, and F1.\n  Results: We evaluated a total of 5,523 concept terms and text contexts from a\npublicly available dataset of human-annotated biomedical abstracts.\nIncorporating GPT-3.5-turbo increased overall $F_{\\beta}$ and F1 in\nnormalization systems +9.5 and +7.3 (MetaMapLite), +13.9 and +10.9 (QuickUMLS),\nand +10.5 and +10.3 (BM25), while the open-source Vicuna model achieved +10.8\nand +12.2 (MetaMapLite), +14.7 and +15 (QuickUMLS), and +15.6 and +18.7 (BM25).\n  Conclusions: Existing general-purpose LLMs, both propriety and open-source,\ncan be leveraged at scale to greatly improve normalization performance using\nexisting tools, with no fine-tuning.\n", "date": "2024-05-24", "categories": []}
{"id": "2405.01682", "title": "Leveraging Prompt-Learning for Structured Information Extraction from\n  Crohn's Disease Radiology Reports in a Low-Resource Language", "abstract": "  Automatic conversion of free-text radiology reports into structured data\nusing Natural Language Processing (NLP) techniques is crucial for analyzing\ndiseases on a large scale. While effective for tasks in widely spoken languages\nlike English, generative large language models (LLMs) typically underperform\nwith less common languages and can pose potential risks to patient privacy.\nFine-tuning local NLP models is hindered by the skewed nature of real-world\nmedical datasets, where rare findings represent a significant data imbalance.\nWe introduce SMP-BERT, a novel prompt learning method that leverages the\nstructured nature of reports to overcome these challenges. In our studies\ninvolving a substantial collection of Crohn's disease radiology reports in\nHebrew (over 8,000 patients and 10,000 reports), SMP-BERT greatly surpassed\ntraditional fine-tuning methods in performance, notably in detecting infrequent\nconditions (AUC: 0.99 vs 0.94, F1: 0.84 vs 0.34). SMP-BERT empowers more\naccurate AI diagnostics available for low-resource languages.\n", "date": "2024-05-02", "categories": []}
{"id": "2405.06673", "title": "Overview of the EHRSQL 2024 Shared Task on Reliable Text-to-SQL Modeling\n  on Electronic Health Records", "abstract": "  Electronic Health Records (EHRs) are relational databases that store the\nentire medical histories of patients within hospitals. They record numerous\naspects of patients' medical care, from hospital admission and diagnosis to\ntreatment and discharge. While EHRs are vital sources of clinical data,\nexploring them beyond a predefined set of queries requires skills in query\nlanguages like SQL. To make information retrieval more accessible, one strategy\nis to build a question-answering system, possibly leveraging text-to-SQL models\nthat can automatically translate natural language questions into corresponding\nSQL queries and use these queries to retrieve the answers. The EHRSQL 2024\nshared task aims to advance and promote research in developing a\nquestion-answering system for EHRs using text-to-SQL modeling, capable of\nreliably providing requested answers to various healthcare professionals to\nimprove their clinical work processes and satisfy their needs. Among more than\n100 participants who applied to the shared task, eight teams were formed and\ncompleted the entire shared task requirement and demonstrated a wide range of\nmethods to effectively solve this task. In this paper, we describe the task of\nreliable text-to-SQL modeling, the dataset, and the methods and results of the\nparticipants. We hope this shared task will spur further research and insights\ninto developing reliable question-answering systems for EHRs.\n", "date": "2024-05-04", "categories": []}
{"id": "2405.13219", "title": "How Reliable AI Chatbots are for Disease Prediction from Patient\n  Complaints?", "abstract": "  Artificial Intelligence (AI) chatbots leveraging Large Language Models (LLMs)\nare gaining traction in healthcare for their potential to automate patient\ninteractions and aid clinical decision-making. This study examines the\nreliability of AI chatbots, specifically GPT 4.0, Claude 3 Opus, and Gemini\nUltra 1.0, in predicting diseases from patient complaints in the emergency\ndepartment. The methodology includes few-shot learning techniques to evaluate\nthe chatbots' effectiveness in disease prediction. We also fine-tune the\ntransformer-based model BERT and compare its performance with the AI chatbots.\nResults suggest that GPT 4.0 achieves high accuracy with increased few-shot\ndata, while Gemini Ultra 1.0 performs well with fewer examples, and Claude 3\nOpus maintains consistent performance. BERT's performance, however, is lower\nthan all the chatbots, indicating limitations due to limited labeled data.\nDespite the chatbots' varying accuracy, none of them are sufficiently reliable\nfor critical medical decision-making, underscoring the need for rigorous\nvalidation and human oversight. This study reflects that while AI chatbots have\npotential in healthcare, they should complement, not replace, human expertise\nto ensure patient safety. Further refinement and research are needed to improve\nAI-based healthcare applications' reliability for disease prediction.\n", "date": "2024-05-21", "categories": []}
{"id": "2405.12619", "title": "MentalQA: An Annotated Arabic Corpus for Questions and Answers of Mental\n  Healthcare", "abstract": "  Mental health disorders significantly impact people globally, regardless of\nbackground, education, or socioeconomic status. However, access to adequate\ncare remains a challenge, particularly for underserved communities with limited\nresources. Text mining tools offer immense potential to support mental\nhealthcare by assisting professionals in diagnosing and treating patients. This\nstudy addresses the scarcity of Arabic mental health resources for developing\nsuch tools. We introduce MentalQA, a novel Arabic dataset featuring\nconversational-style question-and-answer (QA) interactions. To ensure data\nquality, we conducted a rigorous annotation process using a well-defined schema\nwith quality control measures. Data was collected from a question-answering\nmedical platform. The annotation schema for mental health questions and\ncorresponding answers draws upon existing classification schemes with some\nmodifications. Question types encompass six distinct categories: diagnosis,\ntreatment, anatomy \\& physiology, epidemiology, healthy lifestyle, and provider\nchoice. Answer strategies include information provision, direct guidance, and\nemotional support. Three experienced annotators collaboratively annotated the\ndata to ensure consistency. Our findings demonstrate high inter-annotator\nagreement, with Fleiss' Kappa of $0.61$ for question types and $0.98$ for\nanswer strategies. In-depth analysis revealed insightful patterns, including\nvariations in question preferences across age groups and a strong correlation\nbetween question types and answer strategies. MentalQA offers a valuable\nfoundation for developing Arabic text mining tools capable of supporting mental\nhealth professionals and individuals seeking information.\n", "date": "2024-05-21", "categories": []}
{"id": "2301.10451", "title": "Knowledge-augmented Graph Neural Networks with Concept-aware Attention\n  for Adverse Drug Event Detection", "abstract": "  Adverse drug events (ADEs) are an important aspect of drug safety. Various\ntexts such as biomedical literature, drug reviews, and user posts on social\nmedia and medical forums contain a wealth of information about ADEs. Recent\nstudies have applied word embedding and deep learning -based natural language\nprocessing to automate ADE detection from text. However, they did not explore\nincorporating explicit medical knowledge about drugs and adverse reactions or\nthe corresponding feature learning. This paper adopts the heterogenous text\ngraph which describes relationships between documents, words and concepts,\naugments it with medical knowledge from the Unified Medical Language System,\nand proposes a concept-aware attention mechanism which learns features\ndifferently for the different types of nodes in the graph. We further utilize\ncontextualized embeddings from pretrained language models and convolutional\ngraph neural networks for effective feature representation and relational\nlearning. Experiments on four public datasets show that our model achieves\nperformance competitive to the recent advances and the concept-aware attention\nconsistently outperforms other attention mechanisms.\n", "date": "2023-01-25", "categories": []}
{"id": "2405.11162", "title": "LG AI Research & KAIST at EHRSQL 2024: Self-Training Large Language\n  Models with Pseudo-Labeled Unanswerable Questions for a Reliable Text-to-SQL\n  System on EHRs", "abstract": "  Text-to-SQL models are pivotal for making Electronic Health Records (EHRs)\naccessible to healthcare professionals without SQL knowledge. With the\nadvancements in large language models, these systems have become more adept at\ntranslating complex questions into SQL queries. Nonetheless, the critical need\nfor reliability in healthcare necessitates these models to accurately identify\nunanswerable questions or uncertain predictions, preventing misinformation. To\naddress this problem, we present a self-training strategy using pseudo-labeled\nunanswerable questions to enhance the reliability of text-to-SQL models for\nEHRs. This approach includes a two-stage training process followed by a\nfiltering method based on the token entropy and query execution. Our\nmethodology's effectiveness is validated by our top performance in the EHRSQL\n2024 shared task, showcasing the potential to improve healthcare\ndecision-making through more reliable text-to-SQL systems.\n", "date": "2024-05-18", "categories": []}
{"id": "2405.11181", "title": "Towards Knowledge-Infused Automated Disease Diagnosis Assistant", "abstract": "  With the advancement of internet communication and telemedicine, people are\nincreasingly turning to the web for various healthcare activities. With an\never-increasing number of diseases and symptoms, diagnosing patients becomes\nchallenging. In this work, we build a diagnosis assistant to assist doctors,\nwhich identifies diseases based on patient-doctor interaction. During\ndiagnosis, doctors utilize both symptomatology knowledge and diagnostic\nexperience to identify diseases accurately and efficiently. Inspired by this,\nwe investigate the role of medical knowledge in disease diagnosis through\ndoctor-patient interaction. We propose a two-channel, knowledge-infused,\ndiscourse-aware disease diagnosis model (KI-DDI), where the first channel\nencodes patient-doctor communication using a transformer-based encoder, while\nthe other creates an embedding of symptom-disease using a graph attention\nnetwork (GAT). In the next stage, the conversation and knowledge graph\nembeddings are infused together and fed to a deep neural network for disease\nidentification. Furthermore, we first develop an empathetic conversational\nmedical corpus comprising conversations between patients and doctors, annotated\nwith intent and symptoms information. The proposed model demonstrates a\nsignificant improvement over the existing state-of-the-art models, establishing\nthe crucial roles of (a) a doctor's effort for additional symptom extraction\n(in addition to patient self-report) and (b) infusing medical knowledge in\nidentifying diseases effectively. Many times, patients also show their medical\nconditions, which acts as crucial evidence in diagnosis. Therefore, integrating\nvisual sensory information would represent an effective avenue for enhancing\nthe capabilities of diagnostic assistants.\n", "date": "2024-05-18", "categories": []}
{"id": "2405.11255", "title": "WisPerMed at \"Discharge Me!\": Advancing Text Generation in Healthcare\n  with Large Language Models, Dynamic Expert Selection, and Priming Techniques\n  on MIMIC-IV", "abstract": "  This study aims to leverage state of the art language models to automate\ngenerating the \"Brief Hospital Course\" and \"Discharge Instructions\" sections of\nDischarge Summaries from the MIMIC-IV dataset, reducing clinicians'\nadministrative workload. We investigate how automation can improve\ndocumentation accuracy, alleviate clinician burnout, and enhance operational\nefficacy in healthcare facilities. This research was conducted within our\nparticipation in the Shared Task Discharge Me! at BioNLP @ ACL 2024. Various\nstrategies were employed, including few-shot learning, instruction tuning, and\nDynamic Expert Selection (DES), to develop models capable of generating the\nrequired text sections. Notably, utilizing an additional clinical\ndomain-specific dataset demonstrated substantial potential to enhance clinical\nlanguage processing. The DES method, which optimizes the selection of text\noutputs from multiple predictions, proved to be especially effective. It\nachieved the highest overall score of 0.332 in the competition, surpassing\nsingle-model outputs. This finding suggests that advanced deep learning methods\nin combination with DES can effectively automate parts of electronic health\nrecord documentation. These advancements could enhance patient care by freeing\nclinician time for patient interactions. The integration of text selection\nstrategies represents a promising avenue for further research.\n", "date": "2024-05-18", "categories": []}
{"id": "2405.11941", "title": "Biomedical Entity Linking for Dutch: Fine-tuning a Self-alignment BERT\n  Model on an Automatically Generated Wikipedia Corpus", "abstract": "  Biomedical entity linking, a main component in automatic information\nextraction from health-related texts, plays a pivotal role in connecting\ntextual entities (such as diseases, drugs and body parts mentioned by patients)\nto their corresponding concepts in a structured biomedical knowledge base. The\ntask remains challenging despite recent developments in natural language\nprocessing. This paper presents the first evaluated biomedical entity linking\nmodel for the Dutch language. We use MedRoBERTa.nl as base model and perform\nsecond-phase pretraining through self-alignment on a Dutch biomedical ontology\nextracted from the UMLS and Dutch SNOMED. We derive a corpus from Wikipedia of\nontology-linked Dutch biomedical entities in context and fine-tune our model on\nthis dataset. We evaluate our model on the Dutch portion of the Mantra\nGSC-corpus and achieve 54.7% classification accuracy and 69.8% 1-distance\naccuracy. We then perform a case study on a collection of unlabeled,\npatient-support forum data and show that our model is hampered by the limited\nquality of the preceding entity recognition step. Manual evaluation of small\nsample indicates that of the correctly extracted entities, around 65% is linked\nto the correct concept in the ontology. Our results indicate that biomedical\nentity linking in a language other than English remains challenging, but our\nDutch model can be used to for high-level analysis of patient-generated text.\n", "date": "2024-05-20", "categories": []}
{"id": "2303.08179", "title": "MEDBERT.de: A Comprehensive German BERT Model for the Medical Domain", "abstract": "  This paper presents medBERTde, a pre-trained German BERT model specifically\ndesigned for the German medical domain. The model has been trained on a large\ncorpus of 4.7 Million German medical documents and has been shown to achieve\nnew state-of-the-art performance on eight different medical benchmarks covering\na wide range of disciplines and medical document types. In addition to\nevaluating the overall performance of the model, this paper also conducts a\nmore in-depth analysis of its capabilities. We investigate the impact of data\ndeduplication on the model's performance, as well as the potential benefits of\nusing more efficient tokenization methods. Our results indicate that\ndomain-specific models such as medBERTde are particularly useful for longer\ntexts, and that deduplication of training data does not necessarily lead to\nimproved performance. Furthermore, we found that efficient tokenization plays\nonly a minor role in improving model performance, and attribute most of the\nimproved performance to the large amount of training data. To encourage further\nresearch, the pre-trained model weights and new benchmarks based on\nradiological data are made publicly available for use by the scientific\ncommunity.\n", "date": "2023-03-14", "categories": []}
{"id": "2305.12723", "title": "Enhancing Small Medical Learners with Privacy-preserving Contextual\n  Prompting", "abstract": "  Large language models (LLMs) demonstrate remarkable medical expertise, but\ndata privacy concerns impede their direct use in healthcare environments.\nAlthough offering improved data privacy protection, domain-specific small\nlanguage models (SLMs) often underperform LLMs, emphasizing the need for\nmethods that reduce this performance gap while alleviating privacy concerns. In\nthis paper, we present a simple yet effective method that harnesses LLMs'\nmedical proficiency to boost SLM performance in medical tasks under\nprivacy-restricted scenarios. Specifically, we mitigate patient privacy issues\nby extracting keywords from medical data and prompting the LLM to generate a\nmedical knowledge-intensive context by simulating clinicians' thought\nprocesses. This context serves as additional input for SLMs, augmenting their\ndecision-making capabilities. Our method significantly enhances performance in\nboth few-shot and full training settings across three medical\nknowledge-intensive tasks, achieving up to a 22.57% increase in absolute\naccuracy compared to SLM fine-tuning without context, and sets new\nstate-of-the-art results in two medical tasks within privacy-restricted\nscenarios. Further out-of-domain testing and experiments in two general domain\ndatasets showcase its generalizability and broad applicability. Our code can be\nfound at https://github.com/XZhang97666/PrivacyBoost-SLM.\n", "date": "2023-05-22", "categories": []}
{"id": "2405.08839", "title": "PromptMind Team at EHRSQL-2024: Improving Reliability of SQL Generation\n  using Ensemble LLMs", "abstract": "  This paper presents our approach to the EHRSQL-2024 shared task, which aims\nto develop a reliable Text-to-SQL system for electronic health records. We\npropose two approaches that leverage large language models (LLMs) for prompting\nand fine-tuning to generate EHRSQL queries. In both techniques, we concentrate\non bridging the gap between the real-world knowledge on which LLMs are trained\nand the domain specific knowledge required for the task. The paper provides the\nresults of each approach individually, demonstrating that they achieve high\nexecution accuracy. Additionally, we show that an ensemble approach further\nenhances generation reliability by reducing errors. This approach secured us\n2nd place in the shared task competition. The methodologies outlined in this\npaper are designed to be transferable to domain-specific Text-to-SQL problems\nthat emphasize both accuracy and reliability.\n", "date": "2024-05-14", "categories": []}
{"id": "2405.09153", "title": "Adapting Abstract Meaning Representation Parsing to the Clinical\n  Narrative -- the SPRING THYME parser", "abstract": "  This paper is dedicated to the design and evaluation of the first AMR parser\ntailored for clinical notes. Our objective was to facilitate the precise\ntransformation of the clinical notes into structured AMR expressions, thereby\nenhancing the interpretability and usability of clinical text data at scale.\nLeveraging the colon cancer dataset from the Temporal Histories of Your Medical\nEvents (THYME) corpus, we adapted a state-of-the-art AMR parser utilizing\ncontinuous training. Our approach incorporates data augmentation techniques to\nenhance the accuracy of AMR structure predictions. Notably, through this\nlearning strategy, our parser achieved an impressive F1 score of 88% on the\nTHYME corpus's colon cancer dataset. Moreover, our research delved into the\nefficacy of data required for domain adaptation within the realm of clinical\nnotes, presenting domain adaptation data requirements for AMR parsing. This\nexploration not only underscores the parser's robust performance but also\nhighlights its potential in facilitating a deeper understanding of clinical\nnarratives through structured semantic representations.\n", "date": "2024-05-15", "categories": []}
{"id": "2405.09300", "title": "Comparing the Efficacy of GPT-4 and Chat-GPT in Mental Health Care: A\n  Blind Assessment of Large Language Models for Psychological Support", "abstract": "  Background: Rapid advancements in natural language processing have led to the\ndevelopment of large language models with the potential to revolutionize mental\nhealth care. These models have shown promise in assisting clinicians and\nproviding support to individuals experiencing various psychological challenges.\n  Objective: This study aims to compare the performance of two large language\nmodels, GPT-4 and Chat-GPT, in responding to a set of 18 psychological prompts,\nto assess their potential applicability in mental health care settings.\n  Methods: A blind methodology was employed, with a clinical psychologist\nevaluating the models' responses without knowledge of their origins. The\nprompts encompassed a diverse range of mental health topics, including\ndepression, anxiety, and trauma, to ensure a comprehensive assessment.\n  Results: The results demonstrated a significant difference in performance\nbetween the two models (p > 0.05). GPT-4 achieved an average rating of 8.29 out\nof 10, while Chat-GPT received an average rating of 6.52. The clinical\npsychologist's evaluation suggested that GPT-4 was more effective at generating\nclinically relevant and empathetic responses, thereby providing better support\nand guidance to potential users.\n  Conclusions: This study contributes to the growing body of literature on the\napplicability of large language models in mental health care settings. The\nfindings underscore the importance of continued research and development in the\nfield to optimize these models for clinical use. Further investigation is\nnecessary to understand the specific factors underlying the performance\ndifferences between the two models and to explore their generalizability across\nvarious populations and mental health conditions.\n", "date": "2024-05-15", "categories": []}
{"id": "2405.08373", "title": "PromptMind Team at MEDIQA-CORR 2024: Improving Clinical Text Correction\n  with Error Categorization and LLM Ensembles", "abstract": "  This paper describes our approach to the MEDIQA-CORR shared task, which\ninvolves error detection and correction in clinical notes curated by medical\nprofessionals. This task involves handling three subtasks: detecting the\npresence of errors, identifying the specific sentence containing the error, and\ncorrecting it. Through our work, we aim to assess the capabilities of Large\nLanguage Models (LLMs) trained on a vast corpora of internet data that contain\nboth factual and unreliable information. We propose to comprehensively address\nall subtasks together, and suggest employing a unique prompt-based in-context\nlearning strategy. We will evaluate its efficacy in this specialized task\ndemanding a combination of general reasoning and medical knowledge. In medical\nsystems where prediction errors can have grave consequences, we propose\nleveraging self-consistency and ensemble methods to enhance error correction\nand error detection performance.\n", "date": "2024-05-14", "categories": []}
{"id": "2405.06695", "title": "Utilizing Large Language Models to Generate Synthetic Data to Increase\n  the Performance of BERT-Based Neural Networks", "abstract": "  An important issue impacting healthcare is a lack of available experts.\nMachine learning (ML) models could resolve this by aiding in diagnosing\npatients. However, creating datasets large enough to train these models is\nexpensive. We evaluated large language models (LLMs) for data creation. Using\nAutism Spectrum Disorders (ASD), we prompted ChatGPT and GPT-Premium to\ngenerate 4,200 synthetic observations to augment existing medical data. Our\ngoal is to label behaviors corresponding to autism criteria and improve model\naccuracy with synthetic training data. We used a BERT classifier pre-trained on\nbiomedical literature to assess differences in performance between models. A\nrandom sample (N=140) from the LLM-generated data was evaluated by a clinician\nand found to contain 83% correct example-label pairs. Augmenting data increased\nrecall by 13% but decreased precision by 16%, correlating with higher quality\nand lower accuracy across pairs. Future work will analyze how different\nsynthetic data traits affect ML outcomes.\n", "date": "2024-05-08", "categories": []}
{"id": "2405.06712", "title": "Digital Diagnostics: The Potential Of Large Language Models In\n  Recognizing Symptoms Of Common Illnesses", "abstract": "  The recent swift development of LLMs like GPT-4, Gemini, and GPT-3.5 offers a\ntransformative opportunity in medicine and healthcare, especially in digital\ndiagnostics. This study evaluates each model diagnostic abilities by\ninterpreting a user symptoms and determining diagnoses that fit well with\ncommon illnesses, and it demonstrates how each of these models could\nsignificantly increase diagnostic accuracy and efficiency. Through a series of\ndiagnostic prompts based on symptoms from medical databases, GPT-4 demonstrates\nhigher diagnostic accuracy from its deep and complete history of training on\nmedical data. Meanwhile, Gemini performs with high precision as a critical tool\nin disease triage, demonstrating its potential to be a reliable model when\nphysicians are trying to make high-risk diagnoses. GPT-3.5, though slightly\nless advanced, is a good tool for medical diagnostics. This study highlights\nthe need to study LLMs for healthcare and clinical practices with more care and\nattention, ensuring that any system utilizing LLMs promotes patient privacy and\ncomplies with health information privacy laws such as HIPAA compliance, as well\nas the social consequences that affect the varied individuals in complex\nhealthcare contexts. This study marks the start of a larger future effort to\nstudy the various ways in which assigning ethical concerns to LLMs task of\nlearning from human biases could unearth new ways to apply AI in complex\nmedical settings.\n", "date": "2024-05-09", "categories": []}
{"id": "2405.07560", "title": "Coding historical causes of death data with Large Language Models", "abstract": "  This paper investigates the feasibility of using pre-trained generative Large\nLanguage Models (LLMs) to automate the assignment of ICD-10 codes to historical\ncauses of death. Due to the complex narratives often found in historical causes\nof death, this task has traditionally been manually performed by coding\nexperts. We evaluate the ability of GPT-3.5, GPT-4, and Llama 2 LLMs to\naccurately assign ICD-10 codes on the HiCaD dataset that contains causes of\ndeath recorded in the civil death register entries of 19,361 individuals from\nIpswich, Kilmarnock, and the Isle of Skye from the UK between 1861-1901. Our\nfindings show that GPT-3.5, GPT-4, and Llama 2 assign the correct code for 69%,\n83%, and 40% of causes, respectively. However, we achieve a maximum accuracy of\n89% by standard machine learning techniques. All LLMs performed better for\ncauses of death that contained terms still in use today, compared to archaic\nterms. Also they perform better for short causes (1-2 words) compared to longer\ncauses. LLMs therefore do not currently perform well enough for historical\nICD-10 code assignment tasks. We suggest further fine-tuning or alternative\nframeworks to achieve adequate performance.\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": "2404.16226", "title": "Computational analysis of the language of pain: a systematic review", "abstract": "  Objectives: This study aims to systematically review the literature on the\ncomputational processing of the language of pain, or pain narratives, whether\ngenerated by patients or physicians, identifying current trends and challenges.\nMethods: Following the PRISMA guidelines, a comprehensive literature search was\nconducted to select relevant studies on the computational processing of the\nlanguage of pain and answer pre-defined research questions. Data extraction and\nsynthesis were performed to categorize selected studies according to their\nprimary purpose and outcome, patient and pain population, textual data,\ncomputational methodology, and outcome targets. Results: Physician-generated\nlanguage of pain, specifically from clinical notes, was the most used data.\nTasks included patient diagnosis and triaging, identification of pain mentions,\ntreatment response prediction, biomedical entity extraction, correlation of\nlinguistic features with clinical states, and lexico-semantic analysis of pain\nnarratives. Only one study included previous linguistic knowledge on pain\nutterances in their experimental setup. Most studies targeted their outcomes\nfor physicians, either directly as clinical tools or as indirect knowledge. The\nleast targeted stage of clinical pain care was self-management, in which\npatients are most involved. Affective and sociocultural dimensions were the\nleast studied domains. Only one study measured how physician performance on\nclinical tasks improved with the inclusion of the proposed algorithm.\nDiscussion: This review found that future research should focus on analyzing\npatient-generated language of pain, developing patient-centered resources for\nself-management and patient-empowerment, exploring affective and sociocultural\naspects of pain, and measuring improvements in physician performance when aided\nby the proposed tools.\n", "date": "2024-04-24", "categories": []}
{"id": "2404.05694", "title": "Comprehensive Study on German Language Models for Clinical and\n  Biomedical Text Understanding", "abstract": "  Recent advances in natural language processing (NLP) can be largely\nattributed to the advent of pre-trained language models such as BERT and\nRoBERTa. While these models demonstrate remarkable performance on general\ndatasets, they can struggle in specialized domains such as medicine, where\nunique domain-specific terminologies, domain-specific abbreviations, and\nvarying document structures are common. This paper explores strategies for\nadapting these models to domain-specific requirements, primarily through\ncontinuous pre-training on domain-specific data. We pre-trained several German\nmedical language models on 2.4B tokens derived from translated public English\nmedical data and 3B tokens of German clinical data. The resulting models were\nevaluated on various German downstream tasks, including named entity\nrecognition (NER), multi-label classification, and extractive question\nanswering. Our results suggest that models augmented by clinical and\ntranslation-based pre-training typically outperform general domain models in\nmedical contexts. We conclude that continuous pre-training has demonstrated the\nability to match or even exceed the performance of clinical models trained from\nscratch. Furthermore, pre-training on clinical data or leveraging translated\ntexts have proven to be reliable methods for domain adaptation in medical NLP\ntasks.\n", "date": "2024-04-08", "categories": []}
{"id": "2405.04170", "title": "D-NLP at SemEval-2024 Task 2: Evaluating Clinical Inference Capabilities\n  of Large Language Models", "abstract": "  Large language models (LLMs) have garnered significant attention and\nwidespread usage due to their impressive performance in various tasks. However,\nthey are not without their own set of challenges, including issues such as\nhallucinations, factual inconsistencies, and limitations in\nnumerical-quantitative reasoning. Evaluating LLMs in miscellaneous reasoning\ntasks remains an active area of research. Prior to the breakthrough of LLMs,\nTransformers had already proven successful in the medical domain, effectively\nemployed for various natural language understanding (NLU) tasks. Following this\ntrend, LLMs have also been trained and utilized in the medical domain, raising\nconcerns regarding factual accuracy, adherence to safety protocols, and\ninherent limitations. In this paper, we focus on evaluating the natural\nlanguage inference capabilities of popular open-source and closed-source LLMs\nusing clinical trial reports as the dataset. We present the performance results\nof each LLM and further analyze their performance on a development set,\nparticularly focusing on challenging instances that involve medical\nabbreviations and require numerical-quantitative reasoning. Gemini, our leading\nLLM, achieved a test set F1-score of 0.748, securing the ninth position on the\ntask scoreboard. Our work is the first of its kind, offering a thorough\nexamination of the inference capabilities of LLMs within the medical domain.\n", "date": "2024-05-07", "categories": []}
{"id": "2405.04175", "title": "Topicwise Separable Sentence Retrieval for Medical Report Generation", "abstract": "  Automated radiology reporting holds immense clinical potential in alleviating\nthe burdensome workload of radiologists and mitigating diagnostic bias.\nRecently, retrieval-based report generation methods have garnered increasing\nattention due to their inherent advantages in terms of the quality and\nconsistency of generated reports. However, due to the long-tail distribution of\nthe training data, these models tend to learn frequently occurring sentences\nand topics, overlooking the rare topics. Regrettably, in many cases, the\ndescriptions of rare topics often indicate critical findings that should be\nmentioned in the report. To address this problem, we introduce a Topicwise\nSeparable Sentence Retrieval (Teaser) for medical report generation. To ensure\ncomprehensive learning of both common and rare topics, we categorize queries\ninto common and rare types to learn differentiated topics, and then propose\nTopic Contrastive Loss to effectively align topics and queries in the latent\nspace. Moreover, we integrate an Abstractor module following the extraction of\nvisual features, which aids the topic decoder in gaining a deeper understanding\nof the visual observational intent. Experiments on the MIMIC-CXR and IU X-ray\ndatasets demonstrate that Teaser surpasses state-of-the-art models, while also\nvalidating its capability to effectively represent rare topics and establish\nmore dependable correspondences between queries and topics.\n", "date": "2024-05-07", "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": "2405.00465", "title": "BiomedRAG: A Retrieval Augmented Large Language Model for Biomedicine", "abstract": "  Large Language Models (LLMs) have swiftly emerged as vital resources for\ndifferent applications in the biomedical and healthcare domains; however, these\nmodels encounter issues such as generating inaccurate information or\nhallucinations. Retrieval-augmented generation provided a solution for these\nmodels to update knowledge and enhance their performance. In contrast to\nprevious retrieval-augmented LMs, which utilize specialized cross-attention\nmechanisms to help LLM encode retrieved text, BiomedRAG adopts a simpler\napproach by directly inputting the retrieved chunk-based documents into the\nLLM. This straightforward design is easily applicable to existing retrieval and\nlanguage models, effectively bypassing noise information in retrieved\ndocuments, particularly in noise-intensive tasks. Moreover, we demonstrate the\npotential for utilizing the LLM to supervise the retrieval model in the\nbiomedical domain, enabling it to retrieve the document that assists the LM in\nimproving its predictions. Our experiments reveal that with the tuned\nscorer,\\textsc{ BiomedRAG} attains superior performance across 5 biomedical NLP\ntasks, encompassing information extraction (triple extraction, relation\nextraction), text classification, link prediction, and question-answering,\nleveraging over 9 datasets. For instance, in the triple extraction task,\n\\textsc{BiomedRAG} outperforms other triple extraction systems with micro-F1\nscores of 81.42 and 88.83 on GIT and ChemProt corpora, respectively.\n", "date": "2024-05-01", "categories": []}
{"id": "2405.01942", "title": "CRCL at SemEval-2024 Task 2: Simple prompt optimizations", "abstract": "  We present a baseline for the SemEval 2024 task 2 challenge, whose objective\nis to ascertain the inference relationship between pairs of clinical trial\nreport sections and statements. We apply prompt optimization techniques with\nLLM Instruct models provided as a Language Model-as-a-Service (LMaaS). We\nobserved, in line with recent findings, that synthetic CoT prompts\nsignificantly enhance manually crafted ones.\n", "date": "2024-05-03", "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": "2404.15549", "title": "PRISM: Patient Records Interpretation for Semantic Clinical Trial\n  Matching using Large Language Models", "abstract": "  Clinical trial matching is the task of identifying trials for which patients\nmay be potentially eligible. Typically, this task is labor-intensive and\nrequires detailed verification of patient electronic health records (EHRs)\nagainst the stringent inclusion and exclusion criteria of clinical trials. This\nprocess is manual, time-intensive, and challenging to scale up, resulting in\nmany patients missing out on potential therapeutic options. Recent advancements\nin Large Language Models (LLMs) have made automating patient-trial matching\npossible, as shown in multiple concurrent research studies. However, the\ncurrent approaches are confined to constrained, often synthetic datasets that\ndo not adequately mirror the complexities encountered in real-world medical\ndata. In this study, we present the first, end-to-end large-scale empirical\nevaluation of clinical trial matching using real-world EHRs. Our study\nshowcases the capability of LLMs to accurately match patients with appropriate\nclinical trials. We perform experiments with proprietary LLMs, including GPT-4\nand GPT-3.5, as well as our custom fine-tuned model called OncoLLM and show\nthat OncoLLM, despite its significantly smaller size, not only outperforms\nGPT-3.5 but also matches the performance of qualified medical doctors. All\nexperiments were carried out on real-world EHRs that include clinical notes and\navailable clinical trials from a single cancer center in the United States.\n", "date": "2024-04-23", "categories": []}
{"id": "2404.17835", "title": "VANER: Leveraging Large Language Model for Versatile and Adaptive\n  Biomedical Named Entity Recognition", "abstract": "  Prevalent solution for BioNER involves using representation learning\ntechniques coupled with sequence labeling. However, such methods are inherently\ntask-specific, demonstrate poor generalizability, and often require dedicated\nmodel for each dataset. To leverage the versatile capabilities of recently\nremarkable large language models (LLMs), several endeavors have explored\ngenerative approaches to entity extraction. Yet, these approaches often fall\nshort of the effectiveness of previouly sequence labeling approaches. In this\npaper, we utilize the open-sourced LLM LLaMA2 as the backbone model, and design\nspecific instructions to distinguish between different types of entities and\ndatasets. By combining the LLM's understanding of instructions with sequence\nlabeling techniques, we use mix of datasets to train a model capable of\nextracting various types of entities. Given that the backbone LLMs lacks\nspecialized medical knowledge, we also integrate external entity knowledge\nbases and employ instruction tuning to compel the model to densely recognize\ncarefully curated entities. Our model VANER, trained with a small partition of\nparameters, significantly outperforms previous LLMs-based models and, for the\nfirst time, as a model based on LLM, surpasses the majority of conventional\nstate-of-the-art BioNER systems, achieving the highest F1 scores across three\ndatasets.\n", "date": "2024-04-27", "categories": []}
{"id": "2404.17999", "title": "MediFact at MEDIQA-CORR 2024: Why AI Needs a Human Touch", "abstract": "  Accurate representation of medical information is crucial for patient safety,\nyet artificial intelligence (AI) systems, such as Large Language Models (LLMs),\nencounter challenges in error-free clinical text interpretation. This paper\npresents a novel approach submitted to the MEDIQA-CORR 2024 shared task (Ben\nAbacha et al., 2024a), focusing on the automatic correction of single-word\nerrors in clinical notes. Unlike LLMs that rely on extensive generic data, our\nmethod emphasizes extracting contextually relevant information from available\nclinical text data. Leveraging an ensemble of extractive and abstractive\nquestion-answering approaches, we construct a supervised learning framework\nwith domain-specific feature engineering. Our methodology incorporates domain\nexpertise to enhance error correction accuracy. By integrating domain expertise\nand prioritizing meaningful information extraction, our approach underscores\nthe significance of a human-centric strategy in adapting AI for healthcare.\n", "date": "2024-04-27", "categories": []}
{"id": "2404.17183", "title": "Prevalent Frequency of Emotional and Physical Symptoms in Social Anxiety\n  using Zero Shot Classification: An Observational Study", "abstract": "  Social anxiety represents a prevalent challenge in modern society, affecting\nindividuals across personal and professional spheres. Left unaddressed, this\ncondition can yield substantial negative consequences, impacting social\ninteractions and performance. Further understanding its diverse physical and\nemotional symptoms becomes pivotal for comprehensive diagnosis and tailored\ntherapeutic interventions. This study analyze prevalence and frequency of\nsocial anxiety symptoms taken from Mayo Clinic, exploring diverse human\nexperiences from utilizing a large Reddit dataset dedicated to this issue.\nLeveraging these platforms, the research aims to extract insights and examine a\nspectrum of physical and emotional symptoms linked to social anxiety disorder.\nUpholding ethical considerations, the study maintains strict user anonymity\nwithin the dataset. By employing a novel approach, the research utilizes\nBART-based multi-label zero-shot classification to identify and measure symptom\nprevalence and significance in the form of probability score for each symptom\nunder consideration. Results uncover distinctive patterns: \"Trembling\" emerges\nas a prevalent physical symptom, while emotional symptoms like \"Fear of being\njudged negatively\" exhibit high frequencies. These findings offer insights into\nthe multifaceted nature of social anxiety, aiding clinical practices and\ninterventions tailored to its diverse expressions.\n", "date": "2024-04-26", "categories": []}
{"id": "2311.09736", "title": "CARE: Extracting Experimental Findings From Clinical Literature", "abstract": "  Extracting fine-grained experimental findings from literature can provide\ndramatic utility for scientific applications. Prior work has developed\nannotation schemas and datasets for limited aspects of this problem, failing to\ncapture the real-world complexity and nuance required. Focusing on biomedicine,\nthis work presents CARE -- a new IE dataset for the task of extracting clinical\nfindings. We develop a new annotation schema capturing fine-grained findings as\nn-ary relations between entities and attributes, which unifies phenomena\nchallenging for current IE systems such as discontinuous entity spans, nested\nrelations, variable arity n-ary relations and numeric results in a single\nschema. We collect extensive annotations for 700 abstracts from two sources:\nclinical trials and case reports. We also demonstrate the generalizability of\nour schema to the computer science and materials science domains. We benchmark\nstate-of-the-art IE systems on CARE, showing that even models such as GPT4\nstruggle. We release our resources to advance research on extracting and\naggregating literature findings.\n", "date": "2023-11-16", "categories": []}
{"id": "2404.16294", "title": "LLM-Based Section Identifiers Excel on Open Source but Stumble in Real\n  World Applications", "abstract": "  Electronic health records (EHR) even though a boon for healthcare\npractitioners, are growing convoluted and longer every day. Sifting around\nthese lengthy EHRs is taxing and becomes a cumbersome part of physician-patient\ninteraction. Several approaches have been proposed to help alleviate this\nprevalent issue either via summarization or sectioning, however, only a few\napproaches have truly been helpful in the past. With the rise of automated\nmethods, machine learning (ML) has shown promise in solving the task of\nidentifying relevant sections in EHR. However, most ML methods rely on labeled\ndata which is difficult to get in healthcare. Large language models (LLMs) on\nthe other hand, have performed impressive feats in natural language processing\n(NLP), that too in a zero-shot manner, i.e. without any labeled data. To that\nend, we propose using LLMs to identify relevant section headers. We find that\nGPT-4 can effectively solve the task on both zero and few-shot settings as well\nas segment dramatically better than state-of-the-art methods. Additionally, we\nalso annotate a much harder real world dataset and find that GPT-4 struggles to\nperform well, alluding to further research and harder benchmarks.\n", "date": "2024-04-25", "categories": []}
{"id": "2404.16621", "title": "Hippocrates: An Open-Source Framework for Advancing Large Language\n  Models in Healthcare", "abstract": "  The integration of Large Language Models (LLMs) into healthcare promises to\ntransform medical diagnostics, research, and patient care. Yet, the progression\nof medical LLMs faces obstacles such as complex training requirements, rigorous\nevaluation demands, and the dominance of proprietary models that restrict\nacademic exploration. Transparent, comprehensive access to LLM resources is\nessential for advancing the field, fostering reproducibility, and encouraging\ninnovation in healthcare AI. We present Hippocrates, an open-source LLM\nframework specifically developed for the medical domain. In stark contrast to\nprevious efforts, it offers unrestricted access to its training datasets,\ncodebase, checkpoints, and evaluation protocols. This open approach is designed\nto stimulate collaborative research, allowing the community to build upon,\nrefine, and rigorously evaluate medical LLMs within a transparent ecosystem.\nAlso, we introduce Hippo, a family of 7B models tailored for the medical\ndomain, fine-tuned from Mistral and LLaMA2 through continual pre-training,\ninstruction tuning, and reinforcement learning from human and AI feedback. Our\nmodels outperform existing open medical LLMs models by a large-margin, even\nsurpassing models with 70B parameters. Through Hippocrates, we aspire to unlock\nthe full potential of LLMs not just to advance medical knowledge and patient\ncare but also to democratize the benefits of AI research in healthcare, making\nthem available across the globe.\n", "date": "2024-04-25", "categories": []}
{"id": "2404.15488", "title": "IryoNLP at MEDIQA-CORR 2024: Tackling the Medical Error Detection &\n  Correction Task On the Shoulders of Medical Agents", "abstract": "  In natural language processing applied to the clinical domain, utilizing\nlarge language models has emerged as a promising avenue for error detection and\ncorrection on clinical notes, a knowledge-intensive task for which annotated\ndata is scarce. This paper presents MedReAct'N'MedReFlex, which leverages a\nsuite of four LLM-based medical agents. The MedReAct agent initiates the\nprocess by observing, analyzing, and taking action, generating trajectories to\nguide the search to target a potential error in the clinical notes.\nSubsequently, the MedEval agent employs five evaluators to assess the targeted\nerror and the proposed correction. In cases where MedReAct's actions prove\ninsufficient, the MedReFlex agent intervenes, engaging in reflective analysis\nand proposing alternative strategies. Finally, the MedFinalParser agent formats\nthe final output, preserving the original style while ensuring the integrity of\nthe error correction process. One core component of our method is our RAG\npipeline based on our ClinicalCorp corpora. Among other well-known sources\ncontaining clinical guidelines and information, we preprocess and release the\nopen-source MedWiki dataset for clinical RAG application. Our results\ndemonstrate the central role of our RAG approach with ClinicalCorp leveraged\nthrough the MedReAct'N'MedReFlex framework. It achieved the ninth rank on the\nMEDIQA-CORR 2024 final leaderboard.\n", "date": "2024-04-23", "categories": []}
{"id": "2404.15894", "title": "Assessing The Potential Of Mid-Sized Language Models For Clinical QA", "abstract": "  Large language models, such as GPT-4 and Med-PaLM, have shown impressive\nperformance on clinical tasks; however, they require access to compute, are\nclosed-source, and cannot be deployed on device. Mid-size models such as\nBioGPT-large, BioMedLM, LLaMA 2, and Mistral 7B avoid these drawbacks, but\ntheir capacity for clinical tasks has been understudied. To help assess their\npotential for clinical use and help researchers decide which model they should\nuse, we compare their performance on two clinical question-answering (QA)\ntasks: MedQA and consumer query answering. We find that Mistral 7B is the best\nperforming model, winning on all benchmarks and outperforming models trained\nspecifically for the biomedical domain. While Mistral 7B's MedQA score of 63.0%\napproaches the original Med-PaLM, and it often can produce plausible responses\nto consumer health queries, room for improvement still exists. This study\nprovides the first head-to-head assessment of open source mid-sized models on\nclinical tasks.\n", "date": "2024-04-24", "categories": []}
{"id": "2404.14544", "title": "WangLab at MEDIQA-CORR 2024: Optimized LLM-based Programs for Medical\n  Error Detection and Correction", "abstract": "  Medical errors in clinical text pose significant risks to patient safety. The\nMEDIQA-CORR 2024 shared task focuses on detecting and correcting these errors\nacross three subtasks: identifying the presence of an error, extracting the\nerroneous sentence, and generating a corrected sentence. In this paper, we\npresent our approach that achieved top performance in all three subtasks. For\nthe MS dataset, which contains subtle errors, we developed a retrieval-based\nsystem leveraging external medical question-answering datasets. For the UW\ndataset, reflecting more realistic clinical notes, we created a pipeline of\nmodules to detect, localize, and correct errors. Both approaches utilized the\nDSPy framework for optimizing prompts and few-shot examples in large language\nmodel (LLM) based programs. Our results demonstrate the effectiveness of LLM\nbased programs for medical error correction. However, our approach has\nlimitations in addressing the full diversity of potential errors in medical\ndocumentation. We discuss the implications of our work and highlight future\nresearch directions to advance the robustness and applicability of medical\nerror detection and correction systems.\n", "date": "2024-04-22", "categories": []}
{"id": "2402.01693", "title": "Quality of Answers of Generative Large Language Models vs Peer Patients\n  for Interpreting Lab Test Results for Lay Patients: Evaluation Study", "abstract": "  Lab results are often confusing and hard to understand. Large language models\n(LLMs) such as ChatGPT have opened a promising avenue for patients to get their\nquestions answered. We aim to assess the feasibility of using LLMs to generate\nrelevant, accurate, helpful, and unharmful responses to lab test-related\nquestions asked by patients and to identify potential issues that can be\nmitigated with augmentation approaches. We first collected lab test results\nrelated question and answer data from Yahoo! Answers and selected 53 QA pairs\nfor this study. Using the LangChain framework and ChatGPT web portal, we\ngenerated responses to the 53 questions from four LLMs including GPT-4, Meta\nLLaMA 2, MedAlpaca, and ORCA_mini. We first assessed the similarity of their\nanswers using standard QA similarity-based evaluation metrics including ROUGE,\nBLEU, METEOR, BERTScore. We also utilized an LLM-based evaluator to judge\nwhether a target model has higher quality in terms of relevance, correctness,\nhelpfulness, and safety than the baseline model. Finally, we performed a manual\nevaluation with medical experts for all the responses to seven selected\nquestions on the same four aspects. The results of Win Rate and medical expert\nevaluation both showed that GPT-4's responses achieved better scores than all\nthe other LLM responses and human responses on all four aspects (relevance,\ncorrectness, helpfulness, and safety). However, LLM responses occasionally also\nsuffer from a lack of interpretation in one's medical context, incorrect\nstatements, and lack of references. We find that compared to other three LLMs\nand human answer from the Q&A website, GPT-4's responses are more accurate,\nhelpful, relevant, and safer. However, there are cases which GPT-4 responses\nare inaccurate and not individualized. We identified a number of ways to\nimprove the quality of LLM responses.\n", "date": "2024-01-23", "categories": []}
{"id": "2403.09762", "title": "Emotional Intelligence Through Artificial Intelligence : NLP and Deep\n  Learning in the Analysis of Healthcare Texts", "abstract": "  This manuscript presents a methodical examination of the utilization of\nArtificial Intelligence in the assessment of emotions in texts related to\nhealthcare, with a particular focus on the incorporation of Natural Language\nProcessing and deep learning technologies. We scrutinize numerous research\nstudies that employ AI to augment sentiment analysis, categorize emotions, and\nforecast patient outcomes based on textual information derived from clinical\nnarratives, patient feedback on medications, and online health discussions. The\nreview demonstrates noteworthy progress in the precision of algorithms used for\nsentiment classification, the prognostic capabilities of AI models for\nneurodegenerative diseases, and the creation of AI-powered systems that offer\nsupport in clinical decision-making. Remarkably, the utilization of AI\napplications has exhibited an enhancement in personalized therapy plans by\nintegrating patient sentiment and contributing to the early identification of\nmental health disorders. There persist challenges, which encompass ensuring the\nethical application of AI, safeguarding patient confidentiality, and addressing\npotential biases in algorithmic procedures. Nevertheless, the potential of AI\nto revolutionize healthcare practices is unmistakable, offering a future where\nhealthcare is not only more knowledgeable and efficient but also more\nempathetic and centered around the needs of patients. This investigation\nunderscores the transformative influence of AI on healthcare, delivering a\ncomprehensive comprehension of its role in examining emotional content in\nhealthcare texts and highlighting the trajectory towards a more compassionate\napproach to patient care. The findings advocate for a harmonious synergy\nbetween AI's analytical capabilities and the human aspects of healthcare.\n", "date": "2024-03-14", "categories": []}
{"id": "2404.12050", "title": "emrQA-msquad: A Medical Dataset Structured with the SQuAD V2.0\n  Framework, Enriched with emrQA Medical Information", "abstract": "  Machine Reading Comprehension (MRC) holds a pivotal role in shaping Medical\nQuestion Answering Systems (QAS) and transforming the landscape of accessing\nand applying medical information. However, the inherent challenges in the\nmedical field, such as complex terminology and question ambiguity, necessitate\ninnovative solutions. One key solution involves integrating specialized medical\ndatasets and creating dedicated datasets. This strategic approach enhances the\naccuracy of QAS, contributing to advancements in clinical decision-making and\nmedical research. To address the intricacies of medical terminology, a\nspecialized dataset was integrated, exemplified by a novel Span extraction\ndataset derived from emrQA but restructured into 163,695 questions and 4,136\nmanually obtained answers, this new dataset was called emrQA-msquad dataset.\nAdditionally, for ambiguous questions, a dedicated medical dataset for the Span\nextraction task was introduced, reinforcing the system's robustness. The\nfine-tuning of models such as BERT, RoBERTa, and Tiny RoBERTa for medical\ncontexts significantly improved response accuracy within the F1-score range of\n0.75 to 1.00 from 10.1% to 37.4%, 18.7% to 44.7% and 16.0% to 46.8%,\nrespectively. Finally, emrQA-msquad dataset is publicy available at\nhttps://huggingface.co/datasets/Eladio/emrqa-msquad.\n", "date": "2024-04-18", "categories": []}
{"id": "2404.11449", "title": "AI-Enhanced Cognitive Behavioral Therapy: Deep Learning and Large\n  Language Models for Extracting Cognitive Pathways from Social Media Texts", "abstract": "  Cognitive Behavioral Therapy (CBT) is an effective technique for addressing\nthe irrational thoughts stemming from mental illnesses, but it necessitates\nprecise identification of cognitive pathways to be successfully implemented in\npatient care. In current society, individuals frequently express negative\nemotions on social media on specific topics, often exhibiting cognitive\ndistortions, including suicidal behaviors in extreme cases. Yet, there is a\nnotable absence of methodologies for analyzing cognitive pathways that could\naid psychotherapists in conducting effective interventions online. In this\nstudy, we gathered data from social media and established the task of\nextracting cognitive pathways, annotating the data based on a cognitive\ntheoretical framework. We initially categorized the task of extracting\ncognitive pathways as a hierarchical text classification with four main\ncategories and nineteen subcategories. Following this, we structured a text\nsummarization task to help psychotherapists quickly grasp the essential\ninformation. Our experiments evaluate the performance of deep learning and\nlarge language models (LLMs) on these tasks. The results demonstrate that our\ndeep learning method achieved a micro-F1 score of 62.34% in the hierarchical\ntext classification task. Meanwhile, in the text summarization task, GPT-4\nattained a Rouge-1 score of 54.92 and a Rouge-2 score of 30.86, surpassing the\nexperimental deep learning model's performance. However, it may suffer from an\nissue of hallucination. We have made all models and codes publicly available to\nsupport further research in this field.\n", "date": "2024-04-17", "categories": []}
{"id": "2304.14405", "title": "ViMQ: A Vietnamese Medical Question Dataset for Healthcare Dialogue\n  System Development", "abstract": "  Existing medical text datasets usually take the form of question and answer\npairs that support the task of natural language generation, but lacking the\ncomposite annotations of the medical terms. In this study, we publish a\nVietnamese dataset of medical questions from patients with sentence-level and\nentity-level annotations for the Intent Classification and Named Entity\nRecognition tasks. The tag sets for two tasks are in medical domain and can\nfacilitate the development of task-oriented healthcare chatbots with better\ncomprehension of queries from patients. We train baseline models for the two\ntasks and propose a simple self-supervised training strategy with span-noise\nmodelling that substantially improves the performance. Dataset and code will be\npublished at https://github.com/tadeephuy/ViMQ\n", "date": "2023-04-27", "categories": []}
{"id": "2212.03000", "title": "SODA: A Natural Language Processing Package to Extract Social\n  Determinants of Health for Cancer Studies", "abstract": "  Objective: We aim to develop an open-source natural language processing (NLP)\npackage, SODA (i.e., SOcial DeterminAnts), with pre-trained transformer models\nto extract social determinants of health (SDoH) for cancer patients, examine\nthe generalizability of SODA to a new disease domain (i.e., opioid use), and\nevaluate the extraction rate of SDoH using cancer populations.\n  Methods: We identified SDoH categories and attributes and developed an SDoH\ncorpus using clinical notes from a general cancer cohort. We compared four\ntransformer-based NLP models to extract SDoH, examined the generalizability of\nNLP models to a cohort of patients prescribed with opioids, and explored\ncustomization strategies to improve performance. We applied the best NLP model\nto extract 19 categories of SDoH from the breast (n=7,971), lung (n=11,804),\nand colorectal cancer (n=6,240) cohorts.\n  Results and Conclusion: We developed a corpus of 629 cancer patients notes\nwith annotations of 13,193 SDoH concepts/attributes from 19 categories of SDoH.\nThe Bidirectional Encoder Representations from Transformers (BERT) model\nachieved the best strict/lenient F1 scores of 0.9216 and 0.9441 for SDoH\nconcept extraction, 0.9617 and 0.9626 for linking attributes to SDoH concepts.\nFine-tuning the NLP models using new annotations from opioid use patients\nimproved the strict/lenient F1 scores from 0.8172/0.8502 to 0.8312/0.8679. The\nextraction rates among 19 categories of SDoH varied greatly, where 10 SDoH\ncould be extracted from >70% of cancer patients, but 9 SDoH had a low\nextraction rate (<70% of cancer patients). The SODA package with pre-trained\ntransformer models is publicly available at\nhttps://github.com/uf-hobiinformatics-lab/SDoH_SODA.\n", "date": "2022-12-06", "categories": []}
{"id": "2310.06239", "title": "Model Tuning or Prompt Tuning? A Study of Large Language Models for\n  Clinical Concept and Relation Extraction", "abstract": "  Objective To develop soft prompt-based learning algorithms for large language\nmodels (LLMs), examine the shape of prompts, prompt-tuning using\nfrozen/unfrozen LLMs, transfer learning, and few-shot learning abilities.\nMethods We developed a soft prompt-based LLM model and compared 4 training\nstrategies including (1) fine-tuning without prompts; (2) hard-prompt with\nunfrozen LLMs; (3) soft-prompt with unfrozen LLMs; and (4) soft-prompt with\nfrozen LLMs. We evaluated 7 pretrained LLMs using the 4 training strategies for\nclinical concept and relation extraction on two benchmark datasets. We\nevaluated the transfer learning ability of the prompt-based learning algorithms\nin a cross-institution setting. We also assessed the few-shot learning ability.\nResults and Conclusion When LLMs are unfrozen, GatorTron-3.9B with soft\nprompting achieves the best strict F1-scores of 0.9118 and 0.8604 for concept\nextraction, outperforming the traditional fine-tuning and hard prompt-based\nmodels by 0.6~3.1% and 1.2~2.9%, respectively; GatorTron-345M with soft\nprompting achieves the best F1-scores of 0.8332 and 0.7488 for end-to-end\nrelation extraction, outperforming the other two models by 0.2~2% and\n0.6~11.7%, respectively. When LLMs are frozen, small (i.e., 345 million\nparameters) LLMs have a big gap to be competitive with unfrozen models; scaling\nLLMs up to billions of parameters makes frozen LLMs competitive with unfrozen\nLLMs. For cross-institute evaluation, soft prompting with a frozen\nGatorTron-8.9B model achieved the best performance. This study demonstrates\nthat (1) machines can learn soft prompts better than humans, (2) frozen LLMs\nhave better few-shot learning ability and transfer learning ability to\nfacilitate muti-institution applications, and (3) frozen LLMs require large\nmodels.\n", "date": "2023-10-10", "categories": []}
{"id": "2404.09136", "title": "TLDR at SemEval-2024 Task 2: T5-generated clinical-Language summaries\n  for DeBERTa Report Analysis", "abstract": "  This paper introduces novel methodologies for the Natural Language Inference\nfor Clinical Trials (NLI4CT) task. We present TLDR (T5-generated\nclinical-Language summaries for DeBERTa Report Analysis) which incorporates\nT5-model generated premise summaries for improved entailment and contradiction\nanalysis in clinical NLI tasks. This approach overcomes the challenges posed by\nsmall context windows and lengthy premises, leading to a substantial\nimprovement in Macro F1 scores: a 0.184 increase over truncated premises. Our\ncomprehensive experimental evaluation, including detailed error analysis and\nablations, confirms the superiority of TLDR in achieving consistency and\nfaithfulness in predictions against semantically altered inputs.\n", "date": "2024-04-14", "categories": []}
{"id": "2404.09206", "title": "DKE-Research at SemEval-2024 Task 2: Incorporating Data Augmentation\n  with Generative Models and Biomedical Knowledge to Enhance Inference\n  Robustness", "abstract": "  Safe and reliable natural language inference is critical for extracting\ninsights from clinical trial reports but poses challenges due to biases in\nlarge pre-trained language models. This paper presents a novel data\naugmentation technique to improve model robustness for biomedical natural\nlanguage inference in clinical trials. By generating synthetic examples through\nsemantic perturbations and domain-specific vocabulary replacement and adding a\nnew task for numerical and quantitative reasoning, we introduce greater\ndiversity and reduce shortcut learning. Our approach, combined with multi-task\nlearning and the DeBERTa architecture, achieved significant performance gains\non the NLI4CT 2024 benchmark compared to the original language models. Ablation\nstudies validate the contribution of each augmentation method in improving\nrobustness. Our best-performing model ranked 12th in terms of faithfulness and\n8th in terms of consistency, respectively, out of the 32 participants.\n", "date": "2024-04-14", "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": "2404.07424", "title": "CopilotCAD: Empowering Radiologists with Report Completion Models and\n  Quantitative Evidence from Medical Image Foundation Models", "abstract": "  Computer-aided diagnosis systems hold great promise to aid radiologists and\nclinicians in radiological clinical practice and enhance diagnostic accuracy\nand efficiency. However, the conventional systems primarily focus on delivering\ndiagnostic results through text report generation or medical image\nclassification, positioning them as standalone decision-makers rather than\nhelpers and ignoring radiologists' expertise. This study introduces an\ninnovative paradigm to create an assistive co-pilot system for empowering\nradiologists by leveraging Large Language Models (LLMs) and medical image\nanalysis tools. Specifically, we develop a collaborative framework to integrate\nLLMs and quantitative medical image analysis results generated by foundation\nmodels with radiologists in the loop, achieving efficient and safe generation\nof radiology reports and effective utilization of computational power of AI and\nthe expertise of medical professionals. This approach empowers radiologists to\ngenerate more precise and detailed diagnostic reports, enhancing patient\noutcomes while reducing the burnout of clinicians. Our methodology underscores\nthe potential of AI as a supportive tool in medical diagnostics, promoting a\nharmonious integration of technology and human expertise to advance the field\nof radiology.\n", "date": "2024-04-11", "categories": []}
{"id": "2404.07613", "title": "Medical mT5: An Open-Source Multilingual Text-to-Text LLM for The\n  Medical Domain", "abstract": "  Research on language technology for the development of medical applications\nis currently a hot topic in Natural Language Understanding and Generation.\nThus, a number of large language models (LLMs) have recently been adapted to\nthe medical domain, so that they can be used as a tool for mediating in\nhuman-AI interaction. While these LLMs display competitive performance on\nautomated medical texts benchmarks, they have been pre-trained and evaluated\nwith a focus on a single language (English mostly). This is particularly true\nof text-to-text models, which typically require large amounts of\ndomain-specific pre-training data, often not easily accessible for many\nlanguages. In this paper, we address these shortcomings by compiling, to the\nbest of our knowledge, the largest multilingual corpus for the medical domain\nin four languages, namely English, French, Italian and Spanish. This new corpus\nhas been used to train Medical mT5, the first open-source text-to-text\nmultilingual model for the medical domain. Additionally, we present two new\nevaluation benchmarks for all four languages with the aim of facilitating\nmultilingual research in this domain. A comprehensive evaluation shows that\nMedical mT5 outperforms both encoders and similarly sized text-to-text models\nfor the Spanish, French, and Italian benchmarks, while being competitive with\ncurrent state-of-the-art LLMs in English.\n", "date": "2024-04-11", "categories": []}
{"id": "2312.03736", "title": "De-identification of clinical free text using natural language\n  processing: A systematic review of current approaches", "abstract": "  Background: Electronic health records (EHRs) are a valuable resource for\ndata-driven medical research. However, the presence of protected health\ninformation (PHI) makes EHRs unsuitable to be shared for research purposes.\nDe-identification, i.e. the process of removing PHI is a critical step in\nmaking EHR data accessible. Natural language processing has repeatedly\ndemonstrated its feasibility in automating the de-identification process.\nObjectives: Our study aims to provide systematic evidence on how the\nde-identification of clinical free text has evolved in the last thirteen years,\nand to report on the performances and limitations of the current\nstate-of-the-art systems. In addition, we aim to identify challenges and\npotential research opportunities in this field. Methods: A systematic search in\nPubMed, Web of Science and the DBLP was conducted for studies published between\nJanuary 2010 and February 2023. Titles and abstracts were examined to identify\nthe relevant studies. Selected studies were then analysed in-depth, and\ninformation was collected on de-identification methodologies, data sources, and\nmeasured performance. Results: A total of 2125 publications were identified for\nthe title and abstract screening. 69 studies were found to be relevant. Machine\nlearning (37 studies) and hybrid (26 studies) approaches are predominant, while\nsix studies relied only on rules. Majority of the approaches were trained and\nevaluated on public corpora. The 2014 i2b2/UTHealth corpus is the most\nfrequently used (36 studies), followed by the 2006 i2b2 (18 studies) and 2016\nCEGS N-GRID (10 studies) corpora.\n", "date": "2023-11-28", "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": "2311.17213", "title": "General-Purpose vs. Domain-Adapted Large Language Models for Extraction\n  of Structured Data from Chest Radiology Reports", "abstract": "  Radiologists produce unstructured data that can be valuable for clinical care\nwhen consumed by information systems. However, variability in style limits\nusage. Study compares system using domain-adapted language model (RadLing) and\ngeneral-purpose LLM (GPT-4) in extracting relevant features from chest\nradiology reports and standardizing them to common data elements (CDEs). Three\nradiologists annotated a retrospective dataset of 1399 chest XR reports (900\ntraining, 499 test) and mapped to 44 pre-selected relevant CDEs. GPT-4 system\nwas prompted with report, feature set, value set, and dynamic few-shots to\nextract values and map to CDEs. Output key:value pairs were compared to\nreference standard at both stages and an identical match was considered TP. F1\nscore for extraction was 97% for RadLing-based system and 78% for GPT-4 system.\nF1 score for mapping was 98% for RadLing and 94% for GPT-4; difference was\nstatistically significant (P<.001). RadLing's domain-adapted embeddings were\nbetter in feature extraction and its light-weight mapper had better f1 score in\nCDE assignment. RadLing system also demonstrated higher capabilities in\ndifferentiating between absent (99% vs 64%) and unspecified (99% vs 89%).\nRadLing system's domain-adapted embeddings helped improve performance of GPT-4\nsystem to 92% by giving more relevant few-shot prompts. RadLing system offers\noperational advantages including local deployment and reduced runtime costs.\n", "date": "2023-11-28", "categories": []}
{"id": "2404.06367", "title": "ClinLinker: Medical Entity Linking of Clinical Concept Mentions in\n  Spanish", "abstract": "  Advances in natural language processing techniques, such as named entity\nrecognition and normalization to widely used standardized terminologies like\nUMLS or SNOMED-CT, along with the digitalization of electronic health records,\nhave significantly advanced clinical text analysis. This study presents\nClinLinker, a novel approach employing a two-phase pipeline for medical entity\nlinking that leverages the potential of in-domain adapted language models for\nbiomedical text mining: initial candidate retrieval using a SapBERT-based\nbi-encoder and subsequent re-ranking with a cross-encoder, trained by following\na contrastive-learning strategy to be tailored to medical concepts in Spanish.\nThis methodology, focused initially on content in Spanish, substantially\noutperforming multilingual language models designed for the same purpose. This\nis true even for complex scenarios involving heterogeneous medical\nterminologies and being trained on a subset of the original data. Our results,\nevaluated using top-k accuracy at 25 and other top-k metrics, demonstrate our\napproach's performance on two distinct clinical entity linking Gold Standard\ncorpora, DisTEMIST (diseases) and MedProcNER (clinical procedures),\noutperforming previous benchmarks by 40 points in DisTEMIST and 43 points in\nMedProcNER, both normalized to SNOMED-CT codes. These findings highlight our\napproach's ability to address language-specific nuances and set a new benchmark\nin entity linking, offering a potent tool for enhancing the utility of digital\nmedical records. The resulting system is of practical value, both for large\nscale automatic generation of structured data derived from clinical records, as\nwell as for exhaustive extraction and harmonization of predefined clinical\nvariables of interest.\n", "date": "2024-04-09", "categories": []}
{"id": "2404.06503", "title": "Comparing Two Model Designs for Clinical Note Generation; Is an LLM a\n  Useful Evaluator of Consistency?", "abstract": "  Following an interaction with a patient, physicians are responsible for the\nsubmission of clinical documentation, often organized as a SOAP note. A\nclinical note is not simply a summary of the conversation but requires the use\nof appropriate medical terminology. The relevant information can then be\nextracted and organized according to the structure of the SOAP note. In this\npaper we analyze two different approaches to generate the different sections of\na SOAP note based on the audio recording of the conversation, and specifically\nexamine them in terms of note consistency. The first approach generates the\nsections independently, while the second method generates them all together. In\nthis work we make use of PEGASUS-X Transformer models and observe that both\nmethods lead to similar ROUGE values (less than 1% difference) and have no\ndifference in terms of the Factuality metric. We perform a human evaluation to\nmeasure aspects of consistency and demonstrate that LLMs like Llama2 can be\nused to perform the same tasks with roughly the same agreement as the human\nannotators. Between the Llama2 analysis and the human reviewers we observe a\nCohen Kappa inter-rater reliability of 0.79, 1.00, and 0.32 for consistency of\nage, gender, and body part injury, respectively. With this we demonstrate the\nusefulness of leveraging an LLM to measure quality indicators that can be\nidentified by humans but are not currently captured by automatic metrics. This\nallows scaling evaluation to larger data sets, and we find that clinical note\nconsistency improves by generating each new section conditioned on the output\nof all previously generated sections.\n", "date": "2024-04-09", "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": "2404.04510", "title": "IITK at SemEval-2024 Task 2: Exploring the Capabilities of LLMs for Safe\n  Biomedical Natural Language Inference for Clinical Trials", "abstract": "  Large Language models (LLMs) have demonstrated state-of-the-art performance\nin various natural language processing (NLP) tasks across multiple domains, yet\nthey are prone to shortcut learning and factual inconsistencies. This research\ninvestigates LLMs' robustness, consistency, and faithful reasoning when\nperforming Natural Language Inference (NLI) on breast cancer Clinical Trial\nReports (CTRs) in the context of SemEval 2024 Task 2: Safe Biomedical Natural\nLanguage Inference for Clinical Trials. We examine the reasoning capabilities\nof LLMs and their adeptness at logical problem-solving. A comparative analysis\nis conducted on pre-trained language models (PLMs), GPT-3.5, and Gemini Pro\nunder zero-shot settings using Retrieval-Augmented Generation (RAG) framework,\nintegrating various reasoning chains. The evaluation yields an F1 score of\n0.69, consistency of 0.71, and a faithfulness score of 0.90 on the test\ndataset.\n", "date": "2024-04-06", "categories": []}
{"id": "2404.04963", "title": "SemEval-2024 Task 2: Safe Biomedical Natural Language Inference for\n  Clinical Trials", "abstract": "  Large Language Models (LLMs) are at the forefront of NLP achievements but\nfall short in dealing with shortcut learning, factual inconsistency, and\nvulnerability to adversarial inputs.These shortcomings are especially critical\nin medical contexts, where they can misrepresent actual model capabilities.\nAddressing this, we present SemEval-2024 Task 2: Safe Biomedical Natural\nLanguage Inference for ClinicalTrials. Our contributions include the refined\nNLI4CT-P dataset (i.e., Natural Language Inference for Clinical Trials -\nPerturbed), designed to challenge LLMs with interventional and causal reasoning\ntasks, along with a comprehensive evaluation of methods and results for\nparticipant submissions. A total of 106 participants registered for the task\ncontributing to over 1200 individual submissions and 25 system overview papers.\nThis initiative aims to advance the robustness and applicability of NLI models\nin healthcare, ensuring safer and more dependable AI assistance in clinical\ndecision-making. We anticipate that the dataset, models, and outcomes of this\ntask can support future research in the field of biomedical NLI. The dataset,\ncompetition leaderboard, and website are publicly available.\n", "date": "2024-04-07", "categories": []}
{"id": "2404.03977", "title": "SEME at SemEval-2024 Task 2: Comparing Masked and Generative Language\n  Models on Natural Language Inference for Clinical Trials", "abstract": "  This paper describes our submission to Task 2 of SemEval-2024: Safe\nBiomedical Natural Language Inference for Clinical Trials. The Multi-evidence\nNatural Language Inference for Clinical Trial Data (NLI4CT) consists of a\nTextual Entailment (TE) task focused on the evaluation of the consistency and\nfaithfulness of Natural Language Inference (NLI) models applied to Clinical\nTrial Reports (CTR). We test 2 distinct approaches, one based on finetuning and\nensembling Masked Language Models and the other based on prompting Large\nLanguage Models using templates, in particular, using Chain-Of-Thought and\nContrastive Chain-Of-Thought. Prompting Flan-T5-large in a 2-shot setting leads\nto our best system that achieves 0.57 F1 score, 0.64 Faithfulness, and 0.56\nConsistency.\n", "date": "2024-04-05", "categories": []}
{"id": "2403.09057", "title": "A Continued Pretrained LLM Approach for Automatic Medical Note\n  Generation", "abstract": "  LLMs are revolutionizing NLP tasks. However, the use of the most advanced\nLLMs, such as GPT-4, is often prohibitively expensive for most specialized\nfields. We introduce HEAL, the first continuously trained 13B LLaMA2-based LLM\nthat is purpose-built for medical conversations and measured on automated\nscribing. Our results demonstrate that HEAL outperforms GPT-4 and PMC-LLaMA in\nPubMedQA, with an accuracy of 78.4\\%. It also achieves parity with GPT-4 in\ngenerating medical notes. Remarkably, HEAL surpasses GPT-4 and Med-PaLM 2 in\nidentifying more correct medical concepts and exceeds the performance of human\nscribes and other comparable models in correctness and completeness.\n", "date": "2024-03-14", "categories": []}
{"id": "2404.00826", "title": "Extracting Social Determinants of Health from Pediatric Patient Notes\n  Using Large Language Models: Novel Corpus and Methods", "abstract": "  Social determinants of health (SDoH) play a critical role in shaping health\noutcomes, particularly in pediatric populations where interventions can have\nlong-term implications. SDoH are frequently studied in the Electronic Health\nRecord (EHR), which provides a rich repository for diverse patient data. In\nthis work, we present a novel annotated corpus, the Pediatric Social History\nAnnotation Corpus (PedSHAC), and evaluate the automatic extraction of detailed\nSDoH representations using fine-tuned and in-context learning methods with\nLarge Language Models (LLMs). PedSHAC comprises annotated social history\nsections from 1,260 clinical notes obtained from pediatric patients within the\nUniversity of Washington (UW) hospital system. Employing an event-based\nannotation scheme, PedSHAC captures ten distinct health determinants to\nencompass living and economic stability, prior trauma, education access,\nsubstance use history, and mental health with an overall annotator agreement of\n81.9 F1. Our proposed fine-tuning LLM-based extractors achieve high performance\nat 78.4 F1 for event arguments. In-context learning approaches with GPT-4\ndemonstrate promise for reliable SDoH extraction with limited annotated\nexamples, with extraction performance at 82.3 F1 for event triggers.\n", "date": "2024-03-31", "categories": []}
{"id": "2306.15550", "title": "CamemBERT-bio: Leveraging Continual Pre-training for Cost-Effective\n  Models on French Biomedical Data", "abstract": "  Clinical data in hospitals are increasingly accessible for research through\nclinical data warehouses. However these documents are unstructured and it is\ntherefore necessary to extract information from medical reports to conduct\nclinical studies. Transfer learning with BERT-like models such as CamemBERT has\nallowed major advances for French, especially for named entity recognition.\nHowever, these models are trained for plain language and are less efficient on\nbiomedical data. Addressing this gap, we introduce CamemBERT-bio, a dedicated\nFrench biomedical model derived from a new public French biomedical dataset.\nThrough continual pre-training of the original CamemBERT, CamemBERT-bio\nachieves an improvement of 2.54 points of F1-score on average across various\nbiomedical named entity recognition tasks, reinforcing the potential of\ncontinual pre-training as an equally proficient yet less computationally\nintensive alternative to training from scratch. Additionally, we highlight the\nimportance of using a standard evaluation protocol that provides a clear view\nof the current state-of-the-art for French biomedical models.\n", "date": "2023-06-27", "categories": []}
{"id": "2311.11211", "title": "Leveraging Generative AI for Clinical Evidence Summarization Needs to\n  Ensure Trustworthiness", "abstract": "  Evidence-based medicine promises to improve the quality of healthcare by\nempowering medical decisions and practices with the best available evidence.\nThe rapid growth of medical evidence, which can be obtained from various\nsources, poses a challenge in collecting, appraising, and synthesizing the\nevidential information. Recent advancements in generative AI, exemplified by\nlarge language models, hold promise in facilitating the arduous task. However,\ndeveloping accountable, fair, and inclusive models remains a complicated\nundertaking. In this perspective, we discuss the trustworthiness of generative\nAI in the context of automated summarization of medical evidence.\n", "date": "2023-11-19", "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": "2404.00484", "title": "Edinburgh Clinical NLP at SemEval-2024 Task 2: Fine-tune your model\n  unless you have access to GPT-4", "abstract": "  The NLI4CT task assesses Natural Language Inference systems in predicting\nwhether hypotheses entail or contradict evidence from Clinical Trial Reports.\nIn this study, we evaluate various Large Language Models (LLMs) with multiple\nstrategies, including Chain-of-Thought, In-Context Learning, and\nParameter-Efficient Fine-Tuning (PEFT). We propose a PEFT method to improve the\nconsistency of LLMs by merging adapters that were fine-tuned separately using\ntriplet and language modelling objectives. We found that merging the two PEFT\nadapters improves the F1 score (+0.0346) and consistency (+0.152) of the LLMs.\nHowever, our novel methods did not produce more accurate results than GPT-4 in\nterms of faithfulness and consistency. Averaging the three metrics, GPT-4 ranks\njoint-first in the competition with 0.8328. Finally, our contamination analysis\nwith GPT-4 indicates that there was no test data leakage.\n", "date": "2024-03-30", "categories": []}
{"id": "2404.01189", "title": "Generating Faithful and Complete Hospital-Course Summaries from the\n  Electronic Health Record", "abstract": "  The rapid adoption of Electronic Health Records (EHRs) has been instrumental\nin streamlining administrative tasks, increasing transparency, and enabling\ncontinuity of care across providers. An unintended consequence of the increased\ndocumentation burden, however, has been reduced face-time with patients and,\nconcomitantly, a dramatic rise in clinician burnout. In this thesis, we\npinpoint a particularly time-intensive, yet critical, documentation task:\ngenerating a summary of a patient's hospital admissions, and propose and\nevaluate automated solutions. In Chapter 2, we construct a dataset based on\n109,000 hospitalizations (2M source notes) and perform exploratory analyses to\nmotivate future work on modeling and evaluation [NAACL 2021]. In Chapter 3, we\naddress faithfulness from a modeling perspective by revising noisy references\n[EMNLP 2022] and, to reduce the reliance on references, directly calibrating\nmodel outputs to metrics [ACL 2023]. These works relied heavily on automatic\nmetrics as human annotations were limited. To fill this gap, in Chapter 4, we\nconduct a fine-grained expert annotation of system errors in order to\nmeta-evaluate existing metrics and better understand task-specific issues of\ndomain adaptation and source-summary alignments. To learn a metric less\ncorrelated to extractiveness (copy-and-paste), we derive noisy faithfulness\nlabels from an ensemble of existing metrics and train a faithfulness classifier\non these pseudo labels [MLHC 2023]. Finally, in Chapter 5, we demonstrate that\nfine-tuned LLMs (Mistral and Zephyr) are highly prone to entity hallucinations\nand cover fewer salient entities. We improve both coverage and faithfulness by\nperforming sentence-level entity planning based on a set of pre-computed\nsalient entities from the source text, which extends our work on entity-guided\nnews summarization [ACL, 2023], [EMNLP, 2023].\n", "date": "2024-04-01", "categories": []}
{"id": "2404.01218", "title": "Towards System Modelling to Support Diseases Data Extraction from the\n  Electronic Health Records for Physicians Research Activities", "abstract": "  The use of Electronic Health Records (EHRs) has increased dramatically in the\npast 15 years, as, it is considered an important source of managing data od\npatients. The EHRs are primary sources of disease diagnosis and demographic\ndata of patients worldwide. Therefore, the data can be utilized for secondary\ntasks such as research. This paper aims to make such data usable for research\nactivities such as monitoring disease statistics for a specific population. As\na result, the researchers can detect the disease causes for the behavior and\nlifestyle of the target group. One of the limitations of EHRs systems is that\nthe data is not available in the standard format but in various forms.\nTherefore, it is required to first convert the names of the diseases and\ndemographics data into one standardized form to make it usable for research\nactivities. There is a large amount of EHRs available, and solving the\nstandardizing issues requires some optimized techniques. We used a first-hand\nEHR dataset extracted from EHR systems. Our application uploads the dataset\nfrom the EHRs and converts it to the ICD-10 coding system to solve the\nstandardization problem. So, we first apply the steps of pre-processing,\nannotation, and transforming the data to convert it into the standard form. The\ndata pre-processing is applied to normalize demographic formats. In the\nannotation step, a machine learning model is used to recognize the diseases\nfrom the text. Furthermore, the transforming step converts the disease name to\nthe ICD-10 coding format. The model was evaluated manually by comparing its\nperformance in terms of disease recognition with an available dictionary-based\nsystem (MetaMap). The accuracy of the proposed machine learning model is 81%,\nthat outperformed MetaMap accuracy of 67%. This paper contributed to system\nmodelling for EHR data extraction to support research activities.\n", "date": "2024-04-01", "categories": []}
{"id": "2403.19432", "title": "Uncovering Misattributed Suicide Causes through Annotation Inconsistency\n  Detection in Death Investigation Notes", "abstract": "  Data accuracy is essential for scientific research and policy development.\nThe National Violent Death Reporting System (NVDRS) data is widely used for\ndiscovering the patterns and causes of death. Recent studies suggested the\nannotation inconsistencies within the NVDRS and the potential impact on\nerroneous suicide-cause attributions. We present an empirical Natural Language\nProcessing (NLP) approach to detect annotation inconsistencies and adopt a\ncross-validation-like paradigm to identify problematic instances. We analyzed\n267,804 suicide death incidents between 2003 and 2020 from the NVDRS. Our\nresults showed that incorporating the target state's data into training the\nsuicide-crisis classifier brought an increase of 5.4% to the F-1 score on the\ntarget state's test set and a decrease of 1.1% on other states' test set. To\nconclude, we demonstrated the annotation inconsistencies in NVDRS's death\ninvestigation notes, identified problematic instances, evaluated the\neffectiveness of correcting problematic instances, and eventually proposed an\nNLP improvement solution.\n", "date": "2024-03-28", "categories": []}
{"id": "2403.19726", "title": "A Benchmark Evaluation of Clinical Named Entity Recognition in French", "abstract": "  Background: Transformer-based language models have shown strong performance\non many Natural LanguageProcessing (NLP) tasks. Masked Language Models (MLMs)\nattract sustained interest because they can be adaptedto different languages\nand sub-domains through training or fine-tuning on specific corpora while\nremaining lighterthan modern Large Language Models (LLMs). Recently, several\nMLMs have been released for the biomedicaldomain in French, and experiments\nsuggest that they outperform standard French counterparts. However,\nnosystematic evaluation comparing all models on the same corpora is available.\nObjective: This paper presentsan evaluation of masked language models for\nbiomedical French on the task of clinical named entity recognition.Material and\nmethods: We evaluate biomedical models CamemBERT-bio and DrBERT and compare\nthem tostandard French models CamemBERT, FlauBERT and FrALBERT as well as\nmultilingual mBERT using three publicallyavailable corpora for clinical named\nentity recognition in French. The evaluation set-up relies on\ngold-standardcorpora as released by the corpus developers. Results: Results\nsuggest that CamemBERT-bio outperformsDrBERT consistently while FlauBERT offers\ncompetitive performance and FrAlBERT achieves the lowest carbonfootprint.\nConclusion: This is the first benchmark evaluation of biomedical masked\nlanguage models for Frenchclinical entity recognition that compares model\nperformance consistently on nested entity recognition using metricscovering\nperformance and environmental impact.\n", "date": "2024-03-28", "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": "2403.19802", "title": "Developing Healthcare Language Model Embedding Spaces", "abstract": "  Pre-trained Large Language Models (LLMs) often struggle on out-of-domain\ndatasets like healthcare focused text. We explore specialized pre-training to\nadapt smaller LLMs to different healthcare datasets. Three methods are\nassessed: traditional masked language modeling, Deep Contrastive Learning for\nUnsupervised Textual Representations (DeCLUTR), and a novel pre-training\nobjective utilizing metadata categories from the healthcare settings. These\nschemes are evaluated on downstream document classification tasks for each\ndataset, with additional analysis of the resultant embedding spaces.\nContrastively trained models outperform other approaches on the classification\ntasks, delivering strong performance from limited labeled data and with fewer\nmodel parameter updates required. While metadata-based pre-training does not\nfurther improve classifications across the datasets, it yields interesting\nembedding cluster separability. All domain adapted LLMs outperform their\npublicly available general base LLM, validating the importance of\ndomain-specialization. This research illustrates efficient approaches to\ninstill healthcare competency in compact LLMs even under tight computational\nbudgets, an essential capability for responsible and sustainable deployment in\nlocal healthcare settings. We provide pre-training guidelines for specialized\nhealthcare LLMs, motivate continued inquiry into contrastive objectives, and\ndemonstrates adaptation techniques to align small LLMs with privacy-sensitive\nmedical tasks.\n", "date": "2024-03-28", "categories": []}
{"id": "2304.02496", "title": "Evaluation of ChatGPT Family of Models for Biomedical Reasoning and\n  Classification", "abstract": "  Recent advances in large language models (LLMs) have shown impressive ability\nin biomedical question-answering, but have not been adequately investigated for\nmore specific biomedical applications. This study investigates the performance\nof LLMs such as the ChatGPT family of models (GPT-3.5s, GPT-4) in biomedical\ntasks beyond question-answering. Because no patient data can be passed to the\nOpenAI API public interface, we evaluated model performance with over 10000\nsamples as proxies for two fundamental tasks in the clinical domain -\nclassification and reasoning. The first task is classifying whether statements\nof clinical and policy recommendations in scientific literature constitute\nhealth advice. The second task is causal relation detection from the biomedical\nliterature. We compared LLMs with simpler models, such as bag-of-words (BoW)\nwith logistic regression, and fine-tuned BioBERT models. Despite the excitement\naround viral ChatGPT, we found that fine-tuning for two fundamental NLP tasks\nremained the best strategy. The simple BoW model performed on par with the most\ncomplex LLM prompting. Prompt engineering required significant investment.\n", "date": "2023-04-05", "categories": []}
{"id": "2403.18025", "title": "Improving Pre-trained Language Model Sensitivity via Mask Specific\n  losses: A case study on Biomedical NER", "abstract": "  Adapting language models (LMs) to novel domains is often achieved through\nfine-tuning a pre-trained LM (PLM) on domain-specific data. Fine-tuning\nintroduces new knowledge into an LM, enabling it to comprehend and efficiently\nperform a target domain task. Fine-tuning can however be inadvertently\ninsensitive if it ignores the wide array of disparities (e.g in word meaning)\nbetween source and target domains. For instance, words such as chronic and\npressure may be treated lightly in social conversations, however, clinically,\nthese words are usually an expression of concern. To address insensitive\nfine-tuning, we propose Mask Specific Language Modeling (MSLM), an approach\nthat efficiently acquires target domain knowledge by appropriately weighting\nthe importance of domain-specific terms (DS-terms) during fine-tuning. MSLM\njointly masks DS-terms and generic words, then learns mask-specific losses by\nensuring LMs incur larger penalties for inaccurately predicting DS-terms\ncompared to generic words. Results of our analysis show that MSLM improves LMs\nsensitivity and detection of DS-terms. We empirically show that an optimal\nmasking rate not only depends on the LM, but also on the dataset and the length\nof sequences. Our proposed masking strategy outperforms advanced masking\nstrategies such as span- and PMI-based masking.\n", "date": "2024-03-26", "categories": []}
{"id": "2403.18975", "title": "A Novel Corpus of Annotated Medical Imaging Reports and Information\n  Extraction Results Using BERT-based Language Models", "abstract": "  Medical imaging is critical to the diagnosis, surveillance, and treatment of\nmany health conditions, including oncological, neurological, cardiovascular,\nand musculoskeletal disorders, among others. Radiologists interpret these\ncomplex, unstructured images and articulate their assessments through narrative\nreports that remain largely unstructured. This unstructured narrative must be\nconverted into a structured semantic representation to facilitate secondary\napplications such as retrospective analyses or clinical decision support. Here,\nwe introduce the Corpus of Annotated Medical Imaging Reports (CAMIR), which\nincludes 609 annotated radiology reports from three imaging modality types:\nComputed Tomography, Magnetic Resonance Imaging, and Positron Emission\nTomography-Computed Tomography. Reports were annotated using an event-based\nschema that captures clinical indications, lesions, and medical problems. Each\nevent consists of a trigger and multiple arguments, and a majority of the\nargument types, including anatomy, normalize the spans to pre-defined concepts\nto facilitate secondary use. CAMIR uniquely combines a granular event structure\nand concept normalization. To extract CAMIR events, we explored two BERT\n(Bi-directional Encoder Representation from Transformers)-based architectures,\nincluding an existing architecture (mSpERT) that jointly extracts all event\ninformation and a multi-step approach (PL-Marker++) that we augmented for the\nCAMIR schema.\n", "date": "2024-03-27", "categories": []}
{"id": "2403.19511", "title": "Improving Clinical NLP Performance through Language Model-Generated\n  Synthetic Clinical Data", "abstract": "  Generative models have been showing potential for producing data in mass.\nThis study explores the enhancement of clinical natural language processing\nperformance by utilizing synthetic data generated from advanced language\nmodels. Promising results show feasible applications in such a high-stakes\ndomain.\n", "date": "2024-03-28", "categories": []}
{"id": "2403.18336", "title": "A Dataset for Pharmacovigilance in German, French, and Japanese:\n  Annotating Adverse Drug Reactions across Languages", "abstract": "  User-generated data sources have gained significance in uncovering Adverse\nDrug Reactions (ADRs), with an increasing number of discussions occurring in\nthe digital world. However, the existing clinical corpora predominantly revolve\naround scientific articles in English. This work presents a multilingual corpus\nof texts concerning ADRs gathered from diverse sources, including patient fora,\nsocial media, and clinical reports in German, French, and Japanese. Our corpus\ncontains annotations covering 12 entity types, four attribute types, and 13\nrelation types. It contributes to the development of real-world multilingual\nlanguage models for healthcare. We provide statistics to highlight certain\nchallenges associated with the corpus and conduct preliminary experiments\nresulting in strong baselines for extracting entities and relations between\nthese entities, both within and across languages.\n", "date": "2024-03-27", "categories": []}
{"id": "2403.17135", "title": "Exploring the Generalization of Cancer Clinical Trial Eligibility\n  Classifiers Across Diseases", "abstract": "  Clinical trials are pivotal in medical research, and NLP can enhance their\nsuccess, with application in recruitment. This study aims to evaluate the\ngeneralizability of eligibility classification across a broad spectrum of\nclinical trials. Starting with phase 3 cancer trials, annotated with seven\neligibility exclusions, then to determine how well models can generalize to\nnon-cancer and non-phase 3 trials. To assess this, we have compiled eligibility\ncriteria data for five types of trials: (1) additional phase 3 cancer trials,\n(2) phase 1 and 2 cancer trials, (3) heart disease trials, (4) type 2 diabetes\ntrials, and (5) observational trials for any disease, comprising 2,490\nannotated eligibility criteria across seven exclusion types. Our results show\nthat models trained on the extensive cancer dataset can effectively handle\ncriteria commonly found in non-cancer trials, such as autoimmune diseases.\nHowever, they struggle with criteria disproportionately prevalent in cancer\ntrials, like prior malignancy. We also experiment with few-shot learning,\ndemonstrating that a limited number of disease-specific examples can partially\novercome this performance gap. We are releasing this new dataset of annotated\neligibility statements to promote the development of cross-disease\ngeneralization in clinical trial classification.\n", "date": "2024-03-25", "categories": []}
{"id": "2403.17363", "title": "Extracting Biomedical Entities from Noisy Audio Transcripts", "abstract": "  Automatic Speech Recognition (ASR) technology is fundamental in transcribing\nspoken language into text, with considerable applications in the clinical\nrealm, including streamlining medical transcription and integrating with\nElectronic Health Record (EHR) systems. Nevertheless, challenges persist,\nespecially when transcriptions contain noise, leading to significant drops in\nperformance when Natural Language Processing (NLP) models are applied. Named\nEntity Recognition (NER), an essential clinical task, is particularly affected\nby such noise, often termed the ASR-NLP gap. Prior works have primarily studied\nASR's efficiency in clean recordings, leaving a research gap concerning the\nperformance in noisy environments. This paper introduces a novel dataset,\nBioASR-NER, designed to bridge the ASR-NLP gap in the biomedical domain,\nfocusing on extracting adverse drug reactions and mentions of entities from the\nBrief Test of Adult Cognition by Telephone (BTACT) exam. Our dataset offers a\ncomprehensive collection of almost 2,000 clean and noisy recordings. In\naddressing the noise challenge, we present an innovative transcript-cleaning\nmethod using GPT4, investigating both zero-shot and few-shot methodologies. Our\nstudy further delves into an error analysis, shedding light on the types of\nerrors in transcription software, corrections by GPT4, and the challenges GPT4\nfaces. This paper aims to foster improved understanding and potential solutions\nfor the ASR-NLP gap, ultimately supporting enhanced healthcare documentation\npractices.\n", "date": "2024-03-26", "categories": []}
{"id": "2304.14670", "title": "Prompt Engineering for Healthcare: Methodologies and Applications", "abstract": "  Prompt engineering is a critical technique in the field of natural language\nprocessing that involves designing and optimizing the prompts used to input\ninformation into models, aiming to enhance their performance on specific tasks.\nWith the recent advancements in large language models, prompt engineering has\nshown significant superiority across various domains and has become\nincreasingly important in the healthcare domain. However, there is a lack of\ncomprehensive reviews specifically focusing on prompt engineering in the\nmedical field. This review will introduce the latest advances in prompt\nengineering in the field of natural language processing for the medical field.\nFirst, we will provide the development of prompt engineering and emphasize its\nsignificant contributions to healthcare natural language processing\napplications such as question-answering systems, text summarization, and\nmachine translation. With the continuous improvement of general large language\nmodels, the importance of prompt engineering in the healthcare domain is\nbecoming increasingly prominent. The aim of this article is to provide useful\nresources and bridges for healthcare natural language processing researchers to\nbetter explore the application of prompt engineering in this field. We hope\nthat this review can provide new ideas and inspire for research and application\nin medical natural language processing.\n", "date": "2023-04-28", "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": "2403.15464", "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": "2403.15478", "title": "Integrating Supervised Extractive and Generative Language Models for\n  Suicide Risk Evidence Summarization", "abstract": "  We propose a method that integrates supervised extractive and generative\nlanguage models for providing supporting evidence of suicide risk in the\nCLPsych 2024 shared task. Our approach comprises three steps. Initially, we\nconstruct a BERT-based model for estimating sentence-level suicide risk and\nnegative sentiment. Next, we precisely identify high suicide risk sentences by\nemphasizing elevated probabilities of both suicide risk and negative sentiment.\nFinally, we integrate generative summaries using the MentaLLaMa framework and\nextractive summaries from identified high suicide risk sentences and a\nspecialized dictionary of suicidal risk words. SophiaADS, our team, achieved\n1st place for highlight extraction and ranked 10th for summary generation, both\nbased on recall and consistency metrics, respectively.\n", "date": "2024-03-20", "categories": []}
{"id": "2403.16158", "title": "Korean Bio-Medical Corpus (KBMC) for Medical Named Entity Recognition", "abstract": "  Named Entity Recognition (NER) plays a pivotal role in medical Natural\nLanguage Processing (NLP). Yet, there has not been an open-source medical NER\ndataset specifically for the Korean language. To address this, we utilized\nChatGPT to assist in constructing the KBMC (Korean Bio-Medical Corpus), which\nwe are now presenting to the public. With the KBMC dataset, we noticed an\nimpressive 20% increase in medical NER performance compared to models trained\non general Korean NER datasets. This research underscores the significant\nbenefits and importance of using specialized tools and datasets, like ChatGPT,\nto enhance language processing in specialized fields such as healthcare.\n", "date": "2024-03-24", "categories": []}
{"id": "2403.14255", "title": "ERD: A Framework for Improving LLM Reasoning for Cognitive Distortion\n  Classification", "abstract": "  Improving the accessibility of psychotherapy with the aid of Large Language\nModels (LLMs) is garnering a significant attention in recent years. Recognizing\ncognitive distortions from the interviewee's utterances can be an essential\npart of psychotherapy, especially for cognitive behavioral therapy. In this\npaper, we propose ERD, which improves LLM-based cognitive distortion\nclassification performance with the aid of additional modules of (1) extracting\nthe parts related to cognitive distortion, and (2) debating the reasoning steps\nby multiple agents. Our experimental results on a public dataset show that ERD\nimproves the multi-class F1 score as well as binary specificity score.\nRegarding the latter score, it turns out that our method is effective in\ndebiasing the baseline method which has high false positive rate, especially\nwhen the summary of multi-agent debate is provided to LLMs.\n", "date": "2024-03-21", "categories": []}
{"id": "2403.12088", "title": "TMU at TREC Clinical Trials Track 2023", "abstract": "  This paper describes Toronto Metropolitan University's participation in the\nTREC Clinical Trials Track for 2023. As part of the tasks, we utilize advanced\nnatural language processing techniques and neural language models in our\nexperiments to retrieve the most relevant clinical trials. We illustrate the\noverall methodology, experimental settings, and results of our implementation\nfor the run submission as part of Team - V-TorontoMU.\n", "date": "2024-03-12", "categories": []}
{"id": "2306.08666", "title": "Radiology-GPT: A Large Language Model for Radiology", "abstract": "  We introduce Radiology-GPT, a large language model for radiology. Using an\ninstruction tuning approach on an extensive dataset of radiology domain\nknowledge, Radiology-GPT demonstrates superior performance compared to general\nlanguage models such as StableLM, Dolly and LLaMA. It exhibits significant\nversatility in radiological diagnosis, research, and communication. This work\nserves as a catalyst for future developments in clinical NLP. The successful\nimplementation of Radiology-GPT is indicative of the potential of localizing\ngenerative large language models, specifically tailored for distinctive medical\nspecialties, while ensuring adherence to privacy standards such as HIPAA. The\nprospect of developing individualized, large-scale language models that cater\nto specific needs of various hospitals presents a promising direction. The\nfusion of conversational competence and domain-specific knowledge in these\nmodels is set to foster future development in healthcare AI. A demo of\nRadiology-GPT is available at\nhttps://huggingface.co/spaces/allen-eric/radiology-gpt.\n", "date": "2023-06-14", "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": "2403.12374", "title": "Improving Generalizability of Extracting Social Determinants of Health\n  Using Large Language Models through Prompt-tuning", "abstract": "  The progress in natural language processing (NLP) using large language models\n(LLMs) has greatly improved patient information extraction from clinical\nnarratives. However, most methods based on the fine-tuning strategy have\nlimited transfer learning ability for cross-domain applications. This study\nproposed a novel approach that employs a soft prompt-based learning\narchitecture, which introduces trainable prompts to guide LLMs toward desired\noutputs. We examined two types of LLM architectures, including encoder-only\nGatorTron and decoder-only GatorTronGPT, and evaluated their performance for\nthe extraction of social determinants of health (SDoH) using a\ncross-institution dataset from the 2022 n2c2 challenge and a cross-disease\ndataset from the University of Florida (UF) Health. The results show that\ndecoder-only LLMs with prompt tuning achieved better performance in\ncross-domain applications. GatorTronGPT achieved the best F1 scores for both\ndatasets, outperforming traditional fine-tuned GatorTron by 8.9% and 21.8% in a\ncross-institution setting, and 5.5% and 14.5% in a cross-disease setting.\n", "date": "2024-03-19", "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": "2403.11227", "title": "Cheap Ways of Extracting Clinical Markers from Texts", "abstract": "  This paper describes the work of the UniBuc Archaeology team for CLPsych's\n2024 Shared Task, which involved finding evidence within the text supporting\nthe assigned suicide risk level. Two types of evidence were required:\nhighlights (extracting relevant spans within the text) and summaries\n(aggregating evidence into a synthesis). Our work focuses on evaluating Large\nLanguage Models (LLM) as opposed to an alternative method that is much more\nmemory and resource efficient. The first approach employs a good old-fashioned\nmachine learning (GOML) pipeline consisting of a tf-idf vectorizer with a\nlogistic regression classifier, whose representative features are used to\nextract relevant highlights. The second, more resource intensive, uses an LLM\nfor generating the summaries and is guided by chain-of-thought to provide\nsequences of text indicating clinical markers.\n", "date": "2024-03-17", "categories": []}
{"id": "2303.13466", "title": "Mining Clinical Notes for Physical Rehabilitation Exercise Information:\n  Natural Language Processing Algorithm Development and Validation Study", "abstract": "  Post-stroke patient rehabilitation requires precise, personalized treatment\nplans. Natural Language Processing (NLP) offers potential to extract valuable\nexercise information from clinical notes, aiding in the development of more\neffective rehabilitation strategies. Objective: This study aims to develop and\nevaluate a variety of NLP algorithms to extract and categorize physical\nrehabilitation exercise information from the clinical notes of post-stroke\npatients treated at the University of Pittsburgh Medical Center. A cohort of\n13,605 patients diagnosed with stroke was identified, and their clinical notes\ncontaining rehabilitation therapy notes were retrieved. A comprehensive\nclinical ontology was created to represent various aspects of physical\nrehabilitation exercises. State-of-the-art NLP algorithms were then developed\nand compared, including rule-based, machine learning-based algorithms, and\nlarge language model (LLM)-based algorithms (ChatGPT). Analysis was conducted\non a dataset comprising 23,724 notes with detailed demographic and clinical\ncharacteristics. The rule-based NLP algorithm demonstrated superior performance\nin most areas, particularly in detecting the 'Right Side' location with an F1\nscore of 0.975, outperforming Gradient Boosting by 0.063. Gradient Boosting\nexcelled in 'Lower Extremity' location detection (F1 score: 0.978), surpassing\nrule-based NLP by 0.023. It also showed notable performance in 'Passive Range\nof Motion' with an F1 score of 0.970, a 0.032 improvement over rule-based NLP.\nThe rule-based algorithm efficiently handled 'Duration', 'Sets', and 'Reps'\nwith F1 scores up to 0.65. LLM-based NLP, particularly ChatGPT with few-shot\nprompts, achieved high recall but generally lower precision and F1 scores.\nHowever, it notably excelled in 'Backward Plane' motion detection, achieving an\nF1 score of 0.846, surpassing the rule-based algorithm's 0.720.\n", "date": "2023-03-22", "categories": []}
{"id": "2308.03853", "title": "CORAL: Expert-Curated medical Oncology Reports to Advance Language Model\n  Inference", "abstract": "  Both medical care and observational studies in oncology require a thorough\nunderstanding of a patient's disease progression and treatment history, often\nelaborately documented in clinical notes. Despite their vital role, no current\noncology information representation and annotation schema fully encapsulates\nthe diversity of information recorded within these notes. Although large\nlanguage models (LLMs) have recently exhibited impressive performance on\nvarious medical natural language processing tasks, due to the current lack of\ncomprehensively annotated oncology datasets, an extensive evaluation of LLMs in\nextracting and reasoning with the complex rhetoric in oncology notes remains\nunderstudied. We developed a detailed schema for annotating textual oncology\ninformation, encompassing patient characteristics, tumor characteristics,\ntests, treatments, and temporality. Using a corpus of 40 de-identified breast\nand pancreatic cancer progress notes at University of California, San\nFrancisco, we applied this schema to assess the zero-shot abilities of three\nrecent LLMs (GPT-4, GPT-3.5-turbo, and FLAN-UL2) to extract detailed\noncological history from two narrative sections of clinical progress notes. Our\nteam annotated 9028 entities, 9986 modifiers, and 5312 relationships. The GPT-4\nmodel exhibited overall best performance, with an average BLEU score of 0.73,\nan average ROUGE score of 0.72, an exact-match F1-score of 0.51, and an average\naccuracy of 68% on complex tasks (expert manual evaluation on subset). Notably,\nit was proficient in tumor characteristic and medication extraction, and\ndemonstrated superior performance in relational inference like adverse event\ndetection. However, further improvements are needed before using it to reliably\nextract important facts from cancer progress notes needed for clinical\nresearch, complex population management, and documenting quality patient care.\n", "date": "2023-08-07", "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": "2403.09725", "title": "RAD-PHI2: Instruction Tuning PHI-2 for Radiology", "abstract": "  Small Language Models (SLMs) have shown remarkable performance in general\ndomain language understanding, reasoning and coding tasks, but their\ncapabilities in the medical domain, particularly concerning radiology text, is\nless explored. In this study, we investigate the application of SLMs for\ngeneral radiology knowledge specifically question answering related to\nunderstanding of symptoms, radiological appearances of findings, differential\ndiagnosis, assessing prognosis, and suggesting treatments w.r.t diseases\npertaining to different organ systems. Additionally, we explore the utility of\nSLMs in handling text-related tasks with respect to radiology reports within\nAI-driven radiology workflows. We fine-tune Phi-2, a SLM with 2.7 billion\nparameters using high-quality educational content from Radiopaedia, a\ncollaborative online radiology resource. The resulting language model,\nRadPhi-2-Base, exhibits the ability to address general radiology queries across\nvarious systems (e.g., chest, cardiac). Furthermore, we investigate Phi-2 for\ninstruction tuning, enabling it to perform specific tasks. By fine-tuning Phi-2\non both general domain tasks and radiology-specific tasks related to chest\nX-ray reports, we create Rad-Phi2. Our empirical results reveal that Rad-Phi2\nBase and Rad-Phi2 perform comparably or even outperform larger models such as\nMistral-7B-Instruct-v0.2 and GPT-4 providing concise and precise answers. In\nsummary, our work demonstrates the feasibility and effectiveness of utilizing\nSLMs in radiology workflows both for knowledge related queries as well as for\nperforming specific tasks related to radiology reports thereby opening up new\navenues for enhancing the quality and efficiency of radiology practice.\n", "date": "2024-03-12", "categories": []}
{"id": "2403.09997", "title": "Identifying Health Risks from Family History: A Survey of Natural\n  Language Processing Techniques", "abstract": "  Electronic health records include information on patients' status and medical\nhistory, which could cover the history of diseases and disorders that could be\nhereditary. One important use of family history information is in precision\nhealth, where the goal is to keep the population healthy with preventative\nmeasures. Natural Language Processing (NLP) and machine learning techniques can\nassist with identifying information that could assist health professionals in\nidentifying health risks before a condition is developed in their later years,\nsaving lives and reducing healthcare costs.\n  We survey the literature on the techniques from the NLP field that have been\ndeveloped to utilise digital health records to identify risks of familial\ndiseases. We highlight that rule-based methods are heavily investigated and are\nstill actively used for family history extraction. Still, more recent efforts\nhave been put into building neural models based on large-scale pre-trained\nlanguage models. In addition to the areas where NLP has successfully been\nutilised, we also identify the areas where more research is needed to unlock\nthe value of patients' records regarding data collection, task formulation and\ndownstream applications.\n", "date": "2024-03-15", "categories": []}
{"id": "2403.08664", "title": "Zero-shot and Few-shot Generation Strategies for Artificial Clinical\n  Records", "abstract": "  The challenge of accessing historical patient data for clinical research,\nwhile adhering to privacy regulations, is a significant obstacle in medical\nscience. An innovative approach to circumvent this issue involves utilising\nsynthetic medical records that mirror real patient data without compromising\nindividual privacy. The creation of these synthetic datasets, particularly\nwithout using actual patient data to train Large Language Models (LLMs),\npresents a novel solution as gaining access to sensitive patient information to\ntrain models is also a challenge. This study assesses the capability of the\nLlama 2 LLM to create synthetic medical records that accurately reflect real\npatient information, employing zero-shot and few-shot prompting strategies for\ncomparison against fine-tuned methodologies that do require sensitive patient\ndata during training. We focus on generating synthetic narratives for the\nHistory of Present Illness section, utilising data from the MIMIC-IV dataset\nfor comparison. In this work introduce a novel prompting technique that\nleverages a chain-of-thought approach, enhancing the model's ability to\ngenerate more accurate and contextually relevant medical narratives without\nprior fine-tuning. Our findings suggest that this chain-of-thought prompted\napproach allows the zero-shot model to achieve results on par with those of\nfine-tuned models, based on Rouge metrics evaluation.\n", "date": "2024-03-13", "categories": []}
{"id": "2307.00920", "title": "Node-weighted Graph Convolutional Network for Depression Detection in\n  Transcribed Clinical Interviews", "abstract": "  We propose a simple approach for weighting self-connecting edges in a Graph\nConvolutional Network (GCN) and show its impact on depression detection from\ntranscribed clinical interviews. To this end, we use a GCN for modeling\nnon-consecutive and long-distance semantics to classify the transcriptions into\ndepressed or control subjects. The proposed method aims to mitigate the\nlimiting assumptions of locality and the equal importance of self-connections\nvs. edges to neighboring nodes in GCNs, while preserving attractive features\nsuch as low computational cost, data agnostic, and interpretability\ncapabilities. We perform an exhaustive evaluation in two benchmark datasets.\nResults show that our approach consistently outperforms the vanilla GCN model\nas well as previously reported results, achieving an F1=0.84 on both datasets.\nFinally, a qualitative analysis illustrates the interpretability capabilities\nof the proposed approach and its alignment with previous findings in\npsychology.\n", "date": "2023-07-03", "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": "2403.05920", "title": "High Throughput Phenotyping of Physician Notes with Large Language and\n  Hybrid NLP Models", "abstract": "  Deep phenotyping is the detailed description of patient signs and symptoms\nusing concepts from an ontology. The deep phenotyping of the numerous physician\nnotes in electronic health records requires high throughput methods. Over the\npast thirty years, progress toward making high throughput phenotyping feasible.\nIn this study, we demonstrate that a large language model and a hybrid NLP\nmodel (combining word vectors with a machine learning classifier) can perform\nhigh throughput phenotyping on physician notes with high accuracy. Large\nlanguage models will likely emerge as the preferred method for high throughput\ndeep phenotyping of physician notes.\n", "date": "2024-03-09", "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": "2311.11608", "title": "Taiyi: A Bilingual Fine-Tuned Large Language Model for Diverse\n  Biomedical Tasks", "abstract": "  Objective: Most existing fine-tuned biomedical large language models (LLMs)\nfocus on enhancing performance in monolingual biomedical question answering and\nconversation tasks. To investigate the effectiveness of the fine-tuned LLMs on\ndiverse biomedical NLP tasks in different languages, We present Taiyi, a\nbilingual fine-tuned LLM for diverse biomedical tasks. Materials and Methods:\nWe first curated a comprehensive collection of 140 existing biomedical text\nmining datasets (102 English and 38 Chinese datasets) across over 10 task\ntypes. Subsequently, a two-stage strategy is proposed for supervised\nfine-tuning to optimize the model performance across varied tasks. Results:\nExperimental results on 13 test sets covering named entity recognition,\nrelation extraction, text classification, question answering tasks demonstrate\nthat Taiyi achieves superior performance compared to general LLMs. The case\nstudy involving additional biomedical NLP tasks further shows Taiyi's\nconsiderable potential for bilingual biomedical multi-tasking. Conclusion:\nLeveraging rich high-quality biomedical corpora and developing effective\nfine-tuning strategies can significantly improve the performance of LLMs within\nthe biomedical domain. Taiyi shows the bilingual multi-tasking capability\nthrough supervised fine-tuning. However, those tasks such as information\nextraction that are not generation tasks in nature remain challenging for\nLLM-based generative approaches, and they still underperform the conventional\ndiscriminative approaches of smaller language models.\n", "date": "2023-11-20", "categories": []}
{"id": "2403.00788", "title": "PRECISE Framework: GPT-based Text For Improved Readability, Reliability,\n  and Understandability of Radiology Reports For Patient-Centered Care", "abstract": "  This study introduces and evaluates the PRECISE framework, utilizing OpenAI's\nGPT-4 to enhance patient engagement by providing clearer and more accessible\nchest X-ray reports at a sixth-grade reading level. The framework was tested on\n500 reports, demonstrating significant improvements in readability,\nreliability, and understandability. Statistical analyses confirmed the\neffectiveness of the PRECISE approach, highlighting its potential to foster\npatient-centric care delivery in healthcare decision-making.\n", "date": "2024-02-20", "categories": []}
{"id": "2402.19052", "title": "Exploring the Efficacy of Large Language Models in Summarizing Mental\n  Health Counseling Sessions: A Benchmark Study", "abstract": "  Comprehensive summaries of sessions enable an effective continuity in mental\nhealth counseling, facilitating informed therapy planning. Yet, manual\nsummarization presents a significant challenge, diverting experts' attention\nfrom the core counseling process. This study evaluates the effectiveness of\nstate-of-the-art Large Language Models (LLMs) in selectively summarizing\nvarious components of therapy sessions through aspect-based summarization,\naiming to benchmark their performance. We introduce MentalCLOUDS, a\ncounseling-component guided summarization dataset consisting of 191 counseling\nsessions with summaries focused on three distinct counseling components (aka\ncounseling aspects). Additionally, we assess the capabilities of 11\nstate-of-the-art LLMs in addressing the task of component-guided summarization\nin counseling. The generated summaries are evaluated quantitatively using\nstandard summarization metrics and verified qualitatively by mental health\nprofessionals. Our findings demonstrate the superior performance of\ntask-specific LLMs such as MentalLlama, Mistral, and MentalBART in terms of\nstandard quantitative metrics such as Rouge-1, Rouge-2, Rouge-L, and BERTScore\nacross all aspects of counseling components. Further, expert evaluation reveals\nthat Mistral supersedes both MentalLlama and MentalBART based on six parameters\n-- affective attitude, burden, ethicality, coherence, opportunity costs, and\nperceived effectiveness. However, these models share the same weakness by\ndemonstrating a potential for improvement in the opportunity costs and\nperceived effectiveness metrics.\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": "2305.17588", "title": "Diagnosing Transformers: Illuminating Feature Spaces for Clinical\n  Decision-Making", "abstract": "  Pre-trained transformers are often fine-tuned to aid clinical decision-making\nusing limited clinical notes. Model interpretability is crucial, especially in\nhigh-stakes domains like medicine, to establish trust and ensure safety, which\nrequires human engagement. We introduce SUFO, a systematic framework that\nenhances interpretability of fine-tuned transformer feature spaces. SUFO\nutilizes a range of analytic and visualization techniques, including Supervised\nprobing, Unsupervised similarity analysis, Feature dynamics, and Outlier\nanalysis to address key questions about model trust and interpretability. We\nconduct a case study investigating the impact of pre-training data where we\nfocus on real-world pathology classification tasks, and validate our findings\non MedNLI. We evaluate five 110M-sized pre-trained transformer models,\ncategorized into general-domain (BERT, TNLR), mixed-domain (BioBERT, Clinical\nBioBERT), and domain-specific (PubMedBERT) groups. Our SUFO analyses reveal\nthat: (1) while PubMedBERT, the domain-specific model, contains valuable\ninformation for fine-tuning, it can overfit to minority classes when class\nimbalances exist. In contrast, mixed-domain models exhibit greater resistance\nto overfitting, suggesting potential improvements in domain-specific model\nrobustness; (2) in-domain pre-training accelerates feature disambiguation\nduring fine-tuning; and (3) feature spaces undergo significant sparsification\nduring this process, enabling clinicians to identify common outlier modes among\nfine-tuned models as demonstrated in this paper. These findings showcase the\nutility of SUFO in enhancing trust and safety when using transformers in\nmedicine, and we believe SUFO can aid practitioners in evaluating fine-tuned\nlanguage models for other applications in medicine and in more critical\ndomains.\n", "date": "2023-05-27", "categories": []}
{"id": "2402.10965", "title": "Generalization in Healthcare AI: Evaluation of a Clinical Large Language\n  Model", "abstract": "  Advances in large language models (LLMs) provide new opportunities in\nhealthcare for improved patient care, clinical decision-making, and enhancement\nof physician and administrator workflows. However, the potential of these\nmodels importantly depends on their ability to generalize effectively across\nclinical environments and populations, a challenge often underestimated in\nearly development. To better understand reasons for these challenges and inform\nmitigation approaches, we evaluated ClinicLLM, an LLM trained on [HOSPITAL]'s\nclinical notes, analyzing its performance on 30-day all-cause readmission\nprediction focusing on variability across hospitals and patient\ncharacteristics. We found poorer generalization particularly in hospitals with\nfewer samples, among patients with government and unspecified insurance, the\nelderly, and those with high comorbidities. To understand reasons for lack of\ngeneralization, we investigated sample sizes for fine-tuning, note content\n(number of words per note), patient characteristics (comorbidity level, age,\ninsurance type, borough), and health system aspects (hospital, all-cause 30-day\nreadmission, and mortality rates). We used descriptive statistics and\nsupervised classification to identify features. We found that, along with\nsample size, patient age, number of comorbidities, and the number of words in\nnotes are all important factors related to generalization. Finally, we compared\nlocal fine-tuning (hospital specific), instance-based augmented fine-tuning and\ncluster-based fine-tuning for improving generalization. Among these, local\nfine-tuning proved most effective, increasing AUC by 0.25% to 11.74% (most\nhelpful in settings with limited data). Overall, this study provides new\ninsights for enhancing the deployment of large language models in the\nsocietally important domain of healthcare, and improving their performance for\nbroader populations.\n", "date": "2024-02-14", "categories": []}
{"id": "2402.15515", "title": "Feasibility of Identifying Factors Related to Alzheimer's Disease and\n  Related Dementia in Real-World Data", "abstract": "  A comprehensive view of factors associated with AD/ADRD will significantly\naid in studies to develop new treatments for AD/ADRD and identify high-risk\npopulations and patients for prevention efforts. In our study, we summarized\nthe risk factors for AD/ADRD by reviewing existing meta-analyses and review\narticles on risk and preventive factors for AD/ADRD. In total, we extracted 477\nrisk factors in 10 categories from 537 studies. We constructed an interactive\nknowledge map to disseminate our study results. Most of the risk factors are\naccessible from structured Electronic Health Records (EHRs), and clinical\nnarratives show promise as information sources. However, evaluating genomic\nrisk factors using RWD remains a challenge, as genetic testing for AD/ADRD is\nstill not a common practice and is poorly documented in both structured and\nunstructured EHRs. Considering the constantly evolving research on AD/ADRD risk\nfactors, literature mining via NLP methods offers a solution to automatically\nupdate our knowledge map.\n", "date": "2024-02-03", "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": "2402.16689", "title": "Adaptation of Biomedical and Clinical Pretrained Models to French Long\n  Documents: A Comparative Study", "abstract": "  Recently, pretrained language models based on BERT have been introduced for\nthe French biomedical domain. Although these models have achieved\nstate-of-the-art results on biomedical and clinical NLP tasks, they are\nconstrained by a limited input sequence length of 512 tokens, which poses\nchallenges when applied to clinical notes. In this paper, we present a\ncomparative study of three adaptation strategies for long-sequence models,\nleveraging the Longformer architecture. We conducted evaluations of these\nmodels on 16 downstream tasks spanning both biomedical and clinical domains.\nOur findings reveal that further pre-training an English clinical model with\nFrench biomedical texts can outperform both converting a French biomedical BERT\nto the Longformer architecture and pre-training a French biomedical Longformer\nfrom scratch. The results underscore that long-sequence French biomedical\nmodels improve performance across most downstream tasks regardless of sequence\nlength, but BERT based models remain the most efficient for named entity\nrecognition tasks.\n", "date": "2024-02-26", "categories": []}
{"id": "2312.07250", "title": "Neural Machine Translation of Clinical Text: An Empirical Investigation\n  into Multilingual Pre-Trained Language Models and Transfer-Learning", "abstract": "  We conduct investigations on clinical text machine translation by examining\nmultilingual neural network models using deep learning such as Transformer\nbased structures. Furthermore, to address the language resource imbalance\nissue, we also carry out experiments using a transfer learning methodology\nbased on massive multilingual pre-trained language models (MMPLMs). The\nexperimental results on three subtasks including 1) clinical case (CC), 2)\nclinical terminology (CT), and 3) ontological concept (OC) show that our models\nachieved top-level performances in the ClinSpEn-2022 shared task on\nEnglish-Spanish clinical domain data. Furthermore, our expert-based human\nevaluations demonstrate that the small-sized pre-trained language model (PLM)\nwon over the other two extra-large language models by a large margin, in the\nclinical domain fine-tuning, which finding was never reported in the field.\nFinally, the transfer learning method works well in our experimental setting\nusing the WMT21fb model to accommodate a new language space Spanish that was\nnot seen at the pre-training stage within WMT21fb itself, which deserves more\nexploitation for clinical knowledge transformation, e.g. to investigate into\nmore languages. These research findings can shed some light on domain-specific\nmachine translation development, especially in clinical and healthcare fields.\nFurther research projects can be carried out based on our work to improve\nhealthcare text analytics and knowledge transformation. Our data will be openly\navailable for research purposes at https://github.com/HECTA-UoM/ClinicalNMT\n", "date": "2023-12-12", "categories": []}
{"id": "2402.11398", "title": "Reasoning before Comparison: LLM-Enhanced Semantic Similarity Metrics\n  for Domain Specialized Text Analysis", "abstract": "  In this study, we leverage LLM to enhance the semantic analysis and develop\nsimilarity metrics for texts, addressing the limitations of traditional\nunsupervised NLP metrics like ROUGE and BLEU. We develop a framework where LLMs\nsuch as GPT-4 are employed for zero-shot text identification and label\ngeneration for radiology reports, where the labels are then used as\nmeasurements for text similarity. By testing the proposed framework on the\nMIMIC data, we find that GPT-4 generated labels can significantly improve the\nsemantic similarity assessment, with scores more closely aligned with clinical\nground truth than traditional NLP metrics. Our work demonstrates the\npossibility of conducting semantic analysis of the text data using\nsemi-quantitative reasoning results by the LLMs for highly specialized domains.\nWhile the framework is implemented for radiology report similarity analysis,\nits concept can be extended to other specialized domains as well.\n", "date": "2024-02-17", "categories": []}
{"id": "2307.16200", "title": "A Knowledge-enhanced Two-stage Generative Framework for Medical Dialogue\n  Information Extraction", "abstract": "  This paper focuses on term-status pair extraction from medical dialogues\n(MD-TSPE), which is essential in diagnosis dialogue systems and the automatic\nscribe of electronic medical records (EMRs). In the past few years, works on\nMD-TSPE have attracted increasing research attention, especially after the\nremarkable progress made by generative methods. However, these generative\nmethods output a whole sequence consisting of term-status pairs in one stage\nand ignore integrating prior knowledge, which demands a deeper understanding to\nmodel the relationship between terms and infer the status of each term. This\npaper presents a knowledge-enhanced two-stage generative framework (KTGF) to\naddress the above challenges. Using task-specific prompts, we employ a single\nmodel to complete the MD-TSPE through two phases in a unified generative form:\nwe generate all terms the first and then generate the status of each generated\nterm. In this way, the relationship between terms can be learned more\neffectively from the sequence containing only terms in the first phase, and our\ndesigned knowledge-enhanced prompt in the second phase can leverage the\ncategory and status candidates of the generated term for status generation.\nFurthermore, our proposed special status \"not mentioned\" makes more terms\navailable and enriches the training data in the second phase, which is critical\nin the low-resource setting. The experiments on the Chunyu and CMDD datasets\nshow that the proposed method achieves superior results compared to the\nstate-of-the-art models in the full training and low-resource settings.\n", "date": "2023-07-30", "categories": []}
{"id": "2310.04270", "title": "A Comprehensive Evaluation of Large Language Models on Benchmark\n  Biomedical Text Processing Tasks", "abstract": "  Recently, Large Language Models (LLM) have demonstrated impressive capability\nto solve a wide range of tasks. However, despite their success across various\ntasks, no prior work has investigated their capability in the biomedical domain\nyet. To this end, this paper aims to evaluate the performance of LLMs on\nbenchmark biomedical tasks. For this purpose, we conduct a comprehensive\nevaluation of 4 popular LLMs in 6 diverse biomedical tasks across 26 datasets.\nTo the best of our knowledge, this is the first work that conducts an extensive\nevaluation and comparison of various LLMs in the biomedical domain.\nInterestingly, we find based on our evaluation that in biomedical datasets that\nhave smaller training sets, zero-shot LLMs even outperform the current\nstate-of-the-art fine-tuned biomedical models. This suggests that pretraining\non large text corpora makes LLMs quite specialized even in the biomedical\ndomain. We also find that not a single LLM can outperform other LLMs in all\ntasks, with the performance of different LLMs may vary depending on the task.\nWhile their performance is still quite poor in comparison to the biomedical\nmodels that were fine-tuned on large training sets, our findings demonstrate\nthat LLMs have the potential to be a valuable tool for various biomedical tasks\nthat lack large annotated data.\n", "date": "2023-10-06", "categories": []}
{"id": "2402.11883", "title": "InMD-X: Large Language Models for Internal Medicine Doctors", "abstract": "  In this paper, we introduce InMD-X, a collection of multiple large language\nmodels specifically designed to cater to the unique characteristics and demands\nof Internal Medicine Doctors (IMD). InMD-X represents a groundbreaking\ndevelopment in natural language processing, offering a suite of language models\nfine-tuned for various aspects of the internal medicine field. These models\nencompass a wide range of medical sub-specialties, enabling IMDs to perform\nmore efficient and accurate research, diagnosis, and documentation. InMD-X's\nversatility and adaptability make it a valuable tool for improving the\nhealthcare industry, enhancing communication between healthcare professionals,\nand advancing medical research. Each model within InMD-X is meticulously\ntailored to address specific challenges faced by IMDs, ensuring the highest\nlevel of precision and comprehensiveness in clinical text analysis and decision\nsupport. This paper provides an overview of the design, development, and\nevaluation of InMD-X, showcasing its potential to revolutionize the way\ninternal medicine practitioners interact with medical data and information. We\npresent results from extensive testing, demonstrating the effectiveness and\npractical utility of InMD-X in real-world medical scenarios.\n", "date": "2024-02-19", "categories": []}
{"id": "2402.13225", "title": "AgentMD: Empowering Language Agents for Risk Prediction with Large-Scale\n  Clinical Tool Learning", "abstract": "  Clinical calculators play a vital role in healthcare by offering accurate\nevidence-based predictions for various purposes such as prognosis.\nNevertheless, their widespread utilization is frequently hindered by usability\nchallenges, poor dissemination, and restricted functionality. Augmenting large\nlanguage models with extensive collections of clinical calculators presents an\nopportunity to overcome these obstacles and improve workflow efficiency, but\nthe scalability of the manual curation process poses a significant challenge.\nIn response, we introduce AgentMD, a novel language agent capable of curating\nand applying clinical calculators across various clinical contexts. Using the\npublished literature, AgentMD has automatically curated a collection of 2,164\ndiverse clinical calculators with executable functions and structured\ndocumentation, collectively named RiskCalcs. Manual evaluations show that\nRiskCalcs tools achieve an accuracy of over 80% on three quality metrics. At\ninference time, AgentMD can automatically select and apply the relevant\nRiskCalcs tools given any patient description. On the newly established RiskQA\nbenchmark, AgentMD significantly outperforms chain-of-thought prompting with\nGPT-4 (87.7% vs. 40.9% in accuracy). Additionally, we also applied AgentMD to\nreal-world clinical notes for analyzing both population-level and risk-level\npatient characteristics. In summary, our study illustrates the utility of\nlanguage agents augmented with clinical calculators for healthcare analytics\nand patient care.\n", "date": "2024-02-20", "categories": []}
{"id": "2312.13521", "title": "Preparing to Integrate Generative Pretrained Transformer Series 4 models\n  into Genetic Variant Assessment Workflows: Assessing Performance, Drift, and\n  Nondeterminism Characteristics Relative to Classifying Functional Evidence in\n  Literature", "abstract": "  Background. Large Language Models (LLMs) hold promise for improving genetic\nvariant literature review in clinical testing. We assessed Generative\nPretrained Transformer 4's (GPT-4) performance, nondeterminism, and drift to\ninform its suitability for use in complex clinical processes. Methods. A\n2-prompt process for classification of functional evidence was optimized using\na development set of 45 articles. The prompts asked GPT-4 to supply all\nfunctional data present in an article related to a variant or indicate that no\nfunctional evidence is present. For articles indicated as containing functional\nevidence, a second prompt asked GPT-4 to classify the evidence into pathogenic,\nbenign, or intermediate/inconclusive categories. A final test set of 72\nmanually classified articles was used to test performance. Results. Over a\n2.5-month period (Dec 2023-Feb 2024), we observed substantial differences in\nintraday (nondeterminism) and across day (drift) results, which lessened after\n1/18/24. This variability is seen within and across models in the GPT-4 series,\naffecting different performance statistics to different degrees. Twenty runs\nafter 1/18/24 identified articles containing functional evidence with 92.2%\nsensitivity, 95.6% positive predictive value (PPV) and 86.3% negative\npredictive value (NPV). The second prompt's identified pathogenic functional\nevidence with 90.0% sensitivity, 74.0% PPV and 95.3% NVP and for benign\nevidence with 88.0% sensitivity, 76.6% PPV and 96.9% NVP. Conclusion.\nNondeterminism and drift within LLMs must be assessed and monitored when\nintroducing LLM based functionality into clinical workflows. Failing to do this\nassessment or accounting for these challenges could lead to incorrect or\nmissing information that is critical for patient care. The performance of our\nprompts appears adequate to assist in article prioritization but not in\nautomated decision making.\n", "date": "2023-12-21", "categories": []}
{"id": "2402.01741", "title": "Development and Testing of a Novel Large Language Model-Based Clinical\n  Decision Support Systems for Medication Safety in 12 Clinical Specialties", "abstract": "  Importance: We introduce a novel Retrieval Augmented Generation (RAG)-Large\nLanguage Model (LLM) framework as a Clinical Decision Support Systems (CDSS) to\nsupport safe medication prescription.\n  Objective: To evaluate the efficacy of LLM-based CDSS in correctly\nidentifying medication errors in different patient case vignettes from diverse\nmedical and surgical sub-disciplines, against a human expert panel derived\nground truth. We compared performance for under 2 different CDSS practical\nhealthcare integration modalities: LLM-based CDSS alone (fully autonomous mode)\nvs junior pharmacist + LLM-based CDSS (co-pilot, assistive mode).\n  Design, Setting, and Participants: Utilizing a RAG model with\nstate-of-the-art medically-related LLMs (GPT-4, Gemini Pro 1.0 and Med-PaLM 2),\nthis study used 61 prescribing error scenarios embedded into 23 complex\nclinical vignettes across 12 different medical and surgical specialties. A\nmultidisciplinary expert panel assessed these cases for Drug-Related Problems\n(DRPs) using the PCNE classification and graded severity / potential for harm\nusing revised NCC MERP medication error index. We compared.\n  Results RAG-LLM performed better compared to LLM alone. When employed in a\nco-pilot mode, accuracy, recall, and F1 scores were optimized, indicating\neffectiveness in identifying moderate to severe DRPs. The accuracy of DRP\ndetection with RAG-LLM improved in several categories but at the expense of\nlower precision.\n  Conclusions This study established that a RAG-LLM based CDSS significantly\nboosts the accuracy of medication error identification when used alongside\njunior pharmacists (co-pilot), with notable improvements in detecting severe\nDRPs. This study also illuminates the comparative performance of current\nstate-of-the-art LLMs in RAG-based CDSS systems.\n", "date": "2024-01-29", "categories": []}
{"id": "2402.10951", "title": "DAEDRA: A language model for predicting outcomes in passive\n  pharmacovigilance reporting", "abstract": "  Over the recent years, the emergence of large language models (LLMs) has\ngiven rise to a proliferation of domain-specific models that are intended to\nreflect the particularities of linguistic context and content as a correlate of\nthe originating domain. This paper details the conception, design, training and\nevaluation of DAEDRA, a LLM designed to detect regulatory-relevant outcomes\n(mortality, ER attendance and hospitalisation) in adverse event reports\nelicited through passive reporting (PR). While PR is a highly cost-efficient\nway of eliciting information from a wide and diverse audience -- typically\nincluding not only physicians and healthcare providers but also patients,\nfamily members and other lay stakeholders --, this diversity makes PR corpora\ndifficult to analyse. Generic language models may not capture the complex\nclinical dimensions while specific clinical or biomedical models may not\nperform well on lay reports. To evaluate the utility of a subdomain-specific\nlanguage model, an adaptive training approach was adapted, wherein base\nlanguage model candidates were evaluated on a subset of the corpus, and the\nbest performer was trained on the entire corpus. This yielded a small but\nsignificant improvement in $F_1$ (+1%), precision (+2.5%) and recall (+3.8%),\nat a relatively low training cost and a single-day training time.\nSubdomain-specific LLMs continue to be viable options for better results when\nanalysing highly specialised corpora.\n", "date": "2024-02-10", "categories": []}
{"id": "2402.11177", "title": "A Question Answering Based Pipeline for Comprehensive Chinese EHR\n  Information Extraction", "abstract": "  Electronic health records (EHRs) hold significant value for research and\napplications. As a new way of information extraction, question answering (QA)\ncan extract more flexible information than conventional methods and is more\naccessible to clinical researchers, but its progress is impeded by the scarcity\nof annotated data. In this paper, we propose a novel approach that\nautomatically generates training data for transfer learning of QA models. Our\npipeline incorporates a preprocessing module to handle challenges posed by\nextraction types that are not readily compatible with extractive QA frameworks,\nincluding cases with discontinuous answers and many-to-one relationships. The\nobtained QA model exhibits excellent performance on subtasks of information\nextraction in EHRs, and it can effectively handle few-shot or zero-shot\nsettings involving yes-no questions. Case studies and ablation studies\ndemonstrate the necessity of each component in our design, and the resulting\nmodel is deemed suitable for practical use.\n", "date": "2024-02-17", "categories": []}
{"id": "2402.11882", "title": "NOTE: Notable generation Of patient Text summaries through Efficient\n  approach based on direct preference optimization", "abstract": "  The discharge summary is a one of critical documents in the patient journey,\nencompassing all events experienced during hospitalization, including multiple\nvisits, medications, tests, surgery/procedures, and admissions/discharge.\nProviding a summary of the patient's progress is crucial, as it significantly\ninfluences future care and planning. Consequently, clinicians face the\nlaborious and resource-intensive task of manually collecting, organizing, and\ncombining all the necessary data for a discharge summary. Therefore, we propose\n\"NOTE\", which stands for \"Notable generation Of patient Text summaries through\nan Efficient approach based on direct preference optimization\". NOTE is based\non Medical Information Mart for Intensive Care- III dataset and summarizes a\nsingle hospitalization of a patient. Patient events are sequentially combined\nand used to generate a discharge summary for each hospitalization. In the\npresent circumstances, large language models' application programming\ninterfaces (LLMs' APIs) are widely available, but importing and exporting\nmedical data presents significant challenges due to privacy protection policies\nin healthcare institutions. Moreover, to ensure optimal performance, it is\nessential to implement a lightweight model for internal server or program\nwithin the hospital. Therefore, we utilized DPO and parameter efficient fine\ntuning (PEFT) techniques to apply a fine-tuning method that guarantees superior\nperformance. To demonstrate the practical application of the developed NOTE, we\nprovide a webpage-based demonstration software. In the future, we will aim to\ndeploy the software available for actual use by clinicians in hospital. NOTE\ncan be utilized to generate various summaries not only discharge summaries but\nalso throughout a patient's journey, thereby alleviating the labor-intensive\nworkload of clinicians and aiming for increased efficiency.\n", "date": "2024-02-19", "categories": []}
{"id": "2401.16240", "title": "Combining Hierachical VAEs with LLMs for clinically meaningful timeline\n  summarisation in social media", "abstract": "  We introduce a hybrid abstractive summarisation approach combining\nhierarchical VAE with LLMs (LlaMA-2) to produce clinically meaningful summaries\nfrom social media user timelines, appropriate for mental health monitoring. The\nsummaries combine two different narrative points of view: clinical insights in\nthird person useful for a clinician are generated by feeding into an LLM\nspecialised clinical prompts, and importantly, a temporally sensitive\nabstractive summary of the user's timeline in first person, generated by a\nnovel hierarchical variational autoencoder, TH-VAE. We assess the generated\nsummaries via automatic evaluation against expert summaries and via human\nevaluation with clinical experts, showing that timeline summarisation by TH-VAE\nresults in more factual and logically coherent summaries rich in clinical\nutility and superior to LLM-only approaches in capturing changes over time.\n", "date": "2024-01-29", "categories": []}
{"id": "2402.10597", "title": "Efficiency at Scale: Investigating the Performance of Diminutive\n  Language Models in Clinical Tasks", "abstract": "  The entry of large language models (LLMs) into research and commercial spaces\nhas led to a trend of ever-larger models, with initial promises of\ngeneralisability, followed by a widespread desire to downsize and create\nspecialised models without the need for complete fine-tuning, using Parameter\nEfficient Fine-tuning (PEFT) methods. We present an investigation into the\nsuitability of different PEFT methods to clinical decision-making tasks, across\na range of model sizes, including extremely small models with as few as $25$\nmillion parameters.\n  Our analysis shows that the performance of most PEFT approaches varies\nsignificantly from one task to another, with the exception of LoRA, which\nmaintains relatively high performance across all model sizes and tasks,\ntypically approaching or matching full fine-tuned performance. The\neffectiveness of PEFT methods in the clinical domain is evident, particularly\nfor specialised models which can operate on low-cost, in-house computing\ninfrastructure. The advantages of these models, in terms of speed and reduced\ntraining costs, dramatically outweighs any performance gain from large\nfoundation LLMs. Furthermore, we highlight how domain-specific pre-training\ninteracts with PEFT methods and model size, and discuss how these factors\ninterplay to provide the best efficiency-performance trade-off. Full code\navailable at: tbd.\n", "date": "2024-02-16", "categories": []}
{"id": "2309.13184", "title": "Document Understanding for Healthcare Referrals", "abstract": "  Reliance on scanned documents and fax communication for healthcare referrals\nleads to high administrative costs and errors that may affect patient care. In\nthis work we propose a hybrid model leveraging LayoutLMv3 along with\ndomain-specific rules to identify key patient, physician, and exam-related\nentities in faxed referral documents. We explore some of the challenges in\napplying a document understanding model to referrals, which have formats\nvarying by medical practice, and evaluate model performance using MUC-5 metrics\nto obtain appropriate metrics for the practical use case. Our analysis shows\nthe addition of domain-specific rules to the transformer model yields greatly\nincreased precision and F1 scores, suggesting a hybrid model trained on a\ncurated dataset can increase efficiency in referral management.\n", "date": "2023-09-22", "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": "2402.07645", "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": "2402.03435", "title": "Psychological Assessments with Large Language Models: A Privacy-Focused\n  and Cost-Effective Approach", "abstract": "  This study explores the use of Large Language Models (LLMs) to analyze text\ncomments from Reddit users, aiming to achieve two primary objectives: firstly,\nto pinpoint critical excerpts that support a predefined psychological\nassessment of suicidal risk; and secondly, to summarize the material to\nsubstantiate the preassigned suicidal risk level. The work is circumscribed to\nthe use of \"open-source\" LLMs that can be run locally, thereby enhancing data\nprivacy. Furthermore, it prioritizes models with low computational\nrequirements, making it accessible to both individuals and institutions\noperating on limited computing budgets. The implemented strategy only relies on\na carefully crafted prompt and a grammar to guide the LLM's text completion.\nDespite its simplicity, the evaluation metrics show outstanding results, making\nit a valuable privacy-focused and cost-effective approach. This work is part of\nthe Computational Linguistics and Clinical Psychology (CLPsych) 2024 shared\ntask.\n", "date": "2024-02-05", "categories": []}
{"id": "2402.03597", "title": "Identifying Reasons for Contraceptive Switching from Real-World Data\n  Using Large Language Models", "abstract": "  Prescription contraceptives play a critical role in supporting women's\nreproductive health. With nearly 50 million women in the United States using\ncontraceptives, understanding the factors that drive contraceptives selection\nand switching is of significant interest. However, many factors related to\nmedication switching are often only captured in unstructured clinical notes and\ncan be difficult to extract. Here, we evaluate the zero-shot abilities of a\nrecently developed large language model, GPT-4 (via HIPAA-compliant Microsoft\nAzure API), to identify reasons for switching between classes of contraceptives\nfrom the UCSF Information Commons clinical notes dataset. We demonstrate that\nGPT-4 can accurately extract reasons for contraceptive switching, outperforming\nbaseline BERT-based models with microF1 scores of 0.849 and 0.881 for\ncontraceptive start and stop extraction, respectively. Human evaluation of\nGPT-4-extracted reasons for switching showed 91.4% accuracy, with minimal\nhallucinations. Using extracted reasons, we identified patient preference,\nadverse events, and insurance as key reasons for switching using unsupervised\ntopic modeling approaches. Notably, we also showed using our approach that\n\"weight gain/mood change\" and \"insurance coverage\" are disproportionately found\nas reasons for contraceptive switching in specific demographic populations. Our\ncode and supplemental data are available at\nhttps://github.com/BMiao10/contraceptive-switching.\n", "date": "2024-02-06", "categories": []}
{"id": "2401.15222", "title": "Transfer Learning for the Prediction of Entity Modifiers in Clinical\n  Text: Application to Opioid Use Disorder Case Detection", "abstract": "  Background: The semantics of entities extracted from a clinical text can be\ndramatically altered by modifiers, including entity negation, uncertainty,\nconditionality, severity, and subject. Existing models for determining\nmodifiers of clinical entities involve regular expression or features weights\nthat are trained independently for each modifier.\n  Methods: We develop and evaluate a multi-task transformer architecture design\nwhere modifiers are learned and predicted jointly using the publicly available\nSemEval 2015 Task 14 corpus and a new Opioid Use Disorder (OUD) data set that\ncontains modifiers shared with SemEval as well as novel modifiers specific for\nOUD. We evaluate the effectiveness of our multi-task learning approach versus\npreviously published systems and assess the feasibility of transfer learning\nfor clinical entity modifiers when only a portion of clinical modifiers are\nshared.\n  Results: Our approach achieved state-of-the-art results on the ShARe corpus\nfrom SemEval 2015 Task 14, showing an increase of 1.1% on weighted accuracy,\n1.7% on unweighted accuracy, and 10% on micro F1 scores.\n  Conclusions: We show that learned weights from our shared model can be\neffectively transferred to a new partially matched data set, validating the use\nof transfer learning for clinical text modifiers\n", "date": "2024-01-26", "categories": []}
{"id": "2402.01690", "title": "Linguistic-Based Mild Cognitive Impairment Detection Using Informative\n  Loss", "abstract": "  This paper presents a deep learning method using Natural Language Processing\n(NLP) techniques, to distinguish between Mild Cognitive Impairment (MCI) and\nNormal Cognitive (NC) conditions in older adults. We propose a framework that\nanalyzes transcripts generated from video interviews collected within the\nI-CONECT study project, a randomized controlled trial aimed at improving\ncognitive functions through video chats. Our proposed NLP framework consists of\ntwo Transformer-based modules, namely Sentence Embedding (SE) and Sentence\nCross Attention (SCA). First, the SE module captures contextual relationships\nbetween words within each sentence. Subsequently, the SCA module extracts\ntemporal features from a sequence of sentences. This feature is then used by a\nMulti-Layer Perceptron (MLP) for the classification of subjects into MCI or NC.\nTo build a robust model, we propose a novel loss function, called InfoLoss,\nthat considers the reduction in entropy by observing each sequence of sentences\nto ultimately enhance the classification accuracy. The results of our\ncomprehensive model evaluation using the I-CONECT dataset show that our\nframework can distinguish between MCI and NC with an average area under the\ncurve of 84.75%.\n", "date": "2024-01-23", "categories": []}
{"id": "2402.01751", "title": "Performance Assessment of ChatGPT vs Bard in Detecting Alzheimer's\n  Dementia", "abstract": "  Large language models (LLMs) find increasing applications in many fields.\nHere, three LLM chatbots (ChatGPT-3.5, ChatGPT-4 and Bard) are assessed - in\ntheir current form, as publicly available - for their ability to recognize\nAlzheimer's Dementia (AD) and Cognitively Normal (CN) individuals using textual\ninput derived from spontaneous speech recordings. Zero-shot learning approach\nis used at two levels of independent queries, with the second query\n(chain-of-thought prompting) eliciting more detailed than the first. Each LLM\nchatbot's performance is evaluated on the prediction generated in terms of\naccuracy, sensitivity, specificity, precision and F1 score. LLM chatbots\ngenerated three-class outcome (\"AD\", \"CN\", or \"Unsure\"). When positively\nidentifying AD, Bard produced highest true-positives (89% recall) and highest\nF1 score (71%), but tended to misidentify CN as AD, with high confidence (low\n\"Unsure\" rates); for positively identifying CN, GPT-4 resulted in the highest\ntrue-negatives at 56% and highest F1 score (62%), adopting a diplomatic stance\n(moderate \"Unsure\" rates). Overall, three LLM chatbots identify AD vs CN\nsurpassing chance-levels but do not currently satisfy clinical application.\n", "date": "2024-01-30", "categories": []}
{"id": "2402.02558", "title": "Enhancing Robustness in Biomedical NLI Models: A Probing Approach for\n  Clinical Trials", "abstract": "  Large Language Models have revolutionized various fields and industries, such\nas Conversational AI, Content Generation, Information Retrieval, Business\nIntelligence, and Medical, to name a few. One major application in the field of\nmedical is to analyze and investigate clinical trials for entailment\ntasks.However, It has been observed that Large Language Models are susceptible\nto shortcut learning, factual inconsistency, and performance degradation with\nlittle variation in context. Adversarial and robust testing is performed to\nensure the integrity of models output. But, ambiguity still persists. In order\nto ensure the integrity of the reasoning performed and investigate the model\nhas correct syntactic and semantic understanding probing is used. Here, I used\nmnestic probing to investigate the Sci-five model, trained on clinical trial. I\ninvestigated the model for feature learnt with respect to natural logic. To\nachieve the target, I trained task specific probes. Used these probes to\ninvestigate the final layers of trained model. Then, fine tuned the trained\nmodel using iterative null projection. The results shows that model accuracy\nimproved. During experimentation, I observed that size of the probe has affect\non the fine tuning process.\n", "date": "2024-02-04", "categories": []}
{"id": "2402.02656", "title": "RACER: An LLM-powered Methodology for Scalable Analysis of\n  Semi-structured Mental Health Interviews", "abstract": "  Semi-structured interviews (SSIs) are a commonly employed data-collection\nmethod in healthcare research, offering in-depth qualitative insights into\nsubject experiences. Despite their value, the manual analysis of SSIs is\nnotoriously time-consuming and labor-intensive, in part due to the difficulty\nof extracting and categorizing emotional responses, and challenges in scaling\nhuman evaluation for large populations. In this study, we develop RACER, a\nLarge Language Model (LLM) based expert-guided automated pipeline that\nefficiently converts raw interview transcripts into insightful domain-relevant\nthemes and sub-themes. We used RACER to analyze SSIs conducted with 93\nhealthcare professionals and trainees to assess the broad personal and\nprofessional mental health impacts of the COVID-19 crisis. RACER achieves\nmoderately high agreement with two human evaluators (72%), which approaches the\nhuman inter-rater agreement (77%). Interestingly, LLMs and humans struggle with\nsimilar content involving nuanced emotional, ambivalent/dialectical, and\npsychological statements. Our study highlights the opportunities and challenges\nin using LLMs to improve research efficiency and opens new avenues for scalable\nanalysis of SSIs in healthcare research.\n", "date": "2024-02-05", "categories": []}
{"id": "2402.03172", "title": "Accurate and Well-Calibrated ICD Code Assignment Through Attention Over\n  Diverse Label Embeddings", "abstract": "  Although the International Classification of Diseases (ICD) has been adopted\nworldwide, manually assigning ICD codes to clinical text is time-consuming,\nerror-prone, and expensive, motivating the development of automated approaches.\nThis paper describes a novel approach for automated ICD coding, combining\nseveral ideas from previous related work. We specifically employ a strong\nTransformer-based model as a text encoder and, to handle lengthy clinical\nnarratives, we explored either (a) adapting the base encoder model into a\nLongformer, or (b) dividing the text into chunks and processing each chunk\nindependently. The representations produced by the encoder are combined with a\nlabel embedding mechanism that explores diverse ICD code synonyms. Experiments\nwith different splits of the MIMIC-III dataset show that the proposed approach\noutperforms the current state-of-the-art models in ICD coding, with the label\nembeddings significantly contributing to the good performance. Our approach\nalso leads to properly calibrated classification results, which can effectively\ninform downstream tasks such as quantification.\n", "date": "2024-02-05", "categories": []}
{"id": "2401.17602", "title": "Assertion Detection Large Language Model In-context Learning LoRA\n  Fine-tuning", "abstract": "  In this study, we aim to address the task of assertion detection when\nextracting medical concepts from clinical notes, a key process in clinical\nnatural language processing (NLP). Assertion detection in clinical NLP usually\ninvolves identifying assertion types for medical concepts in the clinical text,\nnamely certainty (whether the medical concept is positive, negated, possible,\nor hypothetical), temporality (whether the medical concept is for present or\nthe past history), and experiencer (whether the medical concept is described\nfor the patient or a family member). These assertion types are essential for\nhealthcare professionals to quickly and clearly understand the context of\nmedical conditions from unstructured clinical texts, directly influencing the\nquality and outcomes of patient care. Although widely used, traditional\nmethods, particularly rule-based NLP systems and machine learning or deep\nlearning models, demand intensive manual efforts to create patterns and tend to\noverlook less common assertion types, leading to an incomplete understanding of\nthe context. To address this challenge, our research introduces a novel\nmethodology that utilizes Large Language Models (LLMs) pre-trained on a vast\narray of medical data for assertion detection. We enhanced the current method\nwith advanced reasoning techniques, including Tree of Thought (ToT), Chain of\nThought (CoT), and Self-Consistency (SC), and refine it further with Low-Rank\nAdaptation (LoRA) fine-tuning. We first evaluated the model on the i2b2 2010\nassertion dataset. Our method achieved a micro-averaged F-1 of 0.89, with 0.11\nimprovements over the previous works. To further assess the generalizability of\nour approach, we extended our evaluation to a local dataset that focused on\nsleep concept extraction. Our approach achieved an F-1 of 0.74, which is 0.31\nhigher than the previous method.\n", "date": "2024-01-31", "categories": []}
{"id": "2305.10163", "title": "Large Language Models Leverage External Knowledge to Extend Clinical\n  Insight Beyond Language Boundaries", "abstract": "  $\\textbf{Objectives}$: Large Language Models (LLMs) such as ChatGPT and\nMed-PaLM have excelled in various medical question-answering tasks. However,\nthese English-centric models encounter challenges in non-English clinical\nsettings, primarily due to limited clinical knowledge in respective languages,\na consequence of imbalanced training corpora. We systematically evaluate LLMs\nin the Chinese medical context and develop a novel in-context learning\nframework to enhance their performance.\n  $\\textbf{Materials and Methods}$: The latest China National Medical Licensing\nExamination (CNMLE-2022) served as the benchmark. We collected 53 medical books\nand 381,149 medical questions to construct the medical knowledge base and\nquestion bank. The proposed Knowledge and Few-shot Enhancement In-context\nLearning (KFE) framework leverages the in-context learning ability of LLMs to\nintegrate diverse external clinical knowledge sources. We evaluated KFE with\nChatGPT(GPT3.5), GPT4, Baichuan2(BC2)-7B, and BC2-13B in CNMLE-2022 and\ninvestigated the effectiveness of different pathways for incorporating LLMs\nwith medical knowledge from 7 perspectives.\n  $\\textbf{Results}$: Directly applying ChatGPT failed to qualify for the\nCNMLE-2022 at a score of 51. Cooperated with the KFE, the LLMs with varying\nsizes yielded consistent and significant improvements. The ChatGPT's\nperformance surged to 70.04 and GPT-4 achieved the highest score of 82.59. This\nsurpasses the qualification threshold (60) and exceeds the average human score\nof 68.70. It also enabled a smaller BC2-13B to pass the examination, showcasing\nthe great potential in low-resource settings.\n  $\\textbf{Conclusion}$: By synergizing medical knowledge through in-context\nlearning, LLM can extend clinical insight beyond language barriers,\nsignificantly reducing language-related disparities of LLM applications and\nensuring global benefit in healthcare.\n", "date": "2023-05-17", "categories": []}
{"id": "2401.15780", "title": "Fine-Tuned Large Language Models for Symptom Recognition from Spanish\n  Clinical Text", "abstract": "  The accurate recognition of symptoms in clinical reports is significantly\nimportant in the fields of healthcare and biomedical natural language\nprocessing. These entities serve as essential building blocks for clinical\ninformation extraction, enabling retrieval of critical medical insights from\nvast amounts of textual data. Furthermore, the ability to identify and\ncategorize these entities is fundamental for developing advanced clinical\ndecision support systems, aiding healthcare professionals in diagnosis and\ntreatment planning. In this study, we participated in SympTEMIST, a shared task\non the detection of symptoms, signs and findings in Spanish medical documents.\nWe combine a set of large language models fine-tuned with the data released by\nthe organizers.\n", "date": "2024-01-28", "categories": []}
{"id": "2401.14490", "title": "LongHealth: A Question Answering Benchmark with Long Clinical Documents", "abstract": "  Background: Recent advancements in large language models (LLMs) offer\npotential benefits in healthcare, particularly in processing extensive patient\nrecords. However, existing benchmarks do not fully assess LLMs' capability in\nhandling real-world, lengthy clinical data.\n  Methods: We present the LongHealth benchmark, comprising 20 detailed\nfictional patient cases across various diseases, with each case containing\n5,090 to 6,754 words. The benchmark challenges LLMs with 400 multiple-choice\nquestions in three categories: information extraction, negation, and sorting,\nchallenging LLMs to extract and interpret information from large clinical\ndocuments.\n  Results: We evaluated nine open-source LLMs with a minimum of 16,000 tokens\nand also included OpenAI's proprietary and cost-efficient GPT-3.5 Turbo for\ncomparison. The highest accuracy was observed for Mixtral-8x7B-Instruct-v0.1,\nparticularly in tasks focused on information retrieval from single and multiple\npatient documents. However, all models struggled significantly in tasks\nrequiring the identification of missing information, highlighting a critical\narea for improvement in clinical data interpretation.\n  Conclusion: While LLMs show considerable potential for processing long\nclinical documents, their current accuracy levels are insufficient for reliable\nclinical use, especially in scenarios requiring the identification of missing\ninformation. The LongHealth benchmark provides a more realistic assessment of\nLLMs in a healthcare setting and highlights the need for further model\nrefinement for safe and effective clinical application.\n  We make the benchmark and evaluation code publicly available.\n", "date": "2024-01-25", "categories": []}
{"id": "2401.14493", "title": "K-QA: A Real-World Medical Q&A Benchmark", "abstract": "  Ensuring the accuracy of responses provided by large language models (LLMs)\nis crucial, particularly in clinical settings where incorrect information may\ndirectly impact patient health. To address this challenge, we construct K-QA, a\ndataset containing 1,212 patient questions originating from real-world\nconversations held on K Health (an AI-driven clinical platform). We employ a\npanel of in-house physicians to answer and manually decompose a subset of K-QA\ninto self-contained statements. Additionally, we formulate two NLI-based\nevaluation metrics approximating recall and precision: (1) comprehensiveness,\nmeasuring the percentage of essential clinical information in the generated\nanswer and (2) hallucination rate, measuring the number of statements from the\nphysician-curated response contradicted by the LLM answer. Finally, we use K-QA\nalong with these metrics to evaluate several state-of-the-art models, as well\nas the effect of in-context learning and medically-oriented augmented retrieval\nschemes developed by the authors. Our findings indicate that in-context\nlearning improves the comprehensiveness of the models, and augmented retrieval\nis effective in reducing hallucinations. We make K-QA available to to the\ncommunity to spur research into medically accurate NLP applications.\n", "date": "2024-01-25", "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": "2401.13887", "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": "2401.11120", "title": "Enhancing Large Language Models for Clinical Decision Support by\n  Incorporating Clinical Practice Guidelines", "abstract": "  Background Large Language Models (LLMs), enhanced with Clinical Practice\nGuidelines (CPGs), can significantly improve Clinical Decision Support (CDS).\nHowever, methods for incorporating CPGs into LLMs are not well studied. Methods\nWe develop three distinct methods for incorporating CPGs into LLMs: Binary\nDecision Tree (BDT), Program-Aided Graph Construction (PAGC), and\nChain-of-Thought-Few-Shot Prompting (CoT-FSP). To evaluate the effectiveness of\nthe proposed methods, we create a set of synthetic patient descriptions and\nconduct both automatic and human evaluation of the responses generated by four\nLLMs: GPT-4, GPT-3.5 Turbo, LLaMA, and PaLM 2. Zero-Shot Prompting (ZSP) was\nused as the baseline method. We focus on CDS for COVID-19 outpatient treatment\nas the case study. Results All four LLMs exhibit improved performance when\nenhanced with CPGs compared to the baseline ZSP. BDT outperformed both CoT-FSP\nand PAGC in automatic evaluation. All of the proposed methods demonstrated high\nperformance in human evaluation. Conclusion LLMs enhanced with CPGs demonstrate\nsuperior performance, as compared to plain LLMs with ZSP, in providing accurate\nrecommendations for COVID-19 outpatient treatment, which also highlights the\npotential for broader applications beyond the case study.\n", "date": "2024-01-20", "categories": []}
{"id": "2401.12993", "title": "Estimating the severity of dental and oral problems via sentiment\n  classification over clinical reports", "abstract": "  Analyzing authors' sentiments in texts as a technique for identifying text\npolarity can be practical and useful in various fields, including medicine and\ndentistry. Currently, due to factors such as patients' limited knowledge about\ntheir condition, difficulties in accessing specialist doctors, or fear of\nillness, particularly in pandemic conditions, there might be a delay between\nreceiving a radiology report and consulting a doctor. In some cases, this delay\ncan pose significant risks to the patient, making timely decision-making\ncrucial. Having an automatic system that can inform patients about the\ndeterioration of their condition by analyzing the text of radiology reports\ncould greatly impact timely decision-making. In this study, a dataset\ncomprising 1,134 cone-beam computed tomography (CBCT) photo reports was\ncollected from the Shiraz University of Medical Sciences. Each case was\nexamined, and an expert labeled a severity level for the patient's condition on\neach document. After preprocessing all the text data, a deep learning model\nbased on Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM)\nnetwork architecture, known as CNN-LSTM, was developed to detect the severity\nlevel of the patient's problem based on sentiment analysis in the radiologist's\nreport. The model's performance was evaluated on two datasets, each with two\nand four classes, in both imbalanced and balanced scenarios. Finally, to\ndemonstrate the effectiveness of our model, we compared its performance with\nthat of other classification models. The results, along with one-way ANOVA and\nTukey's test, indicated that our proposed model (CNN-LSTM) performed the best\naccording to precision, recall, and f-measure criteria. This suggests that it\ncan be a reliable model for estimating the severity of oral and dental\ndiseases, thereby assisting patients.\n", "date": "2024-01-17", "categories": []}
{"id": "2401.12994", "title": "Automated Scoring of Clinical Patient Notes using Advanced NLP and\n  Pseudo Labeling", "abstract": "  Clinical patient notes are critical for documenting patient interactions,\ndiagnoses, and treatment plans in medical practice. Ensuring accurate\nevaluation of these notes is essential for medical education and certification.\nHowever, manual evaluation is complex and time-consuming, often resulting in\nvariability and resource-intensive assessments. To tackle these challenges,\nthis research introduces an approach leveraging state-of-the-art Natural\nLanguage Processing (NLP) techniques, specifically Masked Language Modeling\n(MLM) pretraining, and pseudo labeling. Our methodology enhances efficiency and\neffectiveness, significantly reducing training time without compromising\nperformance. Experimental results showcase improved model performance,\nindicating a potential transformation in clinical note assessment.\n", "date": "2024-01-18", "categories": []}
{"id": "2401.12996", "title": "A Comparison of Veterans with Problematic Opioid Use Identified through\n  Natural Language Processing of Clinical Notes versus Using Diagnostic Codes", "abstract": "  Background: Electronic health records (EHRs) are a data source for opioid\nresearch. Opioid use disorder is known to be under-coded as a diagnosis, yet\nproblematic opioid use can be documented in clinical notes.\n  Objectives: Our goals were 1) to identify problematic opioid use from a full\nrange of clinical notes; and 2) to compare the characteristics of patients\nidentified as having problematic opioid use, exclusively documented in clinical\nnotes, to those having documented ICD opioid use disorder diagnostic codes.\n  Materials and Methods: We developed and applied a natural language processing\n(NLP) tool to the clinical notes of a patient cohort (n=222,371) from two\nVeteran Affairs service regions to identify patients with problematic opioid\nuse. We also used a set of ICD diagnostic codes to identify patients with\nopioid use disorder from the same cohort. We compared the demographic and\nclinical characteristics of patients identified only through NLP, to those of\npatients identified through ICD codes.\n  Results: NLP exclusively identified 57,331 patients; 6,997 patients had\npositive ICD code identifications. Patients exclusively identified through NLP\nwere more likely to be women. Those identified through ICD codes were more\nlikely to be male, younger, have concurrent benzodiazepine prescriptions, more\ncomorbidities, more care encounters, and less likely to be married. Patients in\nthe NLP and ICD groups had substantially elevated comorbidity levels compared\nto patients not documented as experiencing problematic opioid use.\n  Conclusions: NLP is a feasible approach for identifying problematic opioid\nuse not otherwise recorded by ICD codes. Clinicians may be reluctant to code\nfor opioid use disorder. It is therefore incumbent on the healthcare team to\nsearch for documentation of opioid concerns within clinical notes.\n", "date": "2024-01-18", "categories": []}
{"id": "2401.13588", "title": "Evaluation of General Large Language Models in Contextually Assessing\n  Semantic Concepts Extracted from Adult Critical Care Electronic Health Record\n  Notes", "abstract": "  The field of healthcare has increasingly turned its focus towards Large\nLanguage Models (LLMs) due to their remarkable performance. However, their\nperformance in actual clinical applications has been underexplored. Traditional\nevaluations based on question-answering tasks don't fully capture the nuanced\ncontexts. This gap highlights the need for more in-depth and practical\nassessments of LLMs in real-world healthcare settings. Objective: We sought to\nevaluate the performance of LLMs in the complex clinical context of adult\ncritical care medicine using systematic and comprehensible analytic methods,\nincluding clinician annotation and adjudication. Methods: We investigated the\nperformance of three general LLMs in understanding and processing real-world\nclinical notes. Concepts from 150 clinical notes were identified by MetaMap and\nthen labeled by 9 clinicians. Each LLM's proficiency was evaluated by\nidentifying the temporality and negation of these concepts using different\nprompts for an in-depth analysis. Results: GPT-4 showed overall superior\nperformance compared to other LLMs. In contrast, both GPT-3.5 and\ntext-davinci-003 exhibit enhanced performance when the appropriate prompting\nstrategies are employed. The GPT family models have demonstrated considerable\nefficiency, evidenced by their cost-effectiveness and time-saving capabilities.\nConclusion: A comprehensive qualitative performance evaluation framework for\nLLMs is developed and operationalized. This framework goes beyond singular\nperformance aspects. With expert annotations, this methodology not only\nvalidates LLMs' capabilities in processing complex medical data but also\nestablishes a benchmark for future LLM evaluations across specialized domains.\n", "date": "2024-01-24", "categories": []}
{"id": "2401.10995", "title": "The Radiation Oncology NLP Database", "abstract": "  We present the Radiation Oncology NLP Database (ROND), the first dedicated\nNatural Language Processing (NLP) dataset for radiation oncology, an important\nmedical specialty that has received limited attention from the NLP community in\nthe past. With the advent of Artificial General Intelligence (AGI), there is an\nincreasing need for specialized datasets and benchmarks to facilitate research\nand development. ROND is specifically designed to address this gap in the\ndomain of radiation oncology, a field that offers many opportunities for NLP\nexploration. It encompasses various NLP tasks including Logic Reasoning, Text\nClassification, Named Entity Recognition (NER), Question Answering (QA), Text\nSummarization, and Patient-Clinician Conversations, each with a distinct focus\non radiation oncology concepts and application cases. In addition, we have\ndeveloped an instruction-tuning dataset consisting of over 20k instruction\npairs (based on ROND) and trained a large language model, CancerChat. This\nserves to demonstrate the potential of instruction-tuning large language models\nwithin a highly-specialized medical domain. The evaluation results in this\nstudy could serve as baseline results for future research. ROND aims to\nstimulate advancements in radiation oncology and clinical NLP by offering a\nplatform for testing and improving algorithms and models in a domain-specific\ncontext. The ROND dataset is a joint effort of multiple U.S. health\ninstitutions. The data is available at\nhttps://github.com/zl-liu/Radiation-Oncology-NLP-Database.\n", "date": "2024-01-19", "categories": []}
{"id": "2401.10850", "title": "Advancements in eHealth Data Analytics through Natural Language\n  Processing and Deep Learning", "abstract": "  The healthcare environment is commonly referred to as \"information-rich\" but\nalso \"knowledge poor\". Healthcare systems collect huge amounts of data from\nvarious sources: lab reports, medical letters, logs of medical tools or\nprograms, medical prescriptions, etc. These massive sets of data can provide\ngreat knowledge and information that can improve the medical services, and\noverall the healthcare domain, such as disease prediction by analyzing the\npatient's symptoms or disease prevention, by facilitating the discovery of\nbehavioral factors for diseases. Unfortunately, only a relatively small volume\nof the textual eHealth data is processed and interpreted, an important factor\nbeing the difficulty in efficiently performing Big Data operations. In the\nmedical field, detecting domain-specific multi-word terms is a crucial task as\nthey can define an entire concept with a few words. A term can be defined as a\nlinguistic structure or a concept, and it is composed of one or more words with\na specific meaning to a domain. All the terms of a domain create its\nterminology. This chapter offers a critical study of the current, most\nperformant solutions for analyzing unstructured (image and textual) eHealth\ndata. This study also provides a comparison of the current Natural Language\nProcessing and Deep Learning techniques in the eHealth context. Finally, we\nexamine and discuss some of the current issues, and we define a set of research\ndirections in this area.\n", "date": "2024-01-19", "categories": []}
{"id": "2401.09717", "title": "Parameter Selection for Analyzing Conversations with Autism Spectrum\n  Disorder", "abstract": "  The diagnosis of autism spectrum disorder (ASD) is a complex, challenging\ntask as it depends on the analysis of interactional behaviors by psychologists\nrather than the use of biochemical diagnostics. In this paper, we present a\nmodeling approach to ASD diagnosis by analyzing acoustic/prosodic and\nlinguistic features extracted from diagnostic conversations between a\npsychologist and children who either are typically developing (TD) or have ASD.\nWe compare the contributions of different features across a range of\nconversation tasks. We focus on finding a minimal set of parameters that\ncharacterize conversational behaviors of children with ASD. Because ASD is\ndiagnosed through conversational interaction, in addition to analyzing the\nbehavior of the children, we also investigate whether the psychologist's\nconversational behaviors vary across diagnostic groups. Our results can\nfacilitate fine-grained analysis of conversation data for children with ASD to\nsupport diagnosis and intervention.\n", "date": "2024-01-18", "categories": []}
{"id": "2306.10070", "title": "Opportunities and Challenges for ChatGPT and Large Language Models in\n  Biomedicine and Health", "abstract": "  ChatGPT has drawn considerable attention from both the general public and\ndomain experts with its remarkable text generation capabilities. This has\nsubsequently led to the emergence of diverse applications in the field of\nbiomedicine and health. In this work, we examine the diverse applications of\nlarge language models (LLMs), such as ChatGPT, in biomedicine and health.\nSpecifically we explore the areas of biomedical information retrieval, question\nanswering, medical text summarization, information extraction, and medical\neducation, and investigate whether LLMs possess the transformative power to\nrevolutionize these tasks or whether the distinct complexities of biomedical\ndomain presents unique challenges. Following an extensive literature survey, we\nfind that significant advances have been made in the field of text generation\ntasks, surpassing the previous state-of-the-art methods. For other\napplications, the advances have been modest. Overall, LLMs have not yet\nrevolutionized biomedicine, but recent rapid progress indicates that such\nmethods hold great potential to provide valuable means for accelerating\ndiscovery and improving health. We also find that the use of LLMs, like\nChatGPT, in the fields of biomedicine and health entails various risks and\nchallenges, including fabricated information in its generated responses, as\nwell as legal and privacy concerns associated with sensitive patient data. We\nbelieve this survey can provide a comprehensive and timely overview to\nbiomedical researchers and healthcare practitioners on the opportunities and\nchallenges associated with using ChatGPT and other LLMs for transforming\nbiomedicine and health.\n", "date": "2023-06-15", "categories": []}
{"id": "2310.00100", "title": "Multilingual Natural Language Processing Model for Radiology Reports --\n  The Summary is all you need!", "abstract": "  The impression section of a radiology report summarizes important radiology\nfindings and plays a critical role in communicating these findings to\nphysicians. However, the preparation of these summaries is time-consuming and\nerror-prone for radiologists. Recently, numerous models for radiology report\nsummarization have been developed. Nevertheless, there is currently no model\nthat can summarize these reports in multiple languages. Such a model could\ngreatly improve future research and the development of Deep Learning models\nthat incorporate data from patients with different ethnic backgrounds. In this\nstudy, the generation of radiology impressions in different languages was\nautomated by fine-tuning a model, publicly available, based on a multilingual\ntext-to-text Transformer to summarize findings available in English,\nPortuguese, and German radiology reports. In a blind test, two board-certified\nradiologists indicated that for at least 70% of the system-generated summaries,\nthe quality matched or exceeded the corresponding human-written summaries,\nsuggesting substantial clinical reliability. Furthermore, this study showed\nthat the multilingual model outperformed other models that specialized in\nsummarizing radiology reports in only one language, as well as models that were\nnot specifically designed for summarizing radiology reports, such as ChatGPT.\n", "date": "2023-09-29", "categories": []}
{"id": "2401.06791", "title": "A Span-based Model for Extracting Overlapping PICO Entities from RCT\n  Publications", "abstract": "  Objectives Extraction of PICO (Populations, Interventions, Comparison, and\nOutcomes) entities is fundamental to evidence retrieval. We present a novel\nmethod PICOX to extract overlapping PICO entities.\n  Materials and Methods PICOX first identifies entities by assessing whether a\nword marks the beginning or conclusion of an entity. Then it uses a multi-label\nclassifier to assign one or more PICO labels to a span candidate. PICOX was\nevaluated using one of the best-performing baselines, EBM-NLP, and three more\ndatasets, i.e., PICO-Corpus, and RCT publications on Alzheimer's Disease or\nCOVID-19, using entity-level precision, recall, and F1 scores.\n  Results PICOX achieved superior precision, recall, and F1 scores across the\nboard, with the micro F1 score improving from 45.05 to 50.87 (p << 0.01). On\nthe PICO-Corpus, PICOX obtained higher recall and F1 scores than the baseline\nand improved the micro recall score from 56.66 to 67.33. On the COVID-19\ndataset, PICOX also outperformed the baseline and improved the micro F1 score\nfrom 77.10 to 80.32. On the AD dataset, PICOX demonstrated comparable F1 scores\nwith higher precision when compared to the baseline.\n  Conclusion PICOX excels in identifying overlapping entities and consistently\nsurpasses a leading baseline across multiple datasets. Ablation studies reveal\nthat its data augmentation strategy effectively minimizes false positives and\nimproves precision.\n", "date": "2024-01-08", "categories": []}
{"id": "2308.12604", "title": "PromptMRG: Diagnosis-Driven Prompts for Medical Report Generation", "abstract": "  Automatic medical report generation (MRG) is of great research value as it\nhas the potential to relieve radiologists from the heavy burden of report\nwriting. Despite recent advancements, accurate MRG remains challenging due to\nthe need for precise clinical understanding and disease identification.\nMoreover, the imbalanced distribution of diseases makes the challenge even more\npronounced, as rare diseases are underrepresented in training data, making\ntheir diagnostic performance unreliable. To address these challenges, we\npropose diagnosis-driven prompts for medical report generation (PromptMRG), a\nnovel framework that aims to improve the diagnostic accuracy of MRG with the\nguidance of diagnosis-aware prompts. Specifically, PromptMRG is based on\nencoder-decoder architecture with an extra disease classification branch. When\ngenerating reports, the diagnostic results from the classification branch are\nconverted into token prompts to explicitly guide the generation process. To\nfurther improve the diagnostic accuracy, we design cross-modal feature\nenhancement, which retrieves similar reports from the database to assist the\ndiagnosis of a query image by leveraging the knowledge from a pre-trained CLIP.\nMoreover, the disease imbalanced issue is addressed by applying an adaptive\nlogit-adjusted loss to the classification branch based on the individual\nlearning status of each disease, which overcomes the barrier of text decoder's\ninability to manipulate disease distributions. Experiments on two MRG\nbenchmarks show the effectiveness of the proposed method, where it obtains\nstate-of-the-art clinical efficacy performance on both datasets. The code is\navailable at https://github.com/jhb86253817/PromptMRG.\n", "date": "2023-08-24", "categories": []}
{"id": "2310.12489", "title": "MedAI Dialog Corpus (MEDIC): Zero-Shot Classification of Doctor and AI\n  Responses in Health Consultations", "abstract": "  Zero-shot classification enables text to be classified into classes not seen\nduring training. In this study, we examine the efficacy of zero-shot learning\nmodels in classifying healthcare consultation responses from Doctors and AI\nsystems. The models evaluated include BART, BERT, XLM, XLM-R and DistilBERT.\nThe models were tested on three different datasets based on a binary and\nmulti-label analysis to identify the origins of text in health consultations\nwithout any prior corpus training. According to our findings, the zero-shot\nlanguage models show a good understanding of language generally, but has\nlimitations when trying to classify doctor and AI responses to healthcare\nconsultations. This research provides a foundation for future research in the\nfield of medical text classification by informing the development of more\naccurate methods of classifying text written by Doctors and AI systems in\nhealth consultations.\n", "date": "2023-10-19", "categories": []}
{"id": "2401.06088", "title": "Autocompletion of Chief Complaints in the Electronic Health Records\n  using Large Language Models", "abstract": "  The Chief Complaint (CC) is a crucial component of a patient's medical record\nas it describes the main reason or concern for seeking medical care. It\nprovides critical information for healthcare providers to make informed\ndecisions about patient care. However, documenting CCs can be time-consuming\nfor healthcare providers, especially in busy emergency departments. To address\nthis issue, an autocompletion tool that suggests accurate and well-formatted\nphrases or sentences for clinical notes can be a valuable resource for triage\nnurses. In this study, we utilized text generation techniques to develop\nmachine learning models using CC data. In our proposed work, we train a Long\nShort-Term Memory (LSTM) model and fine-tune three different variants of\nBiomedical Generative Pretrained Transformers (BioGPT), namely\nmicrosoft/biogpt, microsoft/BioGPT-Large, and microsoft/BioGPT-Large-PubMedQA.\nAdditionally, we tune a prompt by incorporating exemplar CC sentences,\nutilizing the OpenAI API of GPT-4. We evaluate the models' performance based on\nthe perplexity score, modified BERTScore, and cosine similarity score. The\nresults show that BioGPT-Large exhibits superior performance compared to the\nother models. It consistently achieves a remarkably low perplexity score of\n1.65 when generating CC, whereas the baseline LSTM model achieves the best\nperplexity score of 170. Further, we evaluate and assess the proposed models'\nperformance and the outcome of GPT-4.0. Our study demonstrates that utilizing\nLLMs such as BioGPT, leads to the development of an effective autocompletion\ntool for generating CC documentation in healthcare settings.\n", "date": "2024-01-11", "categories": []}
{"id": "2401.05134", "title": "Yes, this is what I was looking for! Towards Multi-modal Medical\n  Consultation Concern Summary Generation", "abstract": "  Over the past few years, the use of the Internet for healthcare-related tasks\nhas grown by leaps and bounds, posing a challenge in effectively managing and\nprocessing information to ensure its efficient utilization. During moments of\nemotional turmoil and psychological challenges, we frequently turn to the\ninternet as our initial source of support, choosing this over discussing our\nfeelings with others due to the associated social stigma. In this paper, we\npropose a new task of multi-modal medical concern summary (MMCS) generation,\nwhich provides a short and precise summary of patients' major concerns brought\nup during the consultation. Nonverbal cues, such as patients' gestures and\nfacial expressions, aid in accurately identifying patients' concerns. Doctors\nalso consider patients' personal information, such as age and gender, in order\nto describe the medical condition appropriately. Motivated by the potential\nefficacy of patients' personal context and visual gestures, we propose a\ntransformer-based multi-task, multi-modal intent-recognition, and medical\nconcern summary generation (IR-MMCSG) system. Furthermore, we propose a\nmultitasking framework for intent recognition and medical concern summary\ngeneration for doctor-patient consultations. We construct the first multi-modal\nmedical concern summary generation (MM-MediConSummation) corpus, which includes\npatient-doctor consultations annotated with medical concern summaries, intents,\npatient personal information, doctor's recommendations, and keywords. Our\nexperiments and analysis demonstrate (a) the significant role of patients'\nexpressions/gestures and their personal information in intent identification\nand medical concern summary generation, and (b) the strong correlation between\nintent recognition and patients' medical concern summary generation\n  The dataset and source code are available at https://github.com/NLP-RL/MMCSG.\n", "date": "2024-01-10", "categories": []}
{"id": "2401.04367", "title": "Probabilistic emotion and sentiment modelling of patient-reported\n  experiences", "abstract": "  This study introduces a novel methodology for modelling patient emotions from\nonline patient experience narratives. We employed metadata network topic\nmodelling to analyse patient-reported experiences from Care Opinion, revealing\nkey emotional themes linked to patient-caregiver interactions and clinical\noutcomes. We develop a probabilistic, context-specific emotion recommender\nsystem capable of predicting both multilabel emotions and binary sentiments\nusing a naive Bayes classifier using contextually meaningful topics as\npredictors. The superior performance of our predicted emotions under this model\ncompared to baseline models was assessed using the information retrieval\nmetrics nDCG and Q-measure, and our predicted sentiments achieved an F1 score\nof 0.921, significantly outperforming standard sentiment lexicons. This method\noffers a transparent, cost-effective way to understand patient feedback,\nenhancing traditional collection methods and informing individualised patient\ncare. Our findings are accessible via an R package and interactive dashboard,\nproviding valuable tools for healthcare researchers and practitioners.\n", "date": "2024-01-09", "categories": []}
{"id": "2311.13273", "title": "Comparative Experimentation of Accuracy Metrics in Automated Medical\n  Reporting: The Case of Otitis Consultations", "abstract": "  Generative Artificial Intelligence (AI) can be used to automatically generate\nmedical reports based on transcripts of medical consultations. The aim is to\nreduce the administrative burden that healthcare professionals face. The\naccuracy of the generated reports needs to be established to ensure their\ncorrectness and usefulness. There are several metrics for measuring the\naccuracy of AI generated reports, but little work has been done towards the\napplication of these metrics in medical reporting. A comparative\nexperimentation of 10 accuracy metrics has been performed on AI generated\nmedical reports against their corresponding General Practitioner's (GP) medical\nreports concerning Otitis consultations. The number of missing, incorrect, and\nadditional statements of the generated reports have been correlated with the\nmetric scores. In addition, we introduce and define a Composite Accuracy Score\nwhich produces a single score for comparing the metrics within the field of\nautomated medical reporting. Findings show that based on the correlation study\nand the Composite Accuracy Score, the ROUGE-L and Word Mover's Distance metrics\nare the preferred metrics, which is not in line with previous work. These\nfindings help determine the accuracy of an AI generated medical report, which\naids the development of systems that generate medical reports for GPs to reduce\nthe administrative burden.\n", "date": "2023-11-22", "categories": []}
{"id": "2401.01943", "title": "Generalist embedding models are better at short-context clinical\n  semantic search than specialized embedding models", "abstract": "  The increasing use of tools and solutions based on Large Language Models\n(LLMs) for various tasks in the medical domain has become a prominent trend.\nTheir use in this highly critical and sensitive domain has thus raised\nimportant questions about their robustness, especially in response to\nvariations in input, and the reliability of the generated outputs. This study\naddresses these questions by constructing a textual dataset based on the\nICD-10-CM code descriptions, widely used in US hospitals and containing many\nclinical terms, and their easily reproducible rephrasing. We then benchmarked\nexisting embedding models, either generalist or specialized in the clinical\ndomain, in a semantic search task where the goal was to correctly match the\nrephrased text to the original description. Our results showed that generalist\nmodels performed better than clinical models, suggesting that existing clinical\nspecialized models are more sensitive to small changes in input that confuse\nthem. The highlighted problem of specialized models may be due to the fact that\nthey have not been trained on sufficient data, and in particular on datasets\nthat are not diverse enough to have a reliable global language understanding,\nwhich is still necessary for accurate handling of medical documents.\n", "date": "2024-01-03", "categories": []}
{"id": "2401.02158", "title": "Shayona@SMM4H23: COVID-19 Self diagnosis classification using BERT and\n  LightGBM models", "abstract": "  This paper describes approaches and results for shared Task 1 and 4 of\nSMMH4-23 by Team Shayona. Shared Task-1 was binary classification of english\ntweets self-reporting a COVID-19 diagnosis, and Shared Task-4 was Binary\nclassification of English Reddit posts self-reporting a social anxiety disorder\ndiagnosis. Our team has achieved the highest f1-score 0.94 in Task-1 among all\nparticipants. We have leveraged the Transformer model (BERT) in combination\nwith the LightGBM model for both tasks.\n", "date": "2024-01-04", "categories": []}
{"id": "2305.11348", "title": "In the Name of Fairness: Assessing the Bias in Clinical Record\n  De-identification", "abstract": "  Data sharing is crucial for open science and reproducible research, but the\nlegal sharing of clinical data requires the removal of protected health\ninformation from electronic health records. This process, known as\nde-identification, is often achieved through the use of machine learning\nalgorithms by many commercial and open-source systems. While these systems have\nshown compelling results on average, the variation in their performance across\ndifferent demographic groups has not been thoroughly examined. In this work, we\ninvestigate the bias of de-identification systems on names in clinical notes\nvia a large-scale empirical analysis. To achieve this, we create 16 name sets\nthat vary along four demographic dimensions: gender, race, name popularity, and\nthe decade of popularity. We insert these names into 100 manually curated\nclinical templates and evaluate the performance of nine public and private\nde-identification methods. Our findings reveal that there are statistically\nsignificant performance gaps along a majority of the demographic dimensions in\nmost methods. We further illustrate that de-identification quality is affected\nby polysemy in names, gender context, and clinical note characteristics. To\nmitigate the identified gaps, we propose a simple and method-agnostic solution\nby fine-tuning de-identification methods with clinical context and diverse\nnames. Overall, it is imperative to address the bias in existing methods\nimmediately so that downstream stakeholders can build high-quality systems to\nserve all demographic parties fairly.\n", "date": "2023-05-18", "categories": []}
{"id": "2401.01596", "title": "MedSumm: A Multimodal Approach to Summarizing Code-Mixed Hindi-English\n  Clinical Queries", "abstract": "  In the healthcare domain, summarizing medical questions posed by patients is\ncritical for improving doctor-patient interactions and medical decision-making.\nAlthough medical data has grown in complexity and quantity, the current body of\nresearch in this domain has primarily concentrated on text-based methods,\noverlooking the integration of visual cues. Also prior works in the area of\nmedical question summarisation have been limited to the English language. This\nwork introduces the task of multimodal medical question summarization for\ncodemixed input in a low-resource setting. To address this gap, we introduce\nthe Multimodal Medical Codemixed Question Summarization MMCQS dataset, which\ncombines Hindi-English codemixed medical queries with visual aids. This\nintegration enriches the representation of a patient's medical condition,\nproviding a more comprehensive perspective. We also propose a framework named\nMedSumm that leverages the power of LLMs and VLMs for this task. By utilizing\nour MMCQS dataset, we demonstrate the value of integrating visual information\nfrom images to improve the creation of medically detailed summaries. This\nmultimodal strategy not only improves healthcare decision-making but also\npromotes a deeper comprehension of patient queries, paving the way for future\nexploration in personalized and responsive medical care. Our dataset, code, and\npre-trained models will be made publicly available.\n", "date": "2024-01-03", "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": "2312.17475", "title": "EHR Interaction Between Patients and AI: NoteAid EHR Interaction", "abstract": "  With the rapid advancement of Large Language Models (LLMs) and their\noutstanding performance in semantic and contextual comprehension, the potential\nof LLMs in specialized domains warrants exploration. This paper introduces the\nNoteAid EHR Interaction Pipeline, an innovative approach developed using\ngenerative LLMs to assist in patient education, a task stemming from the need\nto aid patients in understanding Electronic Health Records (EHRs). Building\nupon the NoteAid work, we designed two novel tasks from the patient's\nperspective: providing explanations for EHR content that patients may not\nunderstand and answering questions posed by patients after reading their EHRs.\nWe extracted datasets containing 10,000 instances from MIMIC Discharge\nSummaries and 876 instances from the MADE medical notes collection,\nrespectively, executing the two tasks through the NoteAid EHR Interaction\nPipeline with these data. Performance data of LLMs on these tasks were\ncollected and constructed as the corresponding NoteAid EHR Interaction Dataset.\nThrough a comprehensive evaluation of the entire dataset using LLM assessment\nand a rigorous manual evaluation of 64 instances, we showcase the potential of\nLLMs in patient education. Besides, the results provide valuable data support\nfor future exploration and applications in this domain while also supplying\nhigh-quality synthetic datasets for in-house system training.\n", "date": "2023-12-29", "categories": []}
{"id": "2312.17522", "title": "Overview of the PromptCBLUE Shared Task in CHIP2023", "abstract": "  This paper presents an overview of the PromptCBLUE shared task\n(http://cips-chip.org.cn/2023/eval1) held in the CHIP-2023 Conference. This\nshared task reformualtes the CBLUE benchmark, and provide a good testbed for\nChinese open-domain or medical-domain large language models (LLMs) in general\nmedical natural language processing. Two different tracks are held: (a) prompt\ntuning track, investigating the multitask prompt tuning of LLMs, (b) probing\nthe in-context learning capabilities of open-sourced LLMs. Many teams from both\nthe industry and academia participated in the shared tasks, and the top teams\nachieved amazing test results. This paper describes the tasks, the datasets,\nevaluation metrics, and the top systems for both tasks. Finally, the paper\nsummarizes the techniques and results of the evaluation of the various\napproaches explored by the participating teams.\n", "date": "2023-12-29", "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": "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": "2312.14184", "title": "Large Language Models in Medical Term Classification and Unexpected\n  Misalignment Between Response and Reasoning", "abstract": "  This study assesses the ability of state-of-the-art large language models\n(LLMs) including GPT-3.5, GPT-4, Falcon, and LLaMA 2 to identify patients with\nmild cognitive impairment (MCI) from discharge summaries and examines instances\nwhere the models' responses were misaligned with their reasoning. Utilizing the\nMIMIC-IV v2.2 database, we focused on a cohort aged 65 and older, verifying MCI\ndiagnoses against ICD codes and expert evaluations. The data was partitioned\ninto training, validation, and testing sets in a 7:2:1 ratio for model\nfine-tuning and evaluation, with an additional metastatic cancer dataset from\nMIMIC III used to further assess reasoning consistency. GPT-4 demonstrated\nsuperior interpretative capabilities, particularly in response to complex\nprompts, yet displayed notable response-reasoning inconsistencies. In contrast,\nopen-source models like Falcon and LLaMA 2 achieved high accuracy but lacked\nexplanatory reasoning, underscoring the necessity for further research to\noptimize both performance and interpretability. The study emphasizes the\nsignificance of prompt engineering and the need for further exploration into\nthe unexpected reasoning-response misalignment observed in GPT-4. The results\nunderscore the promise of incorporating LLMs into healthcare diagnostics,\ncontingent upon methodological advancements to ensure accuracy and clinical\ncoherence of AI-generated outputs, thereby improving the trustworthiness of\nLLMs for medical decision-making.\n", "date": "2023-12-19", "categories": []}
{"id": "2311.18260", "title": "Consensus, dissensus and synergy between clinicians and specialist\n  foundation models in radiology report generation", "abstract": "  Radiology reports are an instrumental part of modern medicine, informing key\nclinical decisions such as diagnosis and treatment. The worldwide shortage of\nradiologists, however, restricts access to expert care and imposes heavy\nworkloads, contributing to avoidable errors and delays in report delivery.\nWhile recent progress in automated report generation with vision-language\nmodels offer clear potential in ameliorating the situation, the path to\nreal-world adoption has been stymied by the challenge of evaluating the\nclinical quality of AI-generated reports. In this study, we build a\nstate-of-the-art report generation system for chest radiographs,\n$\\textit{Flamingo-CXR}$, by fine-tuning a well-known vision-language foundation\nmodel on radiology data. To evaluate the quality of the AI-generated reports, a\ngroup of 16 certified radiologists provide detailed evaluations of AI-generated\nand human written reports for chest X-rays from an intensive care setting in\nthe United States and an inpatient setting in India. At least one radiologist\n(out of two per case) preferred the AI report to the ground truth report in\nover 60$\\%$ of cases for both datasets. Amongst the subset of AI-generated\nreports that contain errors, the most frequently cited reasons were related to\nthe location and finding, whereas for human written reports, most mistakes were\nrelated to severity and finding. This disparity suggested potential\ncomplementarity between our AI system and human experts, prompting us to\ndevelop an assistive scenario in which Flamingo-CXR generates a first-draft\nreport, which is subsequently revised by a clinician. This is the first\ndemonstration of clinician-AI collaboration for report writing, and the\nresultant reports are assessed to be equivalent or preferred by at least one\nradiologist to reports written by experts alone in 80$\\%$ of in-patient cases\nand 60$\\%$ of intensive care cases.\n", "date": "2023-11-30", "categories": []}
{"id": "2312.12442", "title": "Hierarchical Classification System for Breast Cancer Specimen Report\n  (HCSBC) -- an end-to-end model for characterizing severity and diagnosis", "abstract": "  Automated classification of cancer pathology reports can extract information\nfrom unstructured reports and categorize each report into structured diagnosis\nand severity categories. Thus, such system can reduce the burden for populating\ntumor registries, help registration for clinical trial as well as developing\nlarge dataset for deep learning model development using true pathologic ground\ntruth. However, the content of breast pathology reports can be difficult for\ncategorize due to the high linguistic variability in content and wide variety\nof potential diagnoses >50. Existing NLP models are primarily focused on\ndeveloping classifier for primary breast cancer types (e.g. IDC, DCIS, ILC) and\ntumor characteristics, and ignore the rare diagnosis of cancer subtypes. We\nthen developed a hierarchical hybrid transformer-based pipeline (59 labels) -\nHierarchical Classification System for Breast Cancer Specimen Report (HCSBC),\nwhich utilizes the potential of the transformer context-preserving NLP\ntechnique and compared our model to several state of the art ML and DL models.\nWe trained the model on the EUH data and evaluated our model's performance on\ntwo external datasets - MGH and Mayo Clinic. We publicly release the code and a\nlive application under Huggingface spaces repository\n", "date": "2023-11-02", "categories": []}
{"id": "2312.10202", "title": "Low-resource classification of mobility functioning information in\n  clinical sentences using large language models", "abstract": "  Objective: Function is increasingly recognized as an important indicator of\nwhole-person health. This study evaluates the ability of publicly available\nlarge language models (LLMs) to accurately identify the presence of functioning\ninformation from clinical notes. We explore various strategies to improve the\nperformance on this task. Materials and Methods: We collect a balanced binary\nclassification dataset of 1000 sentences from the Mobility NER dataset, which\nwas curated from n2c2 clinical notes. For evaluation, we construct zero-shot\nand few-shot prompts to query the LLMs whether a given sentence contains\nmobility functioning information. Two sampling techniques, random sampling and\nk-nearest neighbor (kNN)-based sampling, are used to select the few-shot\nexamples. Furthermore, we apply a parameter-efficient prompt-based fine-tuning\nmethod to the LLMs and evaluate their performance under various training\nsettings. Results: Flan-T5-xxl outperforms all other models in both zero-shot\nand few-shot settings, achieving a F1 score of 0.865 with a single\ndemonstrative example selected by kNN sampling. In prompt-based fine-tuning\nexperiments, this foundation model also demonstrates superior performance\nacross all low-resource settings, particularly achieving an impressive F1 score\nof 0.922 using the full training dataset. The smaller model, Flan-T5-xl,\nrequires fine-tuning with only 2.3M additional parameters to achieve comparable\nperformance to the fully fine-tuned Gatortron-base model, both surpassing 0.9\nF1 score. Conclusion: Open-source instruction-tuned LLMs demonstrate impressive\nin-context learning capability in the mobility functioning classification task.\nThe performance of these models can be further improved by continuing\nfine-tuning on a task-specific dataset.\n", "date": "2023-12-15", "categories": []}
{"id": "2312.09451", "title": "MANTIS at #SMM4H 2023: Leveraging Hybrid and Ensemble Models for\n  Detection of Social Anxiety Disorder on Reddit", "abstract": "  This paper presents our system employed for the Social Media Mining for\nHealth 2023 Shared Task 4: Binary classification of English Reddit posts\nself-reporting a social anxiety disorder diagnosis. We systematically\ninvestigate and contrast the efficacy of hybrid and ensemble models that\nharness specialized medical domain-adapted transformers in conjunction with\nBiLSTM neural networks. The evaluation results outline that our best performing\nmodel obtained 89.31% F1 on the validation set and 83.76% F1 on the test set.\n", "date": "2023-11-28", "categories": []}
{"id": "2312.09469", "title": "Clinical Text Deduplication Practices for Efficient Pretraining and\n  Improved Clinical Tasks", "abstract": "  Despite being a unique source of information on patients' status and disease\nprogression, clinical notes are characterized by high levels of duplication and\ninformation redundancy. In general domain text, it has been shown that\ndeduplication does not harm language model (LM) pretraining, thus helping\nreduce the training cost. Although large LMs have proven to learn medical\nknowledge, they still require specialized domain adaptation for improved\ndownstream clinical tasks. By leveraging large real-world clinical corpora, we\nfirst provided a fine-grained characterization of duplicates stemming from\ncommon writing practices and clinical relevancy. Second, we demonstrated that\ndeduplicating clinical text can help clinical LMs encode less redundant\ninformation in a more efficient manner and do not harm classification tasks via\nprompt-based learning.\n", "date": "2023-09-29", "categories": []}
{"id": "2312.08495", "title": "Beyond Accuracy: Automated De-Identification of Large Real-World\n  Clinical Text Datasets", "abstract": "  Recent research advances achieve human-level accuracy for de-identifying\nfree-text clinical notes on research datasets, but gaps remain in reproducing\nthis in large real-world settings. This paper summarizes lessons learned from\nbuilding a system used to de-identify over one billion real clinical notes, in\na fully automated way, that was independently certified by multiple\norganizations for production use. A fully automated solution requires a very\nhigh level of accuracy that does not require manual review. A hybrid\ncontext-based model architecture is described, which outperforms a Named Entity\nRecogniton (NER) - only model by 10% on the i2b2-2014 benchmark. The proposed\nsystem makes 50%, 475%, and 575% fewer errors than the comparable AWS, Azure,\nand GCP services respectively while also outperforming ChatGPT by 33%. It\nexceeds 98% coverage of sensitive data across 7 European languages, without a\nneed for fine tuning. A second set of described models enable data obfuscation\n-- replacing sensitive data with random surrogates -- while retaining name,\ndate, gender, clinical, and format consistency. Both the practical need and the\nsolution architecture that provides for reliable & linked anonymized documents\nare described.\n", "date": "2023-12-13", "categories": []}
{"id": "2311.16588", "title": "Ascle: A Python Natural Language Processing Toolkit for Medical Text\n  Generation", "abstract": "  This study introduces Ascle, a pioneering natural language processing (NLP)\ntoolkit designed for medical text generation. Ascle is tailored for biomedical\nresearchers and healthcare professionals with an easy-to-use, all-in-one\nsolution that requires minimal programming expertise. For the first time, Ascle\nevaluates and provides interfaces for the latest pre-trained language models,\nencompassing four advanced and challenging generative functions:\nquestion-answering, text summarization, text simplification, and machine\ntranslation. In addition, Ascle integrates 12 essential NLP functions, along\nwith query and search capabilities for clinical databases. The toolkit, its\nmodels, and associated data are publicly available via\nhttps://github.com/Yale-LILY/MedGen.\n", "date": "2023-11-28", "categories": []}
{"id": "2312.05435", "title": "Enhancing Robustness of Foundation Model Representations under\n  Provenance-related Distribution Shifts", "abstract": "  Foundation models are a current focus of attention in both industry and\nacademia. While they have shown their capabilities in a variety of tasks,\nin-depth research is required to determine their robustness to distribution\nshift when used as a basis for supervised machine learning. This is especially\nimportant in the context of clinical data, with particular limitations related\nto data accessibility, lack of pretraining materials, and limited availability\nof high-quality annotations. In this work, we examine the stability of models\nbased on representations from foundation models under distribution shift. We\nfocus on confounding by provenance, a form of distribution shift that emerges\nin the context of multi-institutional datasets when there are differences in\nsource-specific language use and class distributions. Using a sampling strategy\nthat synthetically induces varying degrees of distribution shift, we evaluate\nthe extent to which representations from foundation models result in\npredictions that are inherently robust to confounding by provenance.\nAdditionally, we examine the effectiveness of a straightforward confounding\nadjustment method inspired by Pearl's conception of backdoor adjustment.\nResults indicate that while foundation models do show some out-of-the-box\nrobustness to confounding-by-provenance related distribution shifts, this can\nbe considerably improved through adjustment. These findings suggest a need for\ndeliberate adjustment of predictive models using representations from\nfoundation models in the context of source-specific distributional differences.\n", "date": "2023-12-09", "categories": []}
{"id": "2312.05585", "title": "Enhancing Medical Specialty Assignment to Patients using NLP Techniques", "abstract": "  The introduction of Large Language Models (LLMs), and the vast volume of\npublicly available medical data, amplified the application of NLP to the\nmedical domain. However, LLMs are pretrained on data that are not explicitly\nrelevant to the domain that are applied to and are often biased towards the\noriginal data they were pretrained upon. Even when pretrained on domainspecific\ndata, these models typically require time-consuming fine-tuning to achieve good\nperformance for a specific task. To address these limitations, we propose an\nalternative approach that achieves superior performance while being\ncomputationally efficient. Specifically, we utilize keywords to train a deep\nlearning architecture that outperforms a language model pretrained on a large\ncorpus of text. Our proposal does not require pretraining nor fine-tuning and\ncan be applied directly to a specific setting for performing multi-label\nclassification. Our objective is to automatically assign a new patient to the\nspecialty of the medical professional they require, using a dataset that\ncontains medical transcriptions and relevant keywords. To this end, we\nfine-tune the PubMedBERT model on this dataset, which serves as the baseline\nfor our experiments. We then twice train/fine-tune a DNN and the RoBERTa\nlanguage model, using both the keywords and the full transcriptions as input.\nWe compare the performance of these approaches using relevant metrics. Our\nresults demonstrate that utilizing keywords for text classification\nsignificantly improves classification performance, for both a basic DL\narchitecture and a large language model. Our approach represents a promising\nand efficient alternative to traditional methods for finetuning language models\non domain-specific data and has potential applications in various medical\ndomains\n", "date": "2023-12-09", "categories": []}
{"id": "2312.06099", "title": "Generative Large Language Models Are All-purpose Text Analytics Engines:\n  Text-to-text Learning Is All Your Need", "abstract": "  Objective To solve major clinical natural language processing (NLP) tasks\nusing a unified text-to-text learning architecture based on a generative large\nlanguage model (LLM) via prompt tuning. Methods We formulated 7 key clinical\nNLP tasks as text-to-text learning and solved them using one unified generative\nclinical LLM, GatorTronGPT, developed using GPT-3 architecture and trained with\nup to 20 billion parameters. We adopted soft prompts (i.e., trainable vectors)\nwith frozen LLM, where the LLM parameters were not updated (i.e., frozen) and\nonly the vectors of soft prompts were updated, known as prompt tuning. We added\nadditional soft prompts as a prefix to the input layer, which were optimized\nduring the prompt tuning. We evaluated the proposed method using 7 clinical NLP\ntasks and compared them with previous task-specific solutions based on\nTransformer models. Results and Conclusion The proposed approach achieved\nstate-of-the-art performance for 5 out of 7 major clinical NLP tasks using one\nunified generative LLM. Our approach outperformed previous task-specific\ntransformer models by ~3% for concept extraction and 7% for relation extraction\napplied to social determinants of health, 3.4% for clinical concept\nnormalization, 3.4~10% for clinical abbreviation disambiguation, and 5.5~9% for\nnatural language inference. Our approach also outperformed a previously\ndeveloped prompt-based machine reading comprehension (MRC) model,\nGatorTron-MRC, for clinical concept and relation extraction. The proposed\napproach can deliver the ``one model for all`` promise from training to\ndeployment using a unified generative LLM.\n", "date": "2023-12-11", "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": "2312.04906", "title": "Ophtha-LLaMA2: A Large Language Model for Ophthalmology", "abstract": "  In recent years, pre-trained large language models (LLMs) have achieved\ntremendous success in the field of Natural Language Processing (NLP). Prior\nstudies have primarily focused on general and generic domains, with relatively\nless research on specialized LLMs in the medical field. The specialization and\nhigh accuracy requirements for diagnosis in the medical field, as well as the\nchallenges in collecting large-scale data, have constrained the application and\ndevelopment of LLMs in medical scenarios. In the field of ophthalmology,\nclinical diagnosis mainly relies on doctors' interpretation of reports and\nmaking diagnostic decisions. In order to take advantage of LLMs to provide\ndecision support for doctors, we collected three modalities of ophthalmic\nreport data and fine-tuned the LLaMA2 model, successfully constructing an LLM\ntermed the \"Ophtha-LLaMA2\" specifically tailored for ophthalmic disease\ndiagnosis. Inference test results show that even with a smaller fine-tuning\ndataset, Ophtha-LLaMA2 performs significantly better in ophthalmic diagnosis\ncompared to other LLMs. It demonstrates that the Ophtha-LLaMA2 exhibits\nsatisfying accuracy and efficiency in ophthalmic disease diagnosis, making it a\nvaluable tool for ophthalmologists to provide improved diagnostic support for\npatients. This research provides a useful reference for the application of LLMs\nin the field of ophthalmology, while showcasing the immense potential and\nprospects in this domain.\n", "date": "2023-12-08", "categories": []}
{"id": "2312.03737", "title": "A Generic NLI approach for Classification of Sentiment Associated with\n  Therapies", "abstract": "  This paper describes our system for addressing SMM4H 2023 Shared Task 2 on\n\"Classification of sentiment associated with therapies (aspect-oriented)\". In\nour work, we adopt an approach based on Natural language inference (NLI) to\nformulate this task as a sentence pair classification problem, and train\ntransformer models to predict sentiment associated with a therapy on a given\ntext. Our best model achieved 75.22\\% F1-score which was 11\\% (4\\%) more than\nthe mean (median) score of all teams' submissions.\n", "date": "2023-11-28", "categories": []}
{"id": "2312.03747", "title": "Classifying patient voice in social media data using neural networks: A\n  comparison of AI models on different data sources and therapeutic domains", "abstract": "  It is essential that healthcare professionals and members of the healthcare\ncommunity can access and easily understand patient experiences in the real\nworld, so that care standards can be improved and driven towards personalised\ndrug treatment. Social media platforms and message boards are deemed suitable\nsources of patient experience information, as patients have been observed to\ndiscuss and exchange knowledge, look for and provide support online. This paper\ntests the hypothesis that not all online patient experience information can be\ntreated and collected in the same way, as a result of the inherent differences\nin the way individuals talk about their journeys, in different therapeutic\ndomains and or data sources.\n  We used linguistic analysis to understand and identify similarities between\ndatasets, across patient language, between data sources (Reddit, SocialGist)\nand therapeutic domains (cardiovascular, oncology, immunology, neurology). We\ndetected common vocabulary used by patients in the same therapeutic domain\nacross data sources, except for immunology patients, who use unique vocabulary\nbetween the two data sources, and compared to all other datasets. We combined\nlinguistically similar datasets to train classifiers (CNN, transformer) to\naccurately identify patient experience posts from social media, a task we refer\nto as patient voice classification. The cardiovascular and neurology\ntransformer classifiers perform the best in their respective comparisons for\nthe Reddit data source, achieving F1-scores of 0.865 and 1.0 respectively. The\noverall best performing classifier is the transformer classifier trained on all\ndata collected for this experiment, achieving F1-scores ranging between 0.863\nand 0.995 across all therapeutic domain and data source specific test datasets.\n", "date": "2023-11-30", "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": "2312.03077", "title": "Clinical Notes Reveal Physician Fatigue", "abstract": "  Physicians write notes about patients. In doing so, they reveal much about\nthemselves. Using data from 129,228 emergency room visits, we train a model to\nidentify notes written by fatigued physicians -- those who worked 5 or more of\nthe prior 7 days. In a hold-out set, the model accurately identifies notes\nwritten by these high-workload physicians, and also flags notes written in\nother high-fatigue settings: on overnight shifts, and after high patient\nvolumes. Model predictions also correlate with worse decision-making on at\nleast one important metric: yield of testing for heart attack is 18% lower with\neach standard deviation increase in model-predicted fatigue. Finally, the model\nindicates that notes written about Black and Hispanic patients have 12% and 21%\nhigher predicted fatigue than Whites -- larger than overnight vs. daytime\ndifferences. These results have an important implication for large language\nmodels (LLMs). Our model indicates that fatigued doctors write more predictable\nnotes. Perhaps unsurprisingly, because word prediction is the core of how LLMs\nwork, we find that LLM-written notes have 17% higher predicted fatigue than\nreal physicians' notes. This indicates that LLMs may introduce distortions in\ngenerated text that are not yet fully understood.\n", "date": "2023-12-05", "categories": []}
{"id": "2312.03567", "title": "XAIQA: Explainer-Based Data Augmentation for Extractive Question\n  Answering", "abstract": "  Extractive question answering (QA) systems can enable physicians and\nresearchers to query medical records, a foundational capability for designing\nclinical studies and understanding patient medical history. However, building\nthese systems typically requires expert-annotated QA pairs. Large language\nmodels (LLMs), which can perform extractive QA, depend on high quality data in\ntheir prompts, specialized for the application domain. We introduce a novel\napproach, XAIQA, for generating synthetic QA pairs at scale from data naturally\navailable in electronic health records. Our method uses the idea of a\nclassification model explainer to generate questions and answers about medical\nconcepts corresponding to medical codes. In an expert evaluation with two\nphysicians, our method identifies $2.2\\times$ more semantic matches and\n$3.8\\times$ more clinical abbreviations than two popular approaches that use\nsentence transformers to create QA pairs. In an ML evaluation, adding our QA\npairs improves performance of GPT-4 as an extractive QA model, including on\ndifficult questions. In both the expert and ML evaluations, we examine\ntrade-offs between our method and sentence transformers for QA pair generation\ndepending on question difficulty.\n", "date": "2023-12-06", "categories": []}
{"id": "2312.02296", "title": "LLMs Accelerate Annotation for Medical Information Extraction", "abstract": "  The unstructured nature of clinical notes within electronic health records\noften conceals vital patient-related information, making it challenging to\naccess or interpret. To uncover this hidden information, specialized Natural\nLanguage Processing (NLP) models are required. However, training these models\nnecessitates large amounts of labeled data, a process that is both\ntime-consuming and costly when relying solely on human experts for annotation.\nIn this paper, we propose an approach that combines Large Language Models\n(LLMs) with human expertise to create an efficient method for generating ground\ntruth labels for medical text annotation. By utilizing LLMs in conjunction with\nhuman annotators, we significantly reduce the human annotation burden, enabling\nthe rapid creation of labeled datasets. We rigorously evaluate our method on a\nmedical information extraction task, demonstrating that our approach not only\nsubstantially cuts down on human intervention but also maintains high accuracy.\nThe results highlight the potential of using LLMs to improve the utilization of\nunstructured clinical data, allowing for the swift deployment of tailored NLP\nsolutions in healthcare.\n", "date": "2023-12-04", "categories": []}
{"id": "2312.02314", "title": "Fine-tuning pre-trained extractive QA models for clinical document\n  parsing", "abstract": "  Electronic health records (EHRs) contain a vast amount of high-dimensional\nmulti-modal data that can accurately represent a patient's medical history.\nUnfortunately, most of this data is either unstructured or semi-structured,\nrendering it unsuitable for real-time and retrospective analyses. A remote\npatient monitoring (RPM) program for Heart Failure (HF) patients needs to have\naccess to clinical markers like EF (Ejection Fraction) or LVEF (Left\nVentricular Ejection Fraction) in order to ascertain eligibility and\nappropriateness for the program. This paper explains a system that can parse\nechocardiogram reports and verify EF values. This system helps identify\neligible HF patients who can be enrolled in such a program. At the heart of\nthis system is a pre-trained extractive QA transformer model that is fine-tuned\non custom-labeled data. The methods used to prepare such a model for deployment\nare illustrated by running experiments on a public clinical dataset like\nMIMIC-IV-Note. The pipeline can be used to generalize solutions to similar\nproblems in a low-resource setting. We found that the system saved over 1500\nhours for our clinicians over 12 months by automating the task at scale.\n", "date": "2023-12-04", "categories": []}
{"id": "2312.00804", "title": "Automatic detection of problem-gambling signs from online texts using\n  large language models", "abstract": "  Problem gambling is a major public health concern and is associated with\nprofound psychological distress and economic problems. There are numerous\ngambling communities on the internet where users exchange information about\ngames, gambling tactics, as well as gambling-related problems. Individuals\nexhibiting higher levels of problem gambling engage more in such communities.\nOnline gambling communities may provide insights into problem-gambling\nbehaviour. Using data scraped from a major German gambling discussion board, we\nfine-tuned a large language model, specifically a Bidirectional Encoder\nRepresentations from Transformers (BERT) model, to predict signs of\nproblem-gambling from forum posts. Training data were generated by manual\nannotation and by taking into account diagnostic criteria and gambling-related\ncognitive distortions. Using k-fold cross-validation, our models achieved a\nprecision of 0.95 and F1 score of 0.71, demonstrating that satisfactory\nclassification performance can be achieved by generating high-quality training\nmaterial through manual annotation based on diagnostic criteria. The current\nstudy confirms that a BERT-based model can be reliably used on small data sets\nand to detect signatures of problem gambling in online communication data. Such\ncomputational approaches may have potential for the detection of changes in\nproblem-gambling prevalence among online users.\n", "date": "2023-11-24", "categories": []}
{"id": "2310.17703", "title": "The impact of responding to patient messages with large language model\n  assistance", "abstract": "  Documentation burden is a major contributor to clinician burnout, which is\nrising nationally and is an urgent threat to our ability to care for patients.\nArtificial intelligence (AI) chatbots, such as ChatGPT, could reduce clinician\nburden by assisting with documentation. Although many hospitals are actively\nintegrating such systems into electronic medical record systems, AI chatbots\nutility and impact on clinical decision-making have not been studied for this\nintended use. We are the first to examine the utility of large language models\nin assisting clinicians draft responses to patient questions. In our two-stage\ncross-sectional study, 6 oncologists responded to 100 realistic synthetic\ncancer patient scenarios and portal messages developed to reflect common\nmedical situations, first manually, then with AI assistance.\n  We find AI-assisted responses were longer, less readable, but provided\nacceptable drafts without edits 58% of time. AI assistance improved efficiency\n77% of time, with low harm risk (82% safe). However, 7.7% unedited AI responses\ncould severely harm. In 31% cases, physicians thought AI drafts were\nhuman-written. AI assistance led to more patient education recommendations,\nfewer clinical actions than manual responses. Results show promise for AI to\nimprove clinician efficiency and patient care through assisting documentation,\nif used judiciously. Monitoring model outputs and human-AI interaction remains\ncrucial for safe implementation.\n", "date": "2023-10-26", "categories": []}
{"id": "2311.16650", "title": "Text2Tree: Aligning Text Representation to the Label Tree Hierarchy for\n  Imbalanced Medical Classification", "abstract": "  Deep learning approaches exhibit promising performances on various text\ntasks. However, they are still struggling on medical text classification since\nsamples are often extremely imbalanced and scarce. Different from existing\nmainstream approaches that focus on supplementary semantics with external\nmedical information, this paper aims to rethink the data challenges in medical\ntexts and present a novel framework-agnostic algorithm called Text2Tree that\nonly utilizes internal label hierarchy in training deep learning models. We\nembed the ICD code tree structure of labels into cascade attention modules for\nlearning hierarchy-aware label representations. Two new learning schemes,\nSimilarity Surrogate Learning (SSL) and Dissimilarity Mixup Learning (DML), are\ndevised to boost text classification by reusing and distinguishing samples of\nother labels following the label representation hierarchy, respectively.\nExperiments on authoritative public datasets and real-world medical records\nshow that our approach stably achieves superior performances over classical and\nadvanced imbalanced classification methods.\n", "date": "2023-11-28", "categories": []}
{"id": "2311.15946", "title": "Leveraging deep active learning to identify low-resource mobility\n  functioning information in public clinical notes", "abstract": "  Function is increasingly recognized as an important indicator of whole-person\nhealth, although it receives little attention in clinical natural language\nprocessing research. We introduce the first public annotated dataset\nspecifically on the Mobility domain of the International Classification of\nFunctioning, Disability and Health (ICF), aiming to facilitate automatic\nextraction and analysis of functioning information from free-text clinical\nnotes. We utilize the National NLP Clinical Challenges (n2c2) research dataset\nto construct a pool of candidate sentences using keyword expansion. Our active\nlearning approach, using query-by-committee sampling weighted by density\nrepresentativeness, selects informative sentences for human annotation. We\ntrain BERT and CRF models, and use predictions from these models to guide the\nselection of new sentences for subsequent annotation iterations. Our final\ndataset consists of 4,265 sentences with a total of 11,784 entities, including\n5,511 Action entities, 5,328 Mobility entities, 306 Assistance entities, and\n639 Quantification entities. The inter-annotator agreement (IAA), averaged over\nall entity types, is 0.72 for exact matching and 0.91 for partial matching. We\nalso train and evaluate common BERT models and state-of-the-art Nested NER\nmodels. The best F1 scores are 0.84 for Action, 0.7 for Mobility, 0.62 for\nAssistance, and 0.71 for Quantification. Empirical results demonstrate\npromising potential of NER models to accurately extract mobility functioning\ninformation from clinical text. The public availability of our annotated\ndataset will facilitate further research to comprehensively capture functioning\ninformation in electronic health records (EHRs).\n", "date": "2023-11-27", "categories": []}
{"id": "2309.17169", "title": "An evaluation of GPT models for phenotype concept recognition", "abstract": "  Objective: Clinical deep phenotyping and phenotype annotation play a critical\nrole in both the diagnosis of patients with rare disorders as well as in\nbuilding computationally-tractable knowledge in the rare disorders field. These\nprocesses rely on using ontology concepts, often from the Human Phenotype\nOntology, in conjunction with a phenotype concept recognition task (supported\nusually by machine learning methods) to curate patient profiles or existing\nscientific literature. With the significant shift in the use of large language\nmodels (LLMs) for most NLP tasks, we examine the performance of the latest\nGenerative Pre-trained Transformer (GPT) models underpinning ChatGPT as a\nfoundation for the tasks of clinical phenotyping and phenotype annotation.\nMaterials and Methods: The experimental setup of the study included seven\nprompts of various levels of specificity, two GPT models (gpt-3.5-turbo and\ngpt-4.0) and two established gold standard corpora for phenotype recognition,\none consisting of publication abstracts and the other clinical observations.\nResults: Our results show that, with an appropriate setup, these models can\nachieve state of the art performance. The best run, using few-shot learning,\nachieved 0.58 macro F1 score on publication abstracts and 0.75 macro F1 score\non clinical observations, the former being comparable with the state of the\nart, while the latter surpassing the current best in class tool. Conclusion:\nWhile the results are promising, the non-deterministic nature of the outcomes,\nthe high cost and the lack of concordance between different runs using the same\nprompt and input make the use of these LLMs challenging for this particular\ntask.\n", "date": "2023-09-29", "categories": []}
{"id": "2311.10809", "title": "Extracting periodontitis diagnosis in clinical notes with RoBERTa and\n  regular expression", "abstract": "  This study aimed to utilize text processing and natural language processing\n(NLP) models to mine clinical notes for the diagnosis of periodontitis and to\nevaluate the performance of a named entity recognition (NER) model on different\nregular expression (RE) methods. Two complexity levels of RE methods were used\nto extract and generate the training data. The SpaCy package and RoBERTa\ntransformer models were used to build the NER model and evaluate its\nperformance with the manual-labeled gold standards. The comparison of the RE\nmethods with the gold standard showed that as the complexity increased in the\nRE algorithms, the F1 score increased from 0.3-0.4 to around 0.9. The NER\nmodels demonstrated excellent predictions, with the simple RE method showing\n0.84-0.92 in the evaluation metrics, and the advanced and combined RE method\ndemonstrating 0.95-0.99 in the evaluation. This study provided an example of\nthe benefit of combining NER methods and NLP models in extracting target\ninformation from free-text to structured data and fulfilling the need for\nmissing diagnoses from unstructured notes.\n", "date": "2023-11-17", "categories": []}
{"id": "2311.10810", "title": "Use GPT-J Prompt Generation with RoBERTa for NER Models on Diagnosis\n  Extraction of Periodontal Diagnosis from Electronic Dental Records", "abstract": "  This study explored the usability of prompt generation on named entity\nrecognition (NER) tasks and the performance in different settings of the\nprompt. The prompt generation by GPT-J models was utilized to directly test the\ngold standard as well as to generate the seed and further fed to the RoBERTa\nmodel with the spaCy package. In the direct test, a lower ratio of negative\nexamples with higher numbers of examples in prompt achieved the best results\nwith a F1 score of 0.72. The performance revealed consistency, 0.92-0.97 in the\nF1 score, in all settings after training with the RoBERTa model. The study\nhighlighted the importance of seed quality rather than quantity in feeding NER\nmodels. This research reports on an efficient and accurate way to mine clinical\nnotes for periodontal diagnoses, allowing researchers to easily and quickly\nbuild a NER model with the prompt generation approach.\n", "date": "2023-11-17", "categories": []}
{"id": "2311.10856", "title": "Exploring the Consistency, Quality and Challenges in Manual and\n  Automated Coding of Free-text Diagnoses from Hospital Outpatient Letters", "abstract": "  Coding of unstructured clinical free-text to produce interoperable structured\ndata is essential to improve direct care, support clinical communication and to\nenable clinical research.However, manual clinical coding is difficult and time\nconsuming, which motivates the development and use of natural language\nprocessing for automated coding. This work evaluates the quality and\nconsistency of both manual and automated clinical coding of diagnoses from\nhospital outpatient letters. Using 100 randomly selected letters, two human\nclinicians performed coding of diagnosis lists to SNOMED CT. Automated coding\nwas also performed using IMO's Concept Tagger. A gold standard was constructed\nby a panel of clinicians from a subset of the annotated diagnoses. This was\nused to evaluate the quality and consistency of both manual and automated\ncoding via (1) a distance-based metric, treating SNOMED CT as a graph, and (2)\na qualitative metric agreed upon by the panel of clinicians. Correlation\nbetween the two metrics was also evaluated. Comparing human and\ncomputer-generated codes to the gold standard, the results indicate that humans\nslightly out-performed automated coding, while both performed notably better\nwhen there was only a single diagnosis contained in the free-text description.\nAutomated coding was considered acceptable by the panel of clinicians in\napproximately 90% of cases.\n", "date": "2023-11-17", "categories": []}
{"id": "2305.13523", "title": "A Study of Generative Large Language Model for Medical Research and\n  Healthcare", "abstract": "  There is enormous enthusiasm and concerns in using large language models\n(LLMs) in healthcare, yet current assumptions are all based on general-purpose\nLLMs such as ChatGPT. This study develops a clinical generative LLM,\nGatorTronGPT, using 277 billion words of mixed clinical and English text with a\nGPT-3 architecture of 20 billion parameters. GatorTronGPT improves biomedical\nnatural language processing for medical research. Synthetic NLP models trained\nusing GatorTronGPT generated text outperform NLP models trained using\nreal-world clinical text. Physicians Turing test using 1 (worst) to 9 (best)\nscale shows that there is no significant difference in linguistic readability\n(p = 0.22; 6.57 of GatorTronGPT compared with 6.93 of human) and clinical\nrelevance (p = 0.91; 7.0 of GatorTronGPT compared with 6.97 of human) and that\nphysicians cannot differentiate them (p < 0.001). This study provides insights\non the opportunities and challenges of LLMs for medical research and\nhealthcare.\n", "date": "2023-05-22", "categories": []}
{"id": "2211.11799", "title": "Unsupervised extraction, labelling and clustering of segments from\n  clinical notes", "abstract": "  This work is motivated by the scarcity of tools for accurate, unsupervised\ninformation extraction from unstructured clinical notes in computationally\nunderrepresented languages, such as Czech. We introduce a stepping stone to a\nbroad array of downstream tasks such as summarisation or integration of\nindividual patient records, extraction of structured information for national\ncancer registry reporting or building of semi-structured semantic patient\nrepresentations for computing patient embeddings. More specifically, we present\na method for unsupervised extraction of semantically-labelled textual segments\nfrom clinical notes and test it out on a dataset of Czech breast cancer\npatients, provided by Masaryk Memorial Cancer Institute (the largest Czech\nhospital specialising in oncology). Our goal was to extract, classify (i.e.\nlabel) and cluster segments of the free-text notes that correspond to specific\nclinical features (e.g., family background, comorbidities or toxicities). The\npresented results demonstrate the practical relevance of the proposed approach\nfor building more sophisticated extraction and analytical pipelines deployed on\nCzech clinical notes.\n", "date": "2022-11-21", "categories": []}
{"id": "2311.09564", "title": "LongBoX: Evaluating Transformers on Long-Sequence Clinical Tasks", "abstract": "  Many large language models (LLMs) for medicine have largely been evaluated on\nshort texts, and their ability to handle longer sequences such as a complete\nelectronic health record (EHR) has not been systematically explored. Assessing\nthese models on long sequences is crucial since prior work in the general\ndomain has demonstrated performance degradation of LLMs on longer texts.\nMotivated by this, we introduce LongBoX, a collection of seven medical datasets\nin text-to-text format, designed to investigate model performance on long\nsequences. Preliminary experiments reveal that both medical LLMs (e.g., BioGPT)\nand strong general domain LLMs (e.g., FLAN-T5) struggle on this benchmark. We\nfurther evaluate two techniques designed for long-sequence handling: (i)\nlocal-global attention, and (ii) Fusion-in-Decoder (FiD). Our results\ndemonstrate mixed results with long-sequence handling - while scores on some\ndatasets increase, there is substantial room for improvement. We hope that\nLongBoX facilitates the development of more effective long-sequence techniques\nfor the medical domain. Data and source code are available at\nhttps://github.com/Mihir3009/LongBoX.\n", "date": "2023-11-16", "categories": []}
{"id": "2311.08687", "title": "An Eye on Clinical BERT: Investigating Language Model Generalization for\n  Diabetic Eye Disease Phenotyping", "abstract": "  Diabetic eye disease is a major cause of blindness worldwide. The ability to\nmonitor relevant clinical trajectories and detect lapses in care is critical to\nmanaging the disease and preventing blindness. Alas, much of the information\nnecessary to support these goals is found only in the free text of the\nelectronic medical record. To fill this information gap, we introduce a system\nfor extracting evidence from clinical text of 19 clinical concepts related to\ndiabetic eye disease and inferring relevant attributes for each. In developing\nthis ophthalmology phenotyping system, we are also afforded a unique\nopportunity to evaluate the effectiveness of clinical language models at\nadapting to new clinical domains. Across multiple training paradigms, we find\nthat BERT language models pretrained on out-of-distribution clinical data offer\nno significant improvement over BERT language models pretrained on non-clinical\ndata for our domain. Our study tempers recent claims that language models\npretrained on clinical data are necessary for clinical NLP tasks and highlights\nthe importance of not treating clinical language data as a single homogeneous\ndomain.\n", "date": "2023-11-15", "categories": []}
{"id": "2307.11254", "title": "An In-Depth Evaluation of Federated Learning on Biomedical Natural\n  Language Processing", "abstract": "  Language models (LMs) such as BERT and GPT have revolutionized natural\nlanguage processing (NLP). However, the medical field faces challenges in\ntraining LMs due to limited data access and privacy constraints imposed by\nregulations like the Health Insurance Portability and Accountability Act\n(HIPPA) and the General Data Protection Regulation (GDPR). Federated learning\n(FL) offers a decentralized solution that enables collaborative learning while\nensuring data privacy. In this study, we evaluated FL on 2 biomedical NLP tasks\nencompassing 8 corpora using 6 LMs. Our results show that: 1) FL models\nconsistently outperformed models trained on individual clients' data and\nsometimes performed comparably with models trained with polled data; 2) with\nthe fixed number of total data, FL models training with more clients produced\ninferior performance but pre-trained transformer-based models exhibited great\nresilience. 3) FL models significantly outperformed large language models using\nzero-/one-shot learning and offered lightning inference speed.\n", "date": "2023-07-20", "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": "2308.06294", "title": "Enhancing Phenotype Recognition in Clinical Notes Using Large Language\n  Models: PhenoBCBERT and PhenoGPT", "abstract": "  We hypothesize that large language models (LLMs) based on the transformer\narchitecture can enable automated detection of clinical phenotype terms,\nincluding terms not documented in the HPO. In this study, we developed two\ntypes of models: PhenoBCBERT, a BERT-based model, utilizing Bio+Clinical BERT\nas its pre-trained model, and PhenoGPT, a GPT-based model that can be\ninitialized from diverse GPT models, including open-source versions such as\nGPT-J, Falcon, and LLaMA, as well as closed-source versions such as GPT-3 and\nGPT-3.5. We compared our methods with PhenoTagger, a recently developed HPO\nrecognition tool that combines rule-based and deep learning methods. We found\nthat our methods can extract more phenotype concepts, including novel ones not\ncharacterized by HPO. We also performed case studies on biomedical literature\nto illustrate how new phenotype information can be recognized and extracted. We\ncompared current BERT-based versus GPT-based models for phenotype tagging, in\nmultiple aspects including model architecture, memory usage, speed, accuracy,\nand privacy protection. We also discussed the addition of a negation step and\nan HPO normalization layer to the transformer models for improved HPO term\ntagging. In conclusion, PhenoBCBERT and PhenoGPT enable the automated discovery\nof phenotype terms from clinical notes and biomedical literature, facilitating\nautomated downstream tasks to derive new biological insights on human diseases.\n", "date": "2023-08-11", "categories": []}
{"id": "2311.04925", "title": "Investigating Deep-Learning NLP for Automating the Extraction of\n  Oncology Efficacy Endpoints from Scientific Literature", "abstract": "  Benchmarking drug efficacy is a critical step in clinical trial design and\nplanning. The challenge is that much of the data on efficacy endpoints is\nstored in scientific papers in free text form, so extraction of such data is\ncurrently a largely manual task. Our objective is to automate this task as much\nas possible. In this study we have developed and optimised a framework to\nextract efficacy endpoints from text in scientific papers, using a machine\nlearning approach. Our machine learning model predicts 25 classes associated\nwith efficacy endpoints and leads to high F1 scores (harmonic mean of precision\nand recall) of 96.4% on the test set, and 93.9% and 93.7% on two case studies.\nThese methods were evaluated against - and showed strong agreement with -\nsubject matter experts and show significant promise in the future of automating\nthe extraction of clinical endpoints from free text. Clinical information\nextraction from text data is currently a laborious manual task which scales\npoorly and is prone to human error. Demonstrating the ability to extract\nefficacy endpoints automatically shows great promise for accelerating clinical\ntrial design moving forwards.\n", "date": "2023-11-03", "categories": []}
{"id": "2310.08759", "title": "Question Answering for Electronic Health Records: A Scoping Review of\n  datasets and models", "abstract": "  Question Answering (QA) systems on patient-related data can assist both\nclinicians and patients. They can, for example, assist clinicians in\ndecision-making and enable patients to have a better understanding of their\nmedical history. Significant amounts of patient data are stored in Electronic\nHealth Records (EHRs), making EHR QA an important research area. In EHR QA, the\nanswer is obtained from the medical record of the patient. Because of the\ndifferences in data format and modality, this differs greatly from other\nmedical QA tasks that employ medical websites or scientific papers to retrieve\nanswers, making it critical to research EHR question answering. This study\naimed to provide a methodological review of existing works on QA over EHRs. We\nsearched for articles from January 1st, 2005 to September 30th, 2023 in four\ndigital sources including Google Scholar, ACL Anthology, ACM Digital Library,\nand PubMed to collect relevant publications on EHR QA. 4111 papers were\nidentified for our study, and after screening based on our inclusion criteria,\nwe obtained a total of 47 papers for further study. Out of the 47 papers, 25\npapers were about EHR QA datasets, and 37 papers were about EHR QA models. It\nwas observed that QA on EHRs is relatively new and unexplored. Most of the\nworks are fairly recent. Also, it was observed that emrQA is by far the most\npopular EHR QA dataset, both in terms of citations and usage in other papers.\nFurthermore, we identified the different models used in EHR QA along with the\nevaluation metrics used for these models.\n", "date": "2023-10-12", "categories": []}
{"id": "2311.02205", "title": "An Introduction to Natural Language Processing Techniques and Framework\n  for Clinical Implementation in Radiation Oncology", "abstract": "  Natural Language Processing (NLP) is a key technique for developing Medical\nArtificial Intelligence (AI) systems that leverage Electronic Health Record\n(EHR) data to build diagnostic and prognostic models. NLP enables the\nconversion of unstructured clinical text into structured data that can be fed\ninto AI algorithms. The emergence of the transformer architecture and large\nlanguage models (LLMs) has led to remarkable advances in NLP for various\nhealthcare tasks, such as entity recognition, relation extraction, sentence\nsimilarity, text summarization, and question answering. In this article, we\nreview the major technical innovations that underpin modern NLP models and\npresent state-of-the-art NLP applications that employ LLMs in radiation\noncology research. However, these LLMs are prone to many errors such as\nhallucinations, biases, and ethical violations, which necessitate rigorous\nevaluation and validation before clinical deployment. As such, we propose a\ncomprehensive framework for assessing the NLP models based on their purpose and\nclinical fit, technical performance, bias and trust, legal and ethical\nimplications, and quality assurance, prior to implementation in clinical\nradiation oncology. Our article aims to provide guidance and insights for\nresearchers and clinicians who are interested in developing and using NLP\nmodels in clinical radiation oncology.\n", "date": "2023-11-03", "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": "2309.10160", "title": "RadOnc-GPT: A Large Language Model for Radiation Oncology", "abstract": "  This paper presents RadOnc-GPT, a large language model specialized for\nradiation oncology through advanced tuning methods. RadOnc-GPT was finetuned on\na large dataset of radiation oncology patient records from the Mayo Clinic in\nArizona. The model employs instruction tuning on three key tasks - generating\nradiotherapy treatment regimens, determining optimal radiation modalities, and\nproviding diagnostic descriptions/ICD codes based on patient diagnostic\ndetails. Evaluations conducted by comparing RadOnc-GPT outputs to general large\nlanguage model outputs showed higher ROUGE scores in these three tasks. The\nstudy demonstrated the potential of using large language models fine-tuned\nusing domain-specific knowledge like RadOnc-GPT to achieve transformational\ncapabilities in highly specialized healthcare fields such as radiation\noncology. However, our model's clinical relevance requires confirmation, and it\nspecializes in only the aforementioned three specific tasks and lacks broader\napplicability. Furthermore, its evaluation through ROUGE scores might not\nreflect the true semantic and clinical accuracy - challenges we intend to\naddress in future research.\n", "date": "2023-09-18", "categories": []}
{"id": "2311.02748", "title": "Pyclipse, a library for deidentification of free-text clinical notes", "abstract": "  Automated deidentification of clinical text data is crucial due to the high\ncost of manual deidentification, which has been a barrier to sharing clinical\ntext and the advancement of clinical natural language processing. However,\ncreating effective automated deidentification tools faces several challenges,\nincluding issues in reproducibility due to differences in text processing,\nevaluation methods, and a lack of consistency across clinical domains and\ninstitutions. To address these challenges, we propose the pyclipse framework, a\nunified and configurable evaluation procedure to streamline the comparison of\ndeidentification algorithms. Pyclipse serves as a single interface for running\nopen-source deidentification algorithms on local clinical data, allowing for\ncontext-specific evaluation. To demonstrate the utility of pyclipse, we compare\nsix deidentification algorithms across four public and two private clinical\ntext datasets. We find that algorithm performance consistently falls short of\nthe results reported in the original papers, even when evaluated on the same\nbenchmark dataset. These discrepancies highlight the complexity of accurately\nassessing and comparing deidentification algorithms, emphasizing the need for a\nreproducible, adjustable, and extensible framework like pyclipse. Our framework\nlays the foundation for a unified approach to evaluate and improve\ndeidentification tools, ultimately enhancing patient protection in clinical\nnatural language processing.\n", "date": "2023-11-05", "categories": []}
{"id": "2311.03113", "title": "Injecting Categorical Labels and Syntactic Information into Biomedical\n  NER", "abstract": "  We present a simple approach to improve biomedical named entity recognition\n(NER) by injecting categorical labels and Part-of-speech (POS) information into\nthe model. We use two approaches, in the first approach, we first train a\nsequence-level classifier to classify the sentences into categories to obtain\nthe sentence-level tags (categorical labels). The sequence classifier is\nmodeled as an entailment problem by modifying the labels as a natural language\ntemplate. This helps to improve the accuracy of the classifier. Further, this\nlabel information is injected into the NER model. In this paper, we demonstrate\neffective ways to represent and inject these labels and POS attributes into the\nNER model. In the second approach, we jointly learn the categorical labels and\nNER labels. Here we also inject the POS tags into the model to increase the\nsyntactic context of the model. Experiments on three benchmark datasets show\nthat incorporating categorical label information with syntactic context is\nquite useful and outperforms baseline BERT-based models.\n", "date": "2023-11-06", "categories": []}
{"id": "2310.20033", "title": "Synthetic Imitation Edit Feedback for Factual Alignment in Clinical\n  Summarization", "abstract": "  Large Language Models (LLMs) like the GPT and LLaMA families have\ndemonstrated exceptional capabilities in capturing and condensing critical\ncontextual information and achieving state-of-the-art performance in the\nsummarization task. However, community concerns about these models'\nhallucination issues continue to rise. LLMs sometimes generate factually\nhallucinated summaries, which can be extremely harmful in the clinical domain\nNLP tasks (e.g., clinical note summarization), where factually incorrect\nstatements can lead to critically erroneous diagnoses. Fine-tuning LLMs using\nhuman feedback has shown the promise of aligning LLMs to be factually\nconsistent during generation, but such training procedure requires high-quality\nhuman-annotated data, which can be extremely expensive to get in the clinical\ndomain. In this work, we propose a new pipeline using ChatGPT instead of human\nexperts to generate high-quality feedback data for improving factual\nconsistency in the clinical note summarization task. We focus specifically on\nedit feedback because recent work discusses the shortcomings of human alignment\nvia preference feedback in complex situations (such as clinical NLP tasks that\nrequire extensive expert knowledge), as well as some advantages of collecting\nedit feedback from domain experts. In addition, although GPT has reached the\nexpert level in many clinical NLP tasks (e.g., USMLE QA), there is not much\nprevious work discussing whether GPT can generate expert-level edit feedback\nfor LMs in the clinical note summarization task. We hope to fill this gap.\nFinally, our evaluations demonstrate the potential use of GPT edits in human\nalignment, especially from a factuality perspective.\n", "date": "2023-10-30", "categories": []}
{"id": "2310.20089", "title": "Keyword-optimized Template Insertion for Clinical Information Extraction\n  via Prompt-based Learning", "abstract": "  Clinical note classification is a common clinical NLP task. However,\nannotated data-sets are scarse. Prompt-based learning has recently emerged as\nan effective method to adapt pre-trained models for text classification using\nonly few training examples. A critical component of prompt design is the\ndefinition of the template (i.e. prompt text). The effect of template position,\nhowever, has been insufficiently investigated. This seems particularly\nimportant in the clinical setting, where task-relevant information is usually\nsparse in clinical notes. In this study we develop a keyword-optimized template\ninsertion method (KOTI) and show how optimizing position can improve\nperformance on several clinical tasks in a zero-shot and few-shot training\nsetting.\n", "date": "2023-10-31", "categories": []}
{"id": "2210.12770", "title": "Exploring the Value of Pre-trained Language Models for Clinical Named\n  Entity Recognition", "abstract": "  The practice of fine-tuning Pre-trained Language Models (PLMs) from general\nor domain-specific data to a specific task with limited resources, has gained\npopularity within the field of natural language processing (NLP). In this work,\nwe re-visit this assumption and carry out an investigation in clinical NLP,\nspecifically Named Entity Recognition on drugs and their related attributes. We\ncompare Transformer models that are trained from scratch to fine-tuned\nBERT-based LLMs namely BERT, BioBERT, and ClinicalBERT. Furthermore, we examine\nthe impact of an additional CRF layer on such models to encourage contextual\nlearning. We use n2c2-2018 shared task data for model development and\nevaluations. The experimental outcomes show that 1) CRF layers improved all\nlanguage models; 2) referring to BIO-strict span level evaluation using\nmacro-average F1 score, although the fine-tuned LLMs achieved 0.83+ scores, the\nTransformerCRF model trained from scratch achieved 0.78+, demonstrating\ncomparable performances with much lower cost - e.g. with 39.80\\% less training\nparameters; 3) referring to BIO-strict span-level evaluation using\nweighted-average F1 score, ClinicalBERT-CRF, BERT-CRF, and TransformerCRF\nexhibited lower score differences, with 97.59\\%/97.44\\%/96.84\\% respectively.\n4) applying efficient training by down-sampling for better data distribution\nfurther reduced the training cost and need for data, while maintaining similar\nscores - i.e. around 0.02 points lower compared to using the full dataset. Our\nmodels will be hosted at \\url{https://github.com/HECTA-UoM/TransformerCRF}\n", "date": "2022-10-23", "categories": []}
{"id": "2305.03598", "title": "NLI4CT: Multi-Evidence Natural Language Inference for Clinical Trial\n  Reports", "abstract": "  How can we interpret and retrieve medical evidence to support clinical\ndecisions? Clinical trial reports (CTR) amassed over the years contain\nindispensable information for the development of personalized medicine.\nHowever, it is practically infeasible to manually inspect over 400,000+\nclinical trial reports in order to find the best evidence for experimental\ntreatments. Natural Language Inference (NLI) offers a potential solution to\nthis problem, by allowing the scalable computation of textual entailment.\nHowever, existing NLI models perform poorly on biomedical corpora, and\npreviously published datasets fail to capture the full complexity of inference\nover CTRs. In this work, we present a novel resource to advance research on NLI\nfor reasoning on CTRs. The resource includes two main tasks. Firstly, to\ndetermine the inference relation between a natural language statement, and a\nCTR. Secondly, to retrieve supporting facts to justify the predicted relation.\nWe provide NLI4CT, a corpus of 2400 statements and CTRs, annotated for these\ntasks. Baselines on this corpus expose the limitations of existing NLI models,\nwith 6 state-of-the-art NLI models achieving a maximum F1 score of 0.627. To\nthe best of our knowledge, we are the first to design a task that covers the\ninterpretation of full CTRs. To encourage further work on this challenging\ndataset, we make the corpus, competition leaderboard, website and code to\nreplicate the baseline experiments available at:\nhttps://github.com/ai-systems/nli4ct\n", "date": "2023-05-05", "categories": []}
{"id": "2310.18355", "title": "Health Disparities through Generative AI Models: A Comparison Study\n  Using A Domain Specific large language model", "abstract": "  Health disparities are differences in health outcomes and access to\nhealthcare between different groups, including racial and ethnic minorities,\nlow-income people, and rural residents. An artificial intelligence (AI) program\ncalled large language models (LLMs) can understand and generate human language,\nimproving health communication and reducing health disparities. There are many\nchallenges in using LLMs in human-doctor interaction, including the need for\ndiverse and representative data, privacy concerns, and collaboration between\nhealthcare providers and technology experts. We introduce the comparative\ninvestigation of domain-specific large language models such as SciBERT with a\nmulti-purpose LLMs BERT. We used cosine similarity to analyze text queries\nabout health disparities in exam rooms when factors such as race are used\nalone. Using text queries, SciBERT fails when it doesn't differentiate between\nqueries text: \"race\" alone and \"perpetuates health disparities.\" We believe\nclinicians can use generative AI to create a draft response when communicating\nasynchronously with patients. However, careful attention must be paid to ensure\nthey are developed and implemented ethically and equitably.\n", "date": "2023-10-23", "categories": []}
{"id": "2310.18431", "title": "SDOH-NLI: a Dataset for Inferring Social Determinants of Health from\n  Clinical Notes", "abstract": "  Social and behavioral determinants of health (SDOH) play a significant role\nin shaping health outcomes, and extracting these determinants from clinical\nnotes is a first step to help healthcare providers systematically identify\nopportunities to provide appropriate care and address disparities. Progress on\nusing NLP methods for this task has been hindered by the lack of high-quality\npublicly available labeled data, largely due to the privacy and regulatory\nconstraints on the use of real patients' information. This paper introduces a\nnew dataset, SDOH-NLI, that is based on publicly available notes and which we\nrelease publicly. We formulate SDOH extraction as a natural language inference\n(NLI) task, and provide binary textual entailment labels obtained from human\nraters for a cross product of a set of social history snippets as premises and\nSDOH factors as hypotheses. Our dataset differs from standard NLI benchmarks in\nthat our premises and hypotheses are obtained independently. We evaluate both\n\"off-the-shelf\" entailment models as well as models fine-tuned on our data, and\nhighlight the ways in which our dataset appears more challenging than commonly\nused NLI datasets.\n", "date": "2023-10-27", "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": "2310.19212", "title": "EHRTutor: Enhancing Patient Understanding of Discharge Instructions", "abstract": "  Large language models have shown success as a tutor in education in various\nfields. Educating patients about their clinical visits plays a pivotal role in\npatients' adherence to their treatment plans post-discharge. This paper\npresents EHRTutor, an innovative multi-component framework leveraging the Large\nLanguage Model (LLM) for patient education through conversational\nquestion-answering. EHRTutor first formulates questions pertaining to the\nelectronic health record discharge instructions. It then educates the patient\nthrough conversation by administering each question as a test. Finally, it\ngenerates a summary at the end of the conversation. Evaluation results using\nLLMs and domain experts have shown a clear preference for EHRTutor over the\nbaseline. Moreover, EHRTutor also offers a framework for generating synthetic\npatient education dialogues that can be used for future in-house system\ntraining.\n", "date": "2023-10-30", "categories": []}
{"id": "2310.11191", "title": "Medical Text Simplification: Optimizing for Readability with\n  Unlikelihood Training and Reranked Beam Search Decoding", "abstract": "  Text simplification has emerged as an increasingly useful application of AI\nfor bridging the communication gap in specialized fields such as medicine,\nwhere the lexicon is often dominated by technical jargon and complex\nconstructs. Despite notable progress, methods in medical simplification\nsometimes result in the generated text having lower quality and diversity. In\nthis work, we explore ways to further improve the readability of text\nsimplification in the medical domain. We propose (1) a new unlikelihood loss\nthat encourages generation of simpler terms and (2) a reranked beam search\ndecoding method that optimizes for simplicity, which achieve better performance\non readability metrics on three datasets. This study's findings offer promising\navenues for improving text simplification in the medical field.\n", "date": "2023-10-17", "categories": []}
{"id": "2310.13864", "title": "RECAP: Towards Precise Radiology Report Generation via Dynamic Disease\n  Progression Reasoning", "abstract": "  Automating radiology report generation can significantly alleviate\nradiologists' workloads. Previous research has primarily focused on realizing\nhighly concise observations while neglecting the precise attributes that\ndetermine the severity of diseases (e.g., small pleural effusion). Since\nincorrect attributes will lead to imprecise radiology reports, strengthening\nthe generation process with precise attribute modeling becomes necessary.\nAdditionally, the temporal information contained in the historical records,\nwhich is crucial in evaluating a patient's current condition (e.g., heart size\nis unchanged), has also been largely disregarded. To address these issues, we\npropose RECAP, which generates precise and accurate radiology reports via\ndynamic disease progression reasoning. Specifically, RECAP first predicts the\nobservations and progressions (i.e., spatiotemporal information) given two\nconsecutive radiographs. It then combines the historical records,\nspatiotemporal information, and radiographs for report generation, where a\ndisease progression graph and dynamic progression reasoning mechanism are\ndevised to accurately select the attributes of each observation and\nprogression. Extensive experiments on two publicly available datasets\ndemonstrate the effectiveness of our model.\n", "date": "2023-10-21", "categories": []}
{"id": "2310.14573", "title": "Exploring the Boundaries of GPT-4 in Radiology", "abstract": "  The recent success of general-domain large language models (LLMs) has\nsignificantly changed the natural language processing paradigm towards a\nunified foundation model across domains and applications. In this paper, we\nfocus on assessing the performance of GPT-4, the most capable LLM so far, on\nthe text-based applications for radiology reports, comparing against\nstate-of-the-art (SOTA) radiology-specific models. Exploring various prompting\nstrategies, we evaluated GPT-4 on a diverse range of common radiology tasks and\nwe found GPT-4 either outperforms or is on par with current SOTA radiology\nmodels. With zero-shot prompting, GPT-4 already obtains substantial gains\n($\\approx$ 10% absolute improvement) over radiology models in temporal sentence\nsimilarity classification (accuracy) and natural language inference ($F_1$).\nFor tasks that require learning dataset-specific style or schema (e.g. findings\nsummarisation), GPT-4 improves with example-based prompting and matches\nsupervised SOTA. Our extensive error analysis with a board-certified\nradiologist shows GPT-4 has a sufficient level of radiology knowledge with only\noccasional errors in complex context that require nuanced domain knowledge. For\nfindings summarisation, GPT-4 outputs are found to be overall comparable with\nexisting manually-written impressions.\n", "date": "2023-10-23", "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": "2305.13395", "title": "BioDEX: Large-Scale Biomedical Adverse Drug Event Extraction for\n  Real-World Pharmacovigilance", "abstract": "  Timely and accurate extraction of Adverse Drug Events (ADE) from biomedical\nliterature is paramount for public safety, but involves slow and costly manual\nlabor. We set out to improve drug safety monitoring (pharmacovigilance, PV)\nthrough the use of Natural Language Processing (NLP). We introduce BioDEX, a\nlarge-scale resource for Biomedical adverse Drug Event Extraction, rooted in\nthe historical output of drug safety reporting in the U.S. BioDEX consists of\n65k abstracts and 19k full-text biomedical papers with 256k associated\ndocument-level safety reports created by medical experts. The core features of\nthese reports include the reported weight, age, and biological sex of a\npatient, a set of drugs taken by the patient, the drug dosages, the reactions\nexperienced, and whether the reaction was life threatening. In this work, we\nconsider the task of predicting the core information of the report given its\noriginating paper. We estimate human performance to be 72.0% F1, whereas our\nbest model achieves 62.3% F1, indicating significant headroom on this task. We\nalso begin to explore ways in which these models could help professional PV\nreviewers. Our code and data are available: https://github.com/KarelDO/BioDEX.\n", "date": "2023-05-22", "categories": []}
{"id": "2309.12273", "title": "Improving VTE Identification through Adaptive NLP Model Selection and\n  Clinical Expert Rule-based Classifier from Radiology Reports", "abstract": "  Rapid and accurate identification of Venous thromboembolism (VTE), a severe\ncardiovascular condition including deep vein thrombosis (DVT) and pulmonary\nembolism (PE), is important for effective treatment. Leveraging Natural\nLanguage Processing (NLP) on radiology reports, automated methods have shown\npromising advancements in identifying VTE events from retrospective data\ncohorts or aiding clinical experts in identifying VTE events from radiology\nreports. However, effectively training Deep Learning (DL) and the NLP models is\nchallenging due to limited labeled medical text data, the complexity and\nheterogeneity of radiology reports, and data imbalance. This study proposes\nnovel method combinations of DL methods, along with data augmentation, adaptive\npre-trained NLP model selection, and a clinical expert NLP rule-based\nclassifier, to improve the accuracy of VTE identification in unstructured\n(free-text) radiology reports. Our experimental results demonstrate the model's\nefficacy, achieving an impressive 97\\% accuracy and 97\\% F1 score in predicting\nDVT, and an outstanding 98.3\\% accuracy and 98.4\\% F1 score in predicting PE.\nThese findings emphasize the model's robustness and its potential to\nsignificantly contribute to VTE research.\n", "date": "2023-09-21", "categories": []}
{"id": "2310.12989", "title": "Enhancing Health Data Interoperability with Large Language Models: A\n  FHIR Study", "abstract": "  In this study, we investigated the ability of the large language model (LLM)\nto enhance healthcare data interoperability. We leveraged the LLM to convert\nclinical texts into their corresponding FHIR resources. Our experiments,\nconducted on 3,671 snippets of clinical text, demonstrated that the LLM not\nonly streamlines the multi-step natural language processing and human\ncalibration processes but also achieves an exceptional accuracy rate of over\n90% in exact matches when compared to human annotations.\n", "date": "2023-09-19", "categories": []}
{"id": "2310.13146", "title": "CLIFT: Analysing Natural Distribution Shift on Question Answering Models\n  in Clinical Domain", "abstract": "  This paper introduces a new testbed CLIFT (Clinical Shift) for the clinical\ndomain Question-answering task. The testbed includes 7.5k high-quality question\nanswering samples to provide a diverse and reliable benchmark. We performed a\ncomprehensive experimental study and evaluated several QA deep-learning models\nunder the proposed testbed. Despite impressive results on the original test\nset, the performance degrades when applied to new test sets, which shows the\ndistribution shift. Our findings emphasize the need for and the potential for\nincreasing the robustness of clinical domain models under distributional\nshifts. The testbed offers one way to track progress in that direction. It also\nhighlights the necessity of adopting evaluation metrics that consider\nrobustness to natural distribution shifts. We plan to expand the corpus by\nadding more samples and model results. The full paper and the updated benchmark\nare available at github.com/openlifescience-ai/clift\n", "date": "2023-10-19", "categories": []}
{"id": "2305.12532", "title": "Multilingual Simplification of Medical Texts", "abstract": "  Automated text simplification aims to produce simple versions of complex\ntexts. This task is especially useful in the medical domain, where the latest\nmedical findings are typically communicated via complex and technical articles.\nThis creates barriers for laypeople seeking access to up-to-date medical\nfindings, consequently impeding progress on health literacy. Most existing work\non medical text simplification has focused on monolingual settings, with the\nresult that such evidence would be available only in just one language (most\noften, English). This work addresses this limitation via multilingual\nsimplification, i.e., directly simplifying complex texts into simplified texts\nin multiple languages. We introduce MultiCochrane, the first sentence-aligned\nmultilingual text simplification dataset for the medical domain in four\nlanguages: English, Spanish, French, and Farsi. We evaluate fine-tuned and\nzero-shot models across these languages, with extensive human assessments and\nanalyses. Although models can now generate viable simplified texts, we identify\noutstanding challenges that this dataset might be used to address.\n", "date": "2023-05-21", "categories": []}
{"id": "2303.09366", "title": "The Scope of In-Context Learning for the Extraction of Medical Temporal\n  Constraints", "abstract": "  Medications often impose temporal constraints on everyday patient activity.\nViolations of such medical temporal constraints (MTCs) lead to a lack of\ntreatment adherence, in addition to poor health outcomes and increased\nhealthcare expenses. These MTCs are found in drug usage guidelines (DUGs) in\nboth patient education materials and clinical texts. Computationally\nrepresenting MTCs in DUGs will advance patient-centric healthcare applications\nby helping to define safe patient activity patterns. We define a novel taxonomy\nof MTCs found in DUGs and develop a novel context-free grammar (CFG) based\nmodel to computationally represent MTCs from unstructured DUGs. Additionally,\nwe release three new datasets with a combined total of N = 836 DUGs labeled\nwith normalized MTCs. We develop an in-context learning (ICL) solution for\nautomatically extracting and normalizing MTCs found in DUGs, achieving an\naverage F1 score of 0.62 across all datasets. Finally, we rigorously\ninvestigate ICL model performance against a baseline model, across datasets and\nMTC types, and through in-depth error analysis.\n", "date": "2023-03-16", "categories": []}
{"id": "2310.09494", "title": "Computational analyses of linguistic features with schizophrenic and\n  autistic traits along with formal thought disorders", "abstract": "  [See full abstract in the pdf] Formal Thought Disorder (FTD), which is a\ngroup of symptoms in cognition that affects language and thought, can be\nobserved through language. FTD is seen across such developmental or psychiatric\ndisorders as Autism Spectrum Disorder (ASD) or Schizophrenia, and its related\nSchizotypal Personality Disorder (SPD). This paper collected a Japanese\naudio-report dataset with score labels related to ASD and SPD through a\ncrowd-sourcing service from the general population. We measured language\ncharacteristics with the 2nd edition of the Social Responsiveness Scale (SRS2)\nand the Schizotypal Personality Questionnaire (SPQ), including an odd speech\nsubscale from SPQ to quantify the FTD symptoms. We investigated the following\nfour research questions through machine-learning-based score predictions: (RQ1)\nHow are schizotypal and autistic measures correlated? (RQ2) What is the most\nsuitable task to elicit FTD symptoms? (RQ3) Does the length of speech affect\nthe elicitation of FTD symptoms? (RQ4) Which features are critical for\ncapturing FTD symptoms? We confirmed that an FTD-related subscale, odd speech,\nwas significantly correlated with both the total SPQ and SRS scores, although\nthey themselves were not correlated significantly. Our regression analysis\nindicated that longer speech about a negative memory elicited more FTD\nsymptoms. The ablation study confirmed the importance of function words and\nboth the abstract and temporal features for FTD-related odd speech estimation.\nIn contrast, content words were effective only in the SRS predictions, and\ncontent words were effective only in the SPQ predictions, a result that implies\nthe differences between SPD-like and ASD-like symptoms. Data and programs used\nin this paper can be found here:\nhttps://sites.google.com/view/sagatake/resource.\n", "date": "2023-10-14", "categories": []}
{"id": "2310.09623", "title": "A Digital Language Coherence Marker for Monitoring Dementia", "abstract": "  The use of spontaneous language to derive appropriate digital markers has\nbecome an emergent, promising and non-intrusive method to diagnose and monitor\ndementia. Here we propose methods to capture language coherence as a\ncost-effective, human-interpretable digital marker for monitoring cognitive\nchanges in people with dementia. We introduce a novel task to learn the\ntemporal logical consistency of utterances in short transcribed narratives and\ninvestigate a range of neural approaches. We compare such language coherence\npatterns between people with dementia and healthy controls and conduct a\nlongitudinal evaluation against three clinical bio-markers to investigate the\nreliability of our proposed digital coherence marker. The coherence marker\nshows a significant difference between people with mild cognitive impairment,\nthose with Alzheimer's Disease and healthy controls. Moreover our analysis\nshows high association between the coherence marker and the clinical\nbio-markers as well as generalisability potential to other related conditions.\n", "date": "2023-10-14", "categories": []}
{"id": "2310.09672", "title": "Towards Semi-Structured Automatic ICD Coding via Tree-based Contrastive\n  Learning", "abstract": "  Automatic coding of International Classification of Diseases (ICD) is a\nmulti-label text categorization task that involves extracting disease or\nprocedure codes from clinical notes. Despite the application of\nstate-of-the-art natural language processing (NLP) techniques, there are still\nchallenges including limited availability of data due to privacy constraints\nand the high variability of clinical notes caused by different writing habits\nof medical professionals and various pathological features of patients. In this\nwork, we investigate the semi-structured nature of clinical notes and propose\nan automatic algorithm to segment them into sections. To address the\nvariability issues in existing ICD coding models with limited data, we\nintroduce a contrastive pre-training approach on sections using a soft\nmulti-label similarity metric based on tree edit distance. Additionally, we\ndesign a masked section training strategy to enable ICD coding models to locate\nsections related to ICD codes. Extensive experimental results demonstrate that\nour proposed training strategies effectively enhance the performance of\nexisting ICD coding methods.\n", "date": "2023-10-14", "categories": []}
{"id": "2310.09897", "title": "Reformulating NLP tasks to Capture Longitudinal Manifestation of\n  Language Disorders in People with Dementia", "abstract": "  Dementia is associated with language disorders which impede communication.\nHere, we automatically learn linguistic disorder patterns by making use of a\nmoderately-sized pre-trained language model and forcing it to focus on\nreformulated natural language processing (NLP) tasks and associated linguistic\npatterns. Our experiments show that NLP tasks that encapsulate contextual\ninformation and enhance the gradient signal with linguistic patterns benefit\nperformance. We then use the probability estimates from the best model to\nconstruct digital linguistic markers measuring the overall quality in\ncommunication and the intensity of a variety of language disorders. We\ninvestigate how the digital markers characterize dementia speech from a\nlongitudinal perspective. We find that our proposed communication marker is\nable to robustly and reliably characterize the language of people with\ndementia, outperforming existing linguistic approaches; and shows external\nvalidity via significant correlation with clinical markers of behaviour.\nFinally, our proposed linguistic disorder markers provide useful insights into\ngradual language impairment associated with disease progression.\n", "date": "2023-10-15", "categories": []}
{"id": "2305.13066", "title": "Biomedical Named Entity Recognition via Dictionary-based Synonym\n  Generalization", "abstract": "  Biomedical named entity recognition is one of the core tasks in biomedical\nnatural language processing (BioNLP). To tackle this task, numerous\nsupervised/distantly supervised approaches have been proposed. Despite their\nremarkable success, these approaches inescapably demand laborious human effort.\nTo alleviate the need of human effort, dictionary-based approaches have been\nproposed to extract named entities simply based on a given dictionary. However,\none downside of existing dictionary-based approaches is that they are\nchallenged to identify concept synonyms that are not listed in the given\ndictionary, which we refer as the synonym generalization problem. In this\nstudy, we propose a novel Synonym Generalization (SynGen) framework that\nrecognizes the biomedical concepts contained in the input text using span-based\npredictions. In particular, SynGen introduces two regularization terms, namely,\n(1) a synonym distance regularizer; and (2) a noise perturbation regularizer,\nto minimize the synonym generalization error. To demonstrate the effectiveness\nof our approach, we provide a theoretical analysis of the bound of synonym\ngeneralization error. We extensively evaluate our approach on a wide range of\nbenchmarks and the results verify that SynGen outperforms previous\ndictionary-based models by notable margins. Lastly, we provide a detailed\nanalysis to further reveal the merits and inner-workings of our approach.\n", "date": "2023-05-22", "categories": []}
{"id": "2310.07282", "title": "An Analysis on Large Language Models in Healthcare: A Case Study of\n  BioBERT", "abstract": "  This paper conducts a comprehensive investigation into applying large\nlanguage models, particularly on BioBERT, in healthcare. It begins with\nthoroughly examining previous natural language processing (NLP) approaches in\nhealthcare, shedding light on the limitations and challenges these methods\nface. Following that, this research explores the path that led to the\nincorporation of BioBERT into healthcare applications, highlighting its\nsuitability for addressing the specific requirements of tasks related to\nbiomedical text mining. The analysis outlines a systematic methodology for\nfine-tuning BioBERT to meet the unique needs of the healthcare domain. This\napproach includes various components, including the gathering of data from a\nwide range of healthcare sources, data annotation for tasks like identifying\nmedical entities and categorizing them, and the application of specialized\npreprocessing techniques tailored to handle the complexities found in\nbiomedical texts. Additionally, the paper covers aspects related to model\nevaluation, with a focus on healthcare benchmarks and functions like processing\nof natural language in biomedical, question-answering, clinical document\nclassification, and medical entity recognition. It explores techniques to\nimprove the model's interpretability and validates its performance compared to\nexisting healthcare-focused language models. The paper thoroughly examines\nethical considerations, particularly patient privacy and data security. It\nhighlights the benefits of incorporating BioBERT into healthcare contexts,\nincluding enhanced clinical decision support and more efficient information\nretrieval. Nevertheless, it acknowledges the impediments and complexities of\nthis integration, encompassing concerns regarding data privacy, transparency,\nresource-intensive requirements, and the necessity for model customization to\nalign with diverse healthcare domains.\n", "date": "2023-10-11", "categories": []}
{"id": "2310.07146", "title": "Empowering Psychotherapy with Large Language Models: Cognitive\n  Distortion Detection through Diagnosis of Thought Prompting", "abstract": "  Mental illness remains one of the most critical public health issues of our\ntime, due to the severe scarcity and accessibility limit of professionals.\nPsychotherapy requires high-level expertise to conduct deep, complex reasoning\nand analysis on the cognition modeling of the patients. In the era of Large\nLanguage Models, we believe it is the right time to develop AI assistance for\ncomputational psychotherapy. We study the task of cognitive distortion\ndetection and propose the Diagnosis of Thought (DoT) prompting. DoT performs\ndiagnosis on the patient's speech via three stages: subjectivity assessment to\nseparate the facts and the thoughts; contrastive reasoning to elicit the\nreasoning processes supporting and contradicting the thoughts; and schema\nanalysis to summarize the cognition schemas. The generated diagnosis rationales\nthrough the three stages are essential for assisting the professionals.\nExperiments demonstrate that DoT obtains significant improvements over ChatGPT\nfor cognitive distortion detection, while generating high-quality rationales\napproved by human experts.\n", "date": "2023-10-11", "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": "2306.17175", "title": "RECAP-KG: Mining Knowledge Graphs from Raw GP Notes for Remote COVID-19\n  Assessment in Primary Care", "abstract": "  Clinical decision-making is a fundamental stage in delivering appropriate\ncare to patients. In recent years several decision-making systems designed to\naid the clinician in this process have been developed. However, technical\nsolutions currently in use are based on simple regression models and are only\nable to take into account simple pre-defined multiple-choice features, such as\npatient age, pre-existing conditions, smoker status, etc. One particular source\nof patient data, that available decision-making systems are incapable of\nprocessing is the collection of patient consultation GP notes. These contain\ncrucial signs and symptoms - the information used by clinicians in order to\nmake a final decision and direct the patient to the appropriate care.\nExtracting information from GP notes is a technically challenging problem, as\nthey tend to include abbreviations, typos, and incomplete sentences.\n  This paper addresses this open challenge. We present a framework that\nperforms knowledge graph construction from raw GP medical notes written during\nor after patient consultations. By relying on support phrases mined from the\nSNOMED ontology, as well as predefined supported facts from values used in the\nRECAP (REmote COVID-19 Assessment in Primary Care) patient risk prediction\ntool, our graph generative framework is able to extract structured knowledge\ngraphs from the highly unstructured and inconsistent format that consultation\nnotes are written in. Our knowledge graphs include information about existing\npatient symptoms, their duration, and their severity.\n  We apply our framework to consultation notes of COVID-19 patients in the UK\nCOVID-19 Clinical Assesment Servcie (CCAS) patient dataset. We provide a\nquantitative evaluation of the performance of our framework, demonstrating that\nour approach has better accuracy than traditional NLP methods when answering\nquestions about patients.\n", "date": "2023-06-17", "categories": []}
{"id": "2310.02229", "title": "Extraction of Medication and Temporal Relation from Clinical Text using\n  Neural Language Models", "abstract": "  Clinical texts, represented in electronic medical records (EMRs), contain\nrich medical information and are essential for disease prediction, personalised\ninformation recommendation, clinical decision support, and medication pattern\nmining and measurement. Relation extractions between medication mentions and\ntemporal information can further help clinicians better understand the\npatients' treatment history. To evaluate the performances of deep learning (DL)\nand large language models (LLMs) in medication extraction and temporal\nrelations classification, we carry out an empirical investigation of\n\\textbf{MedTem} project using several advanced learning structures including\nBiLSTM-CRF and CNN-BiLSTM for a clinical domain named entity recognition (NER),\nand BERT-CNN for temporal relation extraction (RE), in addition to the\nexploration of different word embedding techniques. Furthermore, we also\ndesigned a set of post-processing roles to generate structured output on\nmedications and the temporal relation. Our experiments show that CNN-BiLSTM\nslightly wins the BiLSTM-CRF model on the i2b2-2009 clinical NER task yielding\n75.67, 77.83, and 78.17 for precision, recall, and F1 scores using Macro\nAverage. BERT-CNN model also produced reasonable evaluation scores 64.48,\n67.17, and 65.03 for P/R/F1 using Macro Avg on the temporal relation extraction\ntest set from i2b2-2012 challenges. Code and Tools from MedTem will be hosted\nat \\url{https://github.com/HECTA-UoM/MedTem}\n", "date": "2023-10-03", "categories": []}
{"id": "2310.04595", "title": "Segmented Harmonic Loss: Handling Class-Imbalanced Multi-Label Clinical\n  Data for Medical Coding with Large Language Models", "abstract": "  The precipitous rise and adoption of Large Language Models (LLMs) have\nshattered expectations with the fastest adoption rate of any consumer-facing\ntechnology in history. Healthcare, a field that traditionally uses NLP\ntechniques, was bound to be affected by this meteoric rise. In this paper, we\ngauge the extent of the impact by evaluating the performance of LLMs for the\ntask of medical coding on real-life noisy data. We conducted several\nexperiments on MIMIC III and IV datasets with encoder-based LLMs, such as BERT.\nFurthermore, we developed Segmented Harmonic Loss, a new loss function to\naddress the extreme class imbalance that we found to prevail in most medical\ndata in a multi-label scenario by segmenting and decoupling co-occurring\nclasses of the dataset with a new segmentation algorithm. We also devised a\ntechnique based on embedding similarity to tackle noisy data. Our experimental\nresults show that when trained with the proposed loss, the LLMs achieve\nsignificant performance gains even on noisy long-tailed datasets, outperforming\nthe F1 score of the state-of-the-art by over ten percentage points.\n", "date": "2023-10-06", "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": "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": "2310.02451", "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": "2310.01708", "title": "Deciphering Diagnoses: How Large Language Models Explanations Influence\n  Clinical Decision Making", "abstract": "  Clinical Decision Support Systems (CDSS) utilize evidence-based knowledge and\npatient data to offer real-time recommendations, with Large Language Models\n(LLMs) emerging as a promising tool to generate plain-text explanations for\nmedical decisions. This study explores the effectiveness and reliability of\nLLMs in generating explanations for diagnoses based on patient complaints.\nThree experienced doctors evaluated LLM-generated explanations of the\nconnection between patient complaints and doctor and model-assigned diagnoses\nacross several stages. Experimental results demonstrated that LLM explanations\nsignificantly increased doctors' agreement rates with given diagnoses and\nhighlighted potential errors in LLM outputs, ranging from 5% to 30%. The study\nunderscores the potential and challenges of LLMs in healthcare and emphasizes\nthe need for careful integration and evaluation to ensure patient safety and\noptimal clinical utility.\n", "date": "2023-10-03", "categories": []}
{"id": "2310.01299", "title": "Generating Explanations in Medical Question-Answering by Expectation\n  Maximization Inference over Evidence", "abstract": "  Medical Question Answering~(medical QA) systems play an essential role in\nassisting healthcare workers in finding answers to their questions. However, it\nis not sufficient to merely provide answers by medical QA systems because users\nmight want explanations, that is, more analytic statements in natural language\nthat describe the elements and context that support the answer. To do so, we\npropose a novel approach for generating natural language explanations for\nanswers predicted by medical QA systems. As high-quality medical explanations\nrequire additional medical knowledge, so that our system extract knowledge from\nmedical textbooks to enhance the quality of explanations during the explanation\ngeneration process. Concretely, we designed an expectation-maximization\napproach that makes inferences about the evidence found in these texts,\noffering an efficient way to focus attention on lengthy evidence passages.\nExperimental results, conducted on two datasets MQAE-diag and MQAE, demonstrate\nthe effectiveness of our framework for reasoning with textual evidence. Our\napproach outperforms state-of-the-art models, achieving a significant\nimprovement of \\textbf{6.86} and \\textbf{9.43} percentage points on the Rouge-1\nscore; \\textbf{8.23} and \\textbf{7.82} percentage points on the Bleu-4 score on\nthe respective datasets.\n", "date": "2023-10-02", "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": "2309.15739", "title": "Experience and Evidence are the eyes of an excellent summarizer! Towards\n  Knowledge Infused Multi-modal Clinical Conversation Summarization", "abstract": "  With the advancement of telemedicine, both researchers and medical\npractitioners are working hand-in-hand to develop various techniques to\nautomate various medical operations, such as diagnosis report generation. In\nthis paper, we first present a multi-modal clinical conversation summary\ngeneration task that takes a clinician-patient interaction (both textual and\nvisual information) and generates a succinct synopsis of the conversation. We\npropose a knowledge-infused, multi-modal, multi-tasking medical domain\nidentification and clinical conversation summary generation\n(MM-CliConSummation) framework. It leverages an adapter to infuse knowledge and\nvisual features and unify the fused feature vector using a gated mechanism.\nFurthermore, we developed a multi-modal, multi-intent clinical conversation\nsummarization corpus annotated with intent, symptom, and summary. The extensive\nset of experiments, both quantitatively and qualitatively, led to the following\nfindings: (a) critical significance of visuals, (b) more precise and medical\nentity preserving summary with additional knowledge infusion, and (c) a\ncorrelation between medical department identification and clinical synopsis\ngeneration. Furthermore, the dataset and source code are available at\nhttps://github.com/NLP-RL/MM-CliConSummation.\n", "date": "2023-09-27", "categories": []}
{"id": "2309.12339", "title": "Considerations for health care institutions training large language\n  models on electronic health records", "abstract": "  Large language models (LLMs) like ChatGPT have excited scientists across\nfields; in medicine, one source of excitement is the potential applications of\nLLMs trained on electronic health record (EHR) data. But there are tough\nquestions we must first answer if health care institutions are interested in\nhaving LLMs trained on their own data; should they train an LLM from scratch or\nfine-tune it from an open-source model? For healthcare institutions with a\npredefined budget, what are the biggest LLMs they can afford? In this study, we\ntake steps towards answering these questions with an analysis on dataset sizes,\nmodel sizes, and costs for LLM training using EHR data. This analysis provides\na framework for thinking about these questions in terms of data scale, compute\nscale, and training budgets.\n", "date": "2023-08-24", "categories": []}
{"id": "2309.10608", "title": "Improving Medical Dialogue Generation with Abstract Meaning\n  Representations", "abstract": "  Medical Dialogue Generation serves a critical role in telemedicine by\nfacilitating the dissemination of medical expertise to patients. Existing\nstudies focus on incorporating textual representations, which have limited\ntheir ability to represent the semantics of text, such as ignoring important\nmedical entities. To enhance the model's understanding of the textual semantics\nand the medical knowledge including entities and relations, we introduce the\nuse of Abstract Meaning Representations (AMR) to construct graphical\nrepresentations that delineate the roles of language constituents and medical\nentities within the dialogues. In this paper, We propose a novel framework that\nmodels dialogues between patients and healthcare professionals using AMR\ngraphs, where the neural networks incorporate textual and graphical knowledge\nwith a dual attention mechanism. Experimental results show that our framework\noutperforms strong baseline models in medical dialogue generation,\ndemonstrating the effectiveness of AMR graphs in enhancing the representations\nof medical knowledge and logical relationships. Furthermore, to support future\nresearch in this domain, we provide the corresponding source code at\nhttps://github.com/Bernard-Yang/MedDiaAMR.\n", "date": "2023-09-19", "categories": []}
{"id": "2309.07812", "title": "Text Classification of Cancer Clinical Trial Eligibility Criteria", "abstract": "  Automatic identification of clinical trials for which a patient is eligible\nis complicated by the fact that trial eligibility is stated in natural\nlanguage. A potential solution to this problem is to employ text classification\nmethods for common types of eligibility criteria. In this study, we focus on\nseven common exclusion criteria in cancer trials: prior malignancy, human\nimmunodeficiency virus, hepatitis B, hepatitis C, psychiatric illness,\ndrug/substance abuse, and autoimmune illness. Our dataset consists of 764 phase\nIII cancer trials with these exclusions annotated at the trial level. We\nexperiment with common transformer models as well as a new pre-trained clinical\ntrial BERT model. Our results demonstrate the feasibility of automatically\nclassifying common exclusion criteria. Additionally, we demonstrate the value\nof a pre-trained language model specifically for clinical trials, which yields\nthe highest average performance across all criteria.\n", "date": "2023-09-14", "categories": []}
{"id": "2309.08868", "title": "MHLAT: Multi-hop Label-wise Attention Model for Automatic ICD Coding", "abstract": "  International Classification of Diseases (ICD) coding is the task of\nassigning ICD diagnosis codes to clinical notes. This can be challenging given\nthe large quantity of labels (nearly 9,000) and lengthy texts (up to 8,000\ntokens). However, unlike the single-pass reading process in previous works,\nhumans tend to read the text and label definitions again to get more confident\nanswers. Moreover, although pretrained language models have been used to\naddress these problems, they suffer from huge memory usage. To address the\nabove problems, we propose a simple but effective model called the Multi-Hop\nLabel-wise ATtention (MHLAT), in which multi-hop label-wise attention is\ndeployed to get more precise and informative representations. Extensive\nexperiments on three benchmark MIMIC datasets indicate that our method achieves\nsignificantly better or competitive performance on all seven metrics, with much\nfewer parameters to optimize.\n", "date": "2023-09-16", "categories": []}
{"id": "2309.08008", "title": "An Empirical Evaluation of Prompting Strategies for Large Language\n  Models in Zero-Shot Clinical Natural Language Processing", "abstract": "  Large language models (LLMs) have shown remarkable capabilities in Natural\nLanguage Processing (NLP), especially in domains where labeled data is scarce\nor expensive, such as clinical domain. However, to unlock the clinical\nknowledge hidden in these LLMs, we need to design effective prompts that can\nguide them to perform specific clinical NLP tasks without any task-specific\ntraining data. This is known as in-context learning, which is an art and\nscience that requires understanding the strengths and weaknesses of different\nLLMs and prompt engineering approaches. In this paper, we present a\ncomprehensive and systematic experimental study on prompt engineering for five\nclinical NLP tasks: Clinical Sense Disambiguation, Biomedical Evidence\nExtraction, Coreference Resolution, Medication Status Extraction, and\nMedication Attribute Extraction. We assessed the prompts proposed in recent\nliterature, including simple prefix, simple cloze, chain of thought, and\nanticipatory prompts, and introduced two new types of prompts, namely heuristic\nprompting and ensemble prompting. We evaluated the performance of these prompts\non three state-of-the-art LLMs: GPT-3.5, BARD, and LLAMA2. We also contrasted\nzero-shot prompting with few-shot prompting, and provide novel insights and\nguidelines for prompt engineering for LLMs in clinical NLP. To the best of our\nknowledge, this is one of the first works on the empirical evaluation of\ndifferent prompt engineering approaches for clinical NLP in this era of\ngenerative AI, and we hope that it will inspire and inform future research in\nthis area.\n", "date": "2023-09-14", "categories": []}
{"id": "2309.05475", "title": "Zero-shot Learning with Minimum Instruction to Extract Social\n  Determinants and Family History from Clinical Notes using GPT Model", "abstract": "  Demographics, Social determinants of health, and family history documented in\nthe unstructured text within the electronic health records are increasingly\nbeing studied to understand how this information can be utilized with the\nstructured data to improve healthcare outcomes. After the GPT models were\nreleased, many studies have applied GPT models to extract this information from\nthe narrative clinical notes. Different from the existing work, our research\nfocuses on investigating the zero-shot learning on extracting this information\ntogether by providing minimum information to the GPT model. We utilize\nde-identified real-world clinical notes annotated for demographics, various\nsocial determinants, and family history information. Given that the GPT model\nmight provide text different from the text in the original data, we explore two\nsets of evaluation metrics, including the traditional NER evaluation metrics\nand semantic similarity evaluation metrics, to completely understand the\nperformance. Our results show that the GPT-3.5 method achieved an average of\n0.975 F1 on demographics extraction, 0.615 F1 on social determinants\nextraction, and 0.722 F1 on family history extraction. We believe these results\ncan be further improved through model fine-tuning or few-shots learning.\nThrough the case studies, we also identified the limitations of the GPT models,\nwhich need to be addressed in future research.\n", "date": "2023-09-11", "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": "2212.05857", "title": "Automated ICD Coding using Extreme Multi-label Long Text\n  Transformer-based Models", "abstract": "  Background: Encouraged by the success of pretrained Transformer models in\nmany natural language processing tasks, their use for International\nClassification of Diseases (ICD) coding tasks is now actively being explored.\nIn this study, we investigate three types of Transformer-based models, aiming\nto address the extreme label set and long text classification challenges that\nare posed by automated ICD coding tasks. Methods: The Transformer-based model\nPLM-ICD achieved the current state-of-the-art (SOTA) performance on the ICD\ncoding benchmark dataset MIMIC-III. It was chosen as our baseline model to be\nfurther optimised. XR-Transformer, the new SOTA model in the general extreme\nmulti-label text classification domain, and XR-LAT, a novel adaptation of the\nXR-Transformer model, were also trained on the MIMIC-III dataset. XR-LAT is a\nrecursively trained model chain on a predefined hierarchical code tree with\nlabel-wise attention, knowledge transferring and dynamic negative sampling\nmechanisms. Results: Our optimised PLM-ICD model, which was trained with longer\ntotal and chunk sequence lengths, significantly outperformed the current SOTA\nPLM-ICD model, and achieved the highest micro-F1 score of 60.8%. The\nXR-Transformer model, although SOTA in the general domain, did not perform well\nacross all metrics. The best XR-LAT based model obtained results that were\ncompetitive with the current SOTA PLM-ICD model, including improving the\nmacro-AUC by 2.1%. Conclusion: Our optimised PLM-ICD model is the new SOTA\nmodel for automated ICD coding on the MIMIC-III dataset, while our novel XR-LAT\nmodel performs competitively with the previous SOTA PLM-ICD model.\n", "date": "2022-12-12", "categories": []}
{"id": "2309.06419", "title": "Radiology-Llama2: Best-in-Class Large Language Model for Radiology", "abstract": "  This paper introduces Radiology-Llama2, a large language model specialized\nfor radiology through a process known as instruction tuning. Radiology-Llama2\nis based on the Llama2 architecture and further trained on a large dataset of\nradiology reports to generate coherent and clinically useful impressions from\nradiological findings. Quantitative evaluations using ROUGE metrics on the\nMIMIC-CXR and OpenI datasets demonstrate that Radiology-Llama2 achieves\nstate-of-the-art performance compared to other generative language models, with\na Rouge-1 score of 0.4834 on MIMIC-CXR and 0.4185 on OpenI. Additional\nassessments by radiology experts highlight the model's strengths in\nunderstandability, coherence, relevance, conciseness, and clinical utility. The\nwork illustrates the potential of localized language models designed and tuned\nfor specialized domains like radiology. When properly evaluated and deployed,\nsuch models can transform fields like radiology by automating rote tasks and\nenhancing human expertise.\n", "date": "2023-08-29", "categories": []}
{"id": "2309.04607", "title": "Linking Symptom Inventories using Semantic Textual Similarity", "abstract": "  An extensive library of symptom inventories has been developed over time to\nmeasure clinical symptoms, but this variety has led to several long standing\nissues. Most notably, results drawn from different settings and studies are not\ncomparable, which limits reproducibility. Here, we present an artificial\nintelligence (AI) approach using semantic textual similarity (STS) to link\nsymptoms and scores across previously incongruous symptom inventories. We\ntested the ability of four pre-trained STS models to screen thousands of\nsymptom description pairs for related content - a challenging task typically\nrequiring expert panels. Models were tasked to predict symptom severity across\nfour different inventories for 6,607 participants drawn from 16 international\ndata sources. The STS approach achieved 74.8% accuracy across five tasks,\noutperforming other models tested. This work suggests that incorporating\ncontextual, semantic information can assist expert decision-making processes,\nyielding gains for both general and disease-specific clinical assessment.\n", "date": "2023-09-08", "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": "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": "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": "2308.13710", "title": "WellXplain: Wellness Concept Extraction and Classification in Reddit\n  Posts for Mental Health Analysis", "abstract": "  During the current mental health crisis, the importance of identifying\npotential indicators of mental issues from social media content has surged.\nOverlooking the multifaceted nature of mental and social well-being can have\ndetrimental effects on one's mental state. In traditional therapy sessions,\nprofessionals manually pinpoint the origins and outcomes of underlying mental\nchallenges, a process both detailed and time-intensive. We introduce an\napproach to this intricate mental health analysis by framing the identification\nof wellness dimensions in Reddit content as a wellness concept extraction and\ncategorization challenge. We've curated a unique dataset named WELLXPLAIN,\ncomprising 3,092 entries and totaling 72,813 words. Drawing from Halbert L.\nDunn's well-regarded wellness theory, our team formulated an annotation\nframework along with guidelines. This dataset also includes human-marked\ntextual segments, offering clear reasoning for decisions made in the wellness\nconcept categorization process. Our aim in publishing this dataset and\nanalyzing initial benchmarks is to spearhead the creation of advanced language\nmodels tailored for healthcare-focused concept extraction and categorization.\n", "date": "2023-08-25", "categories": []}
{"id": "2304.14454", "title": "PMC-LLaMA: Towards Building Open-source Language Models for Medicine", "abstract": "  Recently, Large Language Models (LLMs) have showcased remarkable capabilities\nin natural language understanding. While demonstrating proficiency in everyday\nconversations and question-answering situations, these models frequently\nstruggle in domains that require precision, such as medical applications, due\nto their lack of domain-specific knowledge. In this paper, we describe the\nprocedure for building a powerful, open-source language model specifically\ndesigned for medicine applications, termed as PMC-LLaMA. Our contributions are\nthreefold: (i) we systematically investigate the process of adapting a\ngeneral-purpose foundation language model towards medical domain, this involves\ndata-centric knowledge injection through the integration of 4.8M biomedical\nacademic papers and 30K medical textbooks, as well as comprehensive fine-tuning\nfor alignment with domain-specific instructions; (ii) we contribute a\nlarge-scale, comprehensive dataset for instruction tuning. This dataset\nencompasses medical question-answering (QA), rationale for reasoning, and\nconversational dialogues, comprising a total of 202M tokens; (iii) we conduct\nthorough ablation studies to demonstrate the effectiveness of each proposed\ncomponent. While evaluating on various public medical question-answering\nbenchmarks, our lightweight PMCLLaMA, which consists of only 13 billion\nparameters, exhibits superior performance, even surpassing ChatGPT. All models,\ncodes, datasets can be found in https://github.com/chaoyi-wu/PMC-LLaMA.\n", "date": "2023-04-27", "categories": []}
{"id": "2306.04504", "title": "Evaluation of ChatGPT on Biomedical Tasks: A Zero-Shot Comparison with\n  Fine-Tuned Generative Transformers", "abstract": "  ChatGPT is a large language model developed by OpenAI. Despite its impressive\nperformance across various tasks, no prior work has investigated its capability\nin the biomedical domain yet. To this end, this paper aims to evaluate the\nperformance of ChatGPT on various benchmark biomedical tasks, such as relation\nextraction, document classification, question answering, and summarization. To\nthe best of our knowledge, this is the first work that conducts an extensive\nevaluation of ChatGPT in the biomedical domain. Interestingly, we find based on\nour evaluation that in biomedical datasets that have smaller training sets,\nzero-shot ChatGPT even outperforms the state-of-the-art fine-tuned generative\ntransformer models, such as BioGPT and BioBART. This suggests that ChatGPT's\npre-training on large text corpora makes it quite specialized even in the\nbiomedical domain. Our findings demonstrate that ChatGPT has the potential to\nbe a valuable tool for various tasks in the biomedical domain that lack large\nannotated data.\n", "date": "2023-06-07", "categories": []}
{"id": "2308.12025", "title": "Knowledge-injected Prompt Learning for Chinese Biomedical Entity\n  Normalization", "abstract": "  The Biomedical Entity Normalization (BEN) task aims to align raw,\nunstructured medical entities to standard entities, thus promoting data\ncoherence and facilitating better downstream medical applications. Recently,\nprompt learning methods have shown promising results in this task. However,\nexisting research falls short in tackling the more complex Chinese BEN task,\nespecially in the few-shot scenario with limited medical data, and the vast\npotential of the external medical knowledge base has yet to be fully harnessed.\nTo address these challenges, we propose a novel Knowledge-injected Prompt\nLearning (PL-Knowledge) method. Specifically, our approach consists of five\nstages: candidate entity matching, knowledge extraction, knowledge encoding,\nknowledge injection, and prediction output. By effectively encoding the\nknowledge items contained in medical entities and incorporating them into our\ntailor-made knowledge-injected templates, the additional knowledge enhances the\nmodel's ability to capture latent relationships between medical entities, thus\nachieving a better match with the standard entities. We extensively evaluate\nour model on a benchmark dataset in both few-shot and full-scale scenarios. Our\nmethod outperforms existing baselines, with an average accuracy boost of\n12.96\\% in few-shot and 0.94\\% in full-data cases, showcasing its excellence in\nthe BEN task.\n", "date": "2023-08-23", "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": "2308.03253", "title": "PaniniQA: Enhancing Patient Education Through Interactive Question\n  Answering", "abstract": "  Patient portal allows discharged patients to access their personalized\ndischarge instructions in electronic health records (EHRs). However, many\npatients have difficulty understanding or memorizing their discharge\ninstructions. In this paper, we present PaniniQA, a patient-centric interactive\nquestion answering system designed to help patients understand their discharge\ninstructions. PaniniQA first identifies important clinical content from\npatients' discharge instructions and then formulates patient-specific\neducational questions. In addition, PaniniQA is also equipped with answer\nverification functionality to provide timely feedback to correct patients'\nmisunderstandings. Our comprehensive automatic and human evaluation results\ndemonstrate our PaniniQA is capable of improving patients' mastery of their\nmedical instructions through effective interactions\n", "date": "2023-08-07", "categories": []}
{"id": "2308.10758", "title": "DepreSym: A Depression Symptom Annotated Corpus and the Role of LLMs as\n  Assessors of Psychological Markers", "abstract": "  Computational methods for depression detection aim to mine traces of\ndepression from online publications posted by Internet users. However,\nsolutions trained on existing collections exhibit limited generalisation and\ninterpretability. To tackle these issues, recent studies have shown that\nidentifying depressive symptoms can lead to more robust models. The eRisk\ninitiative fosters research on this area and has recently proposed a new\nranking task focused on developing search methods to find sentences related to\ndepressive symptoms. This search challenge relies on the symptoms specified by\nthe Beck Depression Inventory-II (BDI-II), a questionnaire widely used in\nclinical practice. Based on the participant systems' results, we present the\nDepreSym dataset, consisting of 21580 sentences annotated according to their\nrelevance to the 21 BDI-II symptoms. The labelled sentences come from a pool of\ndiverse ranking methods, and the final dataset serves as a valuable resource\nfor advancing the development of models that incorporate depressive markers\nsuch as clinical symptoms. Due to the complex nature of this relevance\nannotation, we designed a robust assessment methodology carried out by three\nexpert assessors (including an expert psychologist). Additionally, we explore\nhere the feasibility of employing recent Large Language Models (ChatGPT and\nGPT4) as potential assessors in this complex task. We undertake a comprehensive\nexamination of their performance, determine their main limitations and analyze\ntheir role as a complement or replacement for human annotators.\n", "date": "2023-08-21", "categories": []}
{"id": "2305.12031", "title": "Clinical Camel: An Open Expert-Level Medical Language Model with\n  Dialogue-Based Knowledge Encoding", "abstract": "  We present Clinical Camel, an open large language model (LLM) explicitly\ntailored for clinical research. Fine-tuned from LLaMA-2 using QLoRA, Clinical\nCamel achieves state-of-the-art performance across medical benchmarks among\nopenly available medical LLMs. Leveraging efficient single-GPU training,\nClinical Camel surpasses GPT-3.5 in five-shot evaluations on all assessed\nbenchmarks, including 64.3% on the USMLE Sample Exam (compared to 58.5% for\nGPT-3.5), 77.9% on PubMedQA (compared to 60.2%), 60.7% on MedQA (compared to\n53.6%), and 54.2% on MedMCQA (compared to 51.0%). In addition to these\nbenchmarks, Clinical Camel demonstrates its broader capabilities, such as\nsynthesizing plausible clinical notes. This work introduces dialogue-based\nknowledge encoding, a novel method to synthesize conversational data from dense\nmedical texts. While benchmark results are encouraging, extensive and rigorous\nhuman evaluation across diverse clinical scenarios is imperative to ascertain\nsafety before implementation. By openly sharing Clinical Camel, we hope to\nfoster transparent and collaborative research, working towards the safe\nintegration of LLMs within the healthcare domain. Significant challenges\nconcerning reliability, bias, and the potential for outdated knowledge persist.\nNonetheless, the transparency provided by an open approach reinforces the\nscientific rigor essential for future clinical applications.\n", "date": "2023-05-19", "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": "2308.08827", "title": "Factuality Detection using Machine Translation -- a Use Case for German\n  Clinical Text", "abstract": "  Factuality can play an important role when automatically processing clinical\ntext, as it makes a difference if particular symptoms are explicitly not\npresent, possibly present, not mentioned, or affirmed. In most cases, a\nsufficient number of examples is necessary to handle such phenomena in a\nsupervised machine learning setting. However, as clinical text might contain\nsensitive information, data cannot be easily shared. In the context of\nfactuality detection, this work presents a simple solution using machine\ntranslation to translate English data to German to train a transformer-based\nfactuality detection model.\n", "date": "2023-08-17", "categories": []}
{"id": "2308.09329", "title": "KESDT: knowledge enhanced shallow and deep Transformer for detecting\n  adverse drug reactions", "abstract": "  Adverse drug reaction (ADR) detection is an essential task in the medical\nfield, as ADRs have a gravely detrimental impact on patients' health and the\nhealthcare system. Due to a large number of people sharing information on\nsocial media platforms, an increasing number of efforts focus on social media\ndata to carry out effective ADR detection. Despite having achieved impressive\nperformance, the existing methods of ADR detection still suffer from three main\nchallenges. Firstly, researchers have consistently ignored the interaction\nbetween domain keywords and other words in the sentence. Secondly, social media\ndatasets suffer from the challenges of low annotated data. Thirdly, the issue\nof sample imbalance is commonly observed in social media datasets. To solve\nthese challenges, we propose the Knowledge Enhanced Shallow and Deep\nTransformer(KESDT) model for ADR detection. Specifically, to cope with the\nfirst issue, we incorporate the domain keywords into the Transformer model\nthrough a shallow fusion manner, which enables the model to fully exploit the\ninteractive relationships between domain keywords and other words in the\nsentence. To overcome the low annotated data, we integrate the synonym sets\ninto the Transformer model through a deep fusion manner, which expands the size\nof the samples. To mitigate the impact of sample imbalance, we replace the\nstandard cross entropy loss function with the focal loss function for effective\nmodel training. We conduct extensive experiments on three public datasets\nincluding TwiMed, Twitter, and CADEC. The proposed KESDT outperforms\nstate-of-the-art baselines on F1 values, with relative improvements of 4.87%,\n47.83%, and 5.73% respectively, which demonstrates the effectiveness of our\nproposed KESDT.\n", "date": "2023-08-18", "categories": []}
{"id": "2308.08494", "title": "Conceptualizing Machine Learning for Dynamic Information Retrieval of\n  Electronic Health Record Notes", "abstract": "  The large amount of time clinicians spend sifting through patient notes and\ndocumenting in electronic health records (EHRs) is a leading cause of clinician\nburnout. By proactively and dynamically retrieving relevant notes during the\ndocumentation process, we can reduce the effort required to find relevant\npatient history. In this work, we conceptualize the use of EHR audit logs for\nmachine learning as a source of supervision of note relevance in a specific\nclinical context, at a particular point in time. Our evaluation focuses on the\ndynamic retrieval in the emergency department, a high acuity setting with\nunique patterns of information retrieval and note writing. We show that our\nmethods can achieve an AUC of 0.963 for predicting which notes will be read in\nan individual note writing session. We additionally conduct a user study with\nseveral clinicians and find that our framework can help clinicians retrieve\nrelevant information more efficiently. Demonstrating that our framework and\nmethods can perform well in this demanding setting is a promising proof of\nconcept that they will translate to other clinical settings and data modalities\n(e.g., labs, medications, imaging).\n", "date": "2023-08-09", "categories": []}
{"id": "2304.06203", "title": "LeafAI: query generator for clinical cohort discovery rivaling a human\n  programmer", "abstract": "  Objective: Identifying study-eligible patients within clinical databases is a\ncritical step in clinical research. However, accurate query design typically\nrequires extensive technical and biomedical expertise. We sought to create a\nsystem capable of generating data model-agnostic queries while also providing\nnovel logical reasoning capabilities for complex clinical trial eligibility\ncriteria.\n  Materials and Methods: The task of query creation from eligibility criteria\nrequires solving several text-processing problems, including named entity\nrecognition and relation extraction, sequence-to-sequence transformation,\nnormalization, and reasoning. We incorporated hybrid deep learning and\nrule-based modules for these, as well as a knowledge base of the Unified\nMedical Language System (UMLS) and linked ontologies. To enable data-model\nagnostic query creation, we introduce a novel method for tagging database\nschema elements using UMLS concepts. To evaluate our system, called LeafAI, we\ncompared the capability of LeafAI to a human database programmer to identify\npatients who had been enrolled in 8 clinical trials conducted at our\ninstitution. We measured performance by the number of actual enrolled patients\nmatched by generated queries.\n  Results: LeafAI matched a mean 43% of enrolled patients with 27,225 eligible\nacross 8 clinical trials, compared to 27% matched and 14,587 eligible in\nqueries by a human database programmer. The human programmer spent 26 total\nhours crafting queries compared to several minutes by LeafAI.\n  Conclusions: Our work contributes a state-of-the-art data model-agnostic\nquery generation system capable of conditional reasoning using a knowledge\nbase. We demonstrate that LeafAI can rival an experienced human programmer in\nfinding patients eligible for clinical trials.\n", "date": "2023-04-13", "categories": []}
{"id": "2308.06546", "title": "MC-DRE: Multi-Aspect Cross Integration for Drug Event/Entity Extraction", "abstract": "  Extracting meaningful drug-related information chunks, such as adverse drug\nevents (ADE), is crucial for preventing morbidity and saving many lives. Most\nADEs are reported via an unstructured conversation with the medical context, so\napplying a general entity recognition approach is not sufficient enough. In\nthis paper, we propose a new multi-aspect cross-integration framework for drug\nentity/event detection by capturing and aligning different\ncontext/language/knowledge properties from drug-related documents. We first\nconstruct multi-aspect encoders to describe semantic, syntactic, and medical\ndocument contextual information by conducting those slot tagging tasks, main\ndrug entity/event detection, part-of-speech tagging, and general medical named\nentity recognition. Then, each encoder conducts cross-integration with other\ncontextual information in three ways: the key-value cross, attention cross, and\nfeedforward cross, so the multi-encoders are integrated in depth. Our model\noutperforms all SOTA on two widely used tasks, flat entity detection and\ndiscontinuous event extraction.\n", "date": "2023-08-12", "categories": []}
{"id": "2308.07359", "title": "Improving ICD-based semantic similarity by accounting for varying\n  degrees of comorbidity", "abstract": "  Finding similar patients is a common objective in precision medicine,\nfacilitating treatment outcome assessment and clinical decision support.\nChoosing widely-available patient features and appropriate mathematical methods\nfor similarity calculations is crucial. International Statistical\nClassification of Diseases and Related Health Problems (ICD) codes are used\nworldwide to encode diseases and are available for nearly all patients.\nAggregated as sets consisting of primary and secondary diagnoses they can\ndisplay a degree of comorbidity and reveal comorbidity patterns. It is possible\nto compute the similarity of patients based on their ICD codes by using\nsemantic similarity algorithms. These algorithms have been traditionally\nevaluated using a single-term expert rated data set.\n  However, real-word patient data often display varying degrees of documented\ncomorbidities that might impair algorithm performance. To account for this, we\npresent a scale term that considers documented comorbidity-variance. In this\nwork, we compared the performance of 80 combinations of established algorithms\nin terms of semantic similarity based on ICD-code sets. The sets have been\nextracted from patients with a C25.X (pancreatic cancer) primary diagnosis and\nprovide a variety of different combinations of ICD-codes. Using our scale term\nwe yielded the best results with a combination of level-based information\ncontent, Leacock & Chodorow concept similarity and bipartite graph matching for\nthe set similarities reaching a correlation of 0.75 with our expert's ground\ntruth. Our results highlight the importance of accounting for comorbidity\nvariance while demonstrating how well current semantic similarity algorithms\nperform.\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": "2308.06199", "title": "Weakly Supervised Text Classification on Free Text Comments in\n  Patient-Reported Outcome Measures", "abstract": "  Free text comments (FTC) in patient-reported outcome measures (PROMs) data\nare typically analysed using manual methods, such as content analysis, which is\nlabour-intensive and time-consuming. Machine learning analysis methods are\nlargely unsupervised, necessitating post-analysis interpretation. Weakly\nsupervised text classification (WSTC) can be a valuable method of analysis to\nclassify domain-specific text data in which there is limited labelled data. In\nthis paper, we apply five WSTC techniques to FTC in PROMs data to identify\nhealth-related quality of life (HRQoL) themes reported by colorectal cancer\npatients. The WSTC methods label all the themes mentioned in the FTC. The\nresults showed moderate performance on the PROMs data, mainly due to the\nprecision of the models, and variation between themes. Evaluation of the\nclassification performance illustrated the potential and limitations of keyword\nbased WSTC to label PROMs FTC when labelled data is limited.\n", "date": "2023-08-11", "categories": []}
{"id": "2308.03629", "title": "MedMine: Examining Pre-trained Language Models on Medication Mining", "abstract": "  Automatic medication mining from clinical and biomedical text has become a\npopular topic due to its real impact on healthcare applications and the recent\ndevelopment of powerful language models (LMs). However, fully-automatic\nextraction models still face obstacles to be overcome such that they can be\ndeployed directly into clinical practice for better impacts. Such obstacles\ninclude their imbalanced performances on different entity types and clinical\nevents. In this work, we examine current state-of-the-art pre-trained language\nmodels (PLMs) on such tasks, via fine-tuning including the monolingual model\nMed7 and multilingual large language model (LLM) XLM-RoBERTa. We compare their\nadvantages and drawbacks using historical medication mining shared task data\nsets from n2c2-2018 challenges. We report the findings we get from these\nfine-tuning experiments such that they can facilitate future research on\naddressing them, for instance, how to combine their outputs, merge such models,\nor improve their overall accuracy by ensemble learning and data augmentation.\nMedMine is part of the M3 Initiative \\url{https://github.com/HECTA-UoM/M3}\n", "date": "2023-08-07", "categories": []}
{"id": "2308.03782", "title": "Bio+Clinical BERT, BERT Base, and CNN Performance Comparison for\n  Predicting Drug-Review Satisfaction", "abstract": "  The objective of this study is to develop natural language processing (NLP)\nmodels that can analyze patients' drug reviews and accurately classify their\nsatisfaction levels as positive, neutral, or negative. Such models would reduce\nthe workload of healthcare professionals and provide greater insight into\npatients' quality of life, which is a critical indicator of treatment\neffectiveness. To achieve this, we implemented and evaluated several\nclassification models, including a BERT base model, Bio+Clinical BERT, and a\nsimpler CNN. Results indicate that the medical domain-specific Bio+Clinical\nBERT model significantly outperformed the general domain base BERT model,\nachieving macro f1 and recall score improvement of 11%, as shown in Table 2.\nFuture research could explore how to capitalize on the specific strengths of\neach model. Bio+Clinical BERT excels in overall performance, particularly with\nmedical jargon, while the simpler CNN demonstrates the ability to identify\ncrucial words and accurately classify sentiment in texts with conflicting\nsentiments.\n", "date": "2023-08-02", "categories": []}
{"id": "2303.16756", "title": "Large Language Models for Healthcare Data Augmentation: An Example on\n  Patient-Trial Matching", "abstract": "  The process of matching patients with suitable clinical trials is essential\nfor advancing medical research and providing optimal care. However, current\napproaches face challenges such as data standardization, ethical\nconsiderations, and a lack of interoperability between Electronic Health\nRecords (EHRs) and clinical trial criteria. In this paper, we explore the\npotential of large language models (LLMs) to address these challenges by\nleveraging their advanced natural language generation capabilities to improve\ncompatibility between EHRs and clinical trial descriptions. We propose an\ninnovative privacy-aware data augmentation approach for LLM-based patient-trial\nmatching (LLM-PTM), which balances the benefits of LLMs while ensuring the\nsecurity and confidentiality of sensitive patient data. Our experiments\ndemonstrate a 7.32% average improvement in performance using the proposed\nLLM-PTM method, and the generalizability to new data is improved by 12.12%.\nAdditionally, we present case studies to further illustrate the effectiveness\nof our approach and provide a deeper understanding of its underlying\nprinciples.\n", "date": "2023-03-24", "categories": []}
{"id": "2304.08999", "title": "A Biomedical Entity Extraction Pipeline for Oncology Health Records in\n  Portuguese", "abstract": "  Textual health records of cancer patients are usually protracted and highly\nunstructured, making it very time-consuming for health professionals to get a\ncomplete overview of the patient's therapeutic course. As such limitations can\nlead to suboptimal and/or inefficient treatment procedures, healthcare\nproviders would greatly benefit from a system that effectively summarizes the\ninformation of those records. With the advent of deep neural models, this\nobjective has been partially attained for English clinical texts, however, the\nresearch community still lacks an effective solution for languages with limited\nresources. In this paper, we present the approach we developed to extract\nprocedures, drugs, and diseases from oncology health records written in\nEuropean Portuguese. This project was conducted in collaboration with the\nPortuguese Institute for Oncology which, besides holding over $10$ years of\nduly protected medical records, also provided oncologist expertise throughout\nthe development of the project. Since there is no annotated corpus for\nbiomedical entity extraction in Portuguese, we also present the strategy we\nfollowed in annotating the corpus for the development of the models. The final\nmodels, which combined a neural architecture with entity linking, achieved\n$F_1$ scores of $88.6$, $95.0$, and $55.8$ per cent in the mention extraction\nof procedures, drugs, and diseases, respectively.\n", "date": "2023-04-18", "categories": []}
{"id": "2308.03360", "title": "Coupling Symbolic Reasoning with Language Modeling for Efficient\n  Longitudinal Understanding of Unstructured Electronic Medical Records", "abstract": "  The application of Artificial Intelligence (AI) in healthcare has been\nrevolutionary, especially with the recent advancements in transformer-based\nLarge Language Models (LLMs). However, the task of understanding unstructured\nelectronic medical records remains a challenge given the nature of the records\n(e.g., disorganization, inconsistency, and redundancy) and the inability of\nLLMs to derive reasoning paradigms that allow for comprehensive understanding\nof medical variables. In this work, we examine the power of coupling symbolic\nreasoning with language modeling toward improved understanding of unstructured\nclinical texts. We show that such a combination improves the extraction of\nseveral medical variables from unstructured records. In addition, we show that\nthe state-of-the-art commercially-free LLMs enjoy retrieval capabilities\ncomparable to those provided by their commercial counterparts. Finally, we\nelaborate on the need for LLM steering through the application of symbolic\nreasoning as the exclusive use of LLMs results in the lowest performance.\n", "date": "2023-08-07", "categories": []}
{"id": "2303.13035", "title": "SPeC: A Soft Prompt-Based Calibration on Performance Variability of\n  Large Language Model in Clinical Notes Summarization", "abstract": "  Electronic health records (EHRs) store an extensive array of patient\ninformation, encompassing medical histories, diagnoses, treatments, and test\noutcomes. These records are crucial for enabling healthcare providers to make\nwell-informed decisions regarding patient care. Summarizing clinical notes\nfurther assists healthcare professionals in pinpointing potential health risks\nand making better-informed decisions. This process contributes to reducing\nerrors and enhancing patient outcomes by ensuring providers have access to the\nmost pertinent and current patient data. Recent research has shown that\nincorporating prompts with large language models (LLMs) substantially boosts\nthe efficacy of summarization tasks. However, we show that this approach also\nleads to increased output variance, resulting in notably divergent outputs even\nwhen prompts share similar meanings. To tackle this challenge, we introduce a\nmodel-agnostic Soft Prompt-Based Calibration (SPeC) pipeline that employs soft\nprompts to diminish variance while preserving the advantages of prompt-based\nsummarization. Experimental findings on multiple clinical note tasks and LLMs\nindicate that our method not only bolsters performance but also effectively\ncurbs variance for various LLMs, providing a more uniform and dependable\nsolution for summarizing vital medical information.\n", "date": "2023-03-23", "categories": []}
{"id": "2308.02199", "title": "A Survey of Spanish Clinical Language Models", "abstract": "  This survey focuses in encoder Language Models for solving tasks in the\nclinical domain in the Spanish language. We review the contributions of 17\ncorpora focused mainly in clinical tasks, then list the most relevant Spanish\nLanguage Models and Spanish Clinical Language models. We perform a thorough\ncomparison of these models by benchmarking them over a curated subset of the\navailable corpora, in order to find the best-performing ones; in total more\nthan 3000 models were fine-tuned for this study. All the tested corpora and the\nbest models are made publically available in an accessible way, so that the\nresults can be reproduced by independent teams or challenged in the future when\nnew Spanish Clinical Language models are created.\n", "date": "2023-08-04", "categories": []}
{"id": "2308.01666", "title": "Evaluating ChatGPT text-mining of clinical records for obesity\n  monitoring", "abstract": "  Background: Veterinary clinical narratives remain a largely untapped resource\nfor addressing complex diseases. Here we compare the ability of a large\nlanguage model (ChatGPT) and a previously developed regular expression (RegexT)\nto identify overweight body condition scores (BCS) in veterinary narratives.\nMethods: BCS values were extracted from 4,415 anonymised clinical narratives\nusing either RegexT or by appending the narrative to a prompt sent to ChatGPT\ncoercing the model to return the BCS information. Data were manually reviewed\nfor comparison. Results: The precision of RegexT was higher (100%, 95% CI\n94.81-100%) than the ChatGPT (89.3%; 95% CI82.75-93.64%). However, the recall\nof ChatGPT (100%. 95% CI 96.18-100%) was considerably higher than that of\nRegexT (72.6%, 95% CI 63.92-79.94%). Limitations: Subtle prompt engineering is\nneeded to improve ChatGPT output. Conclusions: Large language models create\ndiverse opportunities and, whilst complex, present an intuitive interface to\ninformation but require careful implementation to avoid unpredictable errors.\n", "date": "2023-08-03", "categories": []}
{"id": "2308.01727", "title": "Local Large Language Models for Complex Structured Medical Tasks", "abstract": "  This paper introduces an approach that combines the language reasoning\ncapabilities of large language models (LLMs) with the benefits of local\ntraining to tackle complex, domain-specific tasks. Specifically, the authors\ndemonstrate their approach by extracting structured condition codes from\npathology reports. The proposed approach utilizes local LLMs, which can be\nfine-tuned to respond to specific generative instructions and provide\nstructured outputs. The authors collected a dataset of over 150k uncurated\nsurgical pathology reports, containing gross descriptions, final diagnoses, and\ncondition codes. They trained different model architectures, including LLaMA,\nBERT and LongFormer and evaluated their performance. The results show that the\nLLaMA-based models significantly outperform BERT-style models across all\nevaluated metrics, even with extremely reduced precision. The LLaMA models\nperformed especially well with large datasets, demonstrating their ability to\nhandle complex, multi-label tasks. Overall, this work presents an effective\napproach for utilizing LLMs to perform domain-specific tasks using accessible\nhardware, with potential applications in the medical domain, where complex data\nextraction and classification are required.\n", "date": "2023-08-03", "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": "2307.14522", "title": "CliniDigest: A Case Study in Large Language Model Based Large-Scale\n  Summarization of Clinical Trial Descriptions", "abstract": "  A clinical trial is a study that evaluates new biomedical interventions. To\ndesign new trials, researchers draw inspiration from those current and\ncompleted. In 2022, there were on average more than 100 clinical trials\nsubmitted to ClinicalTrials.gov every day, with each trial having a mean of\napproximately 1500 words [1]. This makes it nearly impossible to keep up to\ndate. To mitigate this issue, we have created a batch clinical trial summarizer\ncalled CliniDigest using GPT-3.5. CliniDigest is, to our knowledge, the first\ntool able to provide real-time, truthful, and comprehensive summaries of\nclinical trials. CliniDigest can reduce up to 85 clinical trial descriptions\n(approximately 10,500 words) into a concise 200-word summary with references\nand limited hallucinations. We have tested CliniDigest on its ability to\nsummarize 457 trials divided across 27 medical subdomains. For each field,\nCliniDigest generates summaries of $\\mu=153,\\ \\sigma=69 $ words, each of which\nutilizes $\\mu=54\\%,\\ \\sigma=30\\% $ of the sources. A more comprehensive\nevaluation is planned and outlined in this paper.\n", "date": "2023-07-26", "categories": []}
{"id": "2308.00107", "title": "Validation of a Zero-Shot Learning Natural Language Processing Tool for\n  Data Abstraction from Unstructured Healthcare Data", "abstract": "  Objectives: To describe the development and validation of a zero-shot\nlearning natural language processing (NLP) tool for abstracting data from\nunstructured text contained within PDF documents, such as those found within\nelectronic health records. Materials and Methods: A data abstraction tool based\non the GPT-3.5 model from OpenAI was developed and compared to three physician\nhuman abstractors in terms of time to task completion and accuracy for\nabstracting data on 14 unique variables from a set of 199 de-identified radical\nprostatectomy pathology reports. The reports were processed by the software\ntool in vectorized and scanned formats to establish the impact of optical\ncharacter recognition on data abstraction. The tool was assessed for\nsuperiority for data abstraction speed and non-inferiority for accuracy.\nResults: The human abstractors required a mean of 101s per report for data\nabstraction, with times varying from 15 to 284 s. In comparison, the software\ntool required a mean of 12.8 s to process the vectorized reports and a mean of\n15.8 to process the scanned reports (P < 0.001). The overall accuracies of the\nthree human abstractors were 94.7%, 97.8%, and 96.4% for the combined set of\n2786 datapoints. The software tool had an overall accuracy of 94.2% for the\nvectorized reports, proving to be non-inferior to the human abstractors at a\nmargin of -10% ($\\alpha$=0.025). The tool had a slightly lower accuracy of\n88.7% using the scanned reports, proving to be non-inferiority to 2 out of 3\nhuman abstractors. Conclusion: The developed zero-shot learning NLP tool\naffords researchers comparable levels of accuracy to that of human abstractors,\nwith significant time savings benefits. Because of the lack of need for\ntask-specific model training, the developed tool is highly generalizable and\ncan be used for a wide variety of data abstraction tasks, even outside the\nfield of medicine.\n", "date": "2023-07-23", "categories": []}
{"id": "2304.05368", "title": "Are Large Language Models Ready for Healthcare? A Comparative Study on\n  Clinical Language Understanding", "abstract": "  Large language models (LLMs) have made significant progress in various\ndomains, including healthcare. However, the specialized nature of clinical\nlanguage understanding tasks presents unique challenges and limitations that\nwarrant further investigation. In this study, we conduct a comprehensive\nevaluation of state-of-the-art LLMs, namely GPT-3.5, GPT-4, and Bard, within\nthe realm of clinical language understanding tasks. These tasks span a diverse\nrange, including named entity recognition, relation extraction, natural\nlanguage inference, semantic textual similarity, document classification, and\nquestion-answering. We also introduce a novel prompting strategy,\nself-questioning prompting (SQP), tailored to enhance LLMs' performance by\neliciting informative questions and answers pertinent to the clinical scenarios\nat hand. Our evaluation underscores the significance of task-specific learning\nstrategies and prompting techniques for improving LLMs' effectiveness in\nhealthcare-related tasks. Additionally, our in-depth error analysis on the\nchallenging relation extraction task offers valuable insights into error\ndistribution and potential avenues for improvement using SQP. Our study sheds\nlight on the practical implications of employing LLMs in the specialized domain\nof healthcare, serving as a foundation for future research and the development\nof potential applications in healthcare settings.\n", "date": "2023-04-09", "categories": []}
{"id": "2307.12803", "title": "Guidance in Radiology Report Summarization: An Empirical Evaluation and\n  Error Analysis", "abstract": "  Automatically summarizing radiology reports into a concise impression can\nreduce the manual burden of clinicians and improve the consistency of\nreporting. Previous work aimed to enhance content selection and factuality\nthrough guided abstractive summarization. However, two key issues persist.\nFirst, current methods heavily rely on domain-specific resources to extract the\nguidance signal, limiting their transferability to domains and languages where\nthose resources are unavailable. Second, while automatic metrics like ROUGE\nshow progress, we lack a good understanding of the errors and failure modes in\nthis task. To bridge these gaps, we first propose a domain-agnostic guidance\nsignal in form of variable-length extractive summaries. Our empirical results\non two English benchmarks demonstrate that this guidance signal improves upon\nunguided summarization while being competitive with domain-specific methods.\nAdditionally, we run an expert evaluation of four systems according to a\ntaxonomy of 11 fine-grained errors. We find that the most pressing differences\nbetween automatic summaries and those of radiologists relate to content\nselection including omissions (up to 52%) and additions (up to 57%). We\nhypothesize that latent reporting factors and corpus-level inconsistencies may\nlimit models to reliably learn content selection from the available data,\npresenting promising directions for future work.\n", "date": "2023-07-24", "categories": []}
{"id": "2307.11346", "title": "CohortGPT: An Enhanced GPT for Participant Recruitment in Clinical Study", "abstract": "  Participant recruitment based on unstructured medical texts such as clinical\nnotes and radiology reports has been a challenging yet important task for the\ncohort establishment in clinical research. Recently, Large Language Models\n(LLMs) such as ChatGPT have achieved tremendous success in various downstream\ntasks thanks to their promising performance in language understanding,\ninference, and generation. It is then natural to test their feasibility in\nsolving the cohort recruitment task, which involves the classification of a\ngiven paragraph of medical text into disease label(s). However, when applied to\nknowledge-intensive problem settings such as medical text classification, where\nthe LLMs are expected to understand the decision made by human experts and\naccurately identify the implied disease labels, the LLMs show a mediocre\nperformance. A possible explanation is that, by only using the medical text,\nthe LLMs neglect to use the rich context of additional information that\nlanguages afford. To this end, we propose to use a knowledge graph as auxiliary\ninformation to guide the LLMs in making predictions. Moreover, to further boost\nthe LLMs adapt to the problem setting, we apply a chain-of-thought (CoT) sample\nselection strategy enhanced by reinforcement learning, which selects a set of\nCoT samples given each individual medical report. Experimental results and\nvarious ablation studies show that our few-shot learning method achieves\nsatisfactory performance compared with fine-tuning strategies and gains superb\nadvantages when the available data is limited. The code and sample dataset of\nthe proposed CohortGPT model is available at:\nhttps://anonymous.4open.science/r/CohortGPT-4872/\n", "date": "2023-07-21", "categories": []}
{"id": "2305.03642", "title": "Jointly Extracting Interventions, Outcomes, and Findings from RCT\n  Reports with LLMs", "abstract": "  Results from Randomized Controlled Trials (RCTs) establish the comparative\neffectiveness of interventions, and are in turn critical inputs for\nevidence-based care. However, results from RCTs are presented in (often\nunstructured) natural language articles describing the design, execution, and\noutcomes of trials; clinicians must manually extract findings pertaining to\ninterventions and outcomes of interest from such articles. This onerous manual\nprocess has motivated work on (semi-)automating extraction of structured\nevidence from trial reports. In this work we propose and evaluate a\ntext-to-text model built on instruction-tuned Large Language Models (LLMs) to\njointly extract Interventions, Outcomes, and Comparators (ICO elements) from\nclinical abstracts, and infer the associated results reported. Manual (expert)\nand automated evaluations indicate that framing evidence extraction as a\nconditional generation task and fine-tuning LLMs for this purpose realizes\nconsiderable ($\\sim$20 point absolute F1 score) gains over the previous SOTA.\nWe perform ablations and error analyses to assess aspects that contribute to\nmodel performance, and to highlight potential directions for further\nimprovements. We apply our model to a collection of published RCTs through\nmid-2022, and release a searchable database of structured findings:\nhttp://ico-relations.ebm-nlp.com\n", "date": "2023-05-05", "categories": []}
{"id": "2307.08070", "title": "Automatic Identification of Alzheimer's Disease using Lexical Features\n  extracted from Language Samples", "abstract": "  Objective: this study has a twofold goal. First, it aims to improve the\nunderstanding of the impact of Dementia of type Alzheimer's Disease (AD) on\ndifferent aspects of the lexicon. Second, it aims to demonstrate that such\naspects of the lexicon, when used as features of a machine learning classifier,\ncan help achieve state-of-the-art performance in automatically identifying\nlanguage samples produced by patients with AD. Methods: data is derived from\nthe ADDreSS challenge, which is a part of the DementiaBank corpus. The used\ndataset consists of transcripts of Cookie Theft picture descriptions, produced\nby 54 subjects in the training part and 24 subjects in the test part. The\nnumber of narrative samples is 108 in the training set and 48 in the test set.\nFirst, the impact of AD on 99 selected lexical features is studied using both\nthe training and testing parts of the dataset. Then some machine learning\nexperiments were conducted on the task of classifying transcribed speech\nsamples with text samples that were produced by people with AD from those\nproduced by normal subjects. Several experiments were conducted to compare the\ndifferent areas of lexical complexity, identify the subset of features that\nhelp achieve optimal performance, and study the impact of the size of the input\non the classification. To evaluate the generalization of the models built on\nnarrative speech, two generalization tests were conducted using written data\nfrom two British authors, Iris Murdoch and Agatha Christie, and the\ntranscription of some speeches by former President Ronald Reagan. Results:\nusing lexical features only, state-of-the-art classification, F1 and\naccuracies, of over 91% were achieved in categorizing language samples produced\nby individuals with AD from the ones produced by healthy control subjects. This\nconfirms the substantial impact of AD on lexicon processing.\n", "date": "2023-07-16", "categories": []}
{"id": "2307.08152", "title": "The Potential and Pitfalls of using a Large Language Model such as\n  ChatGPT or GPT-4 as a Clinical Assistant", "abstract": "  Recent studies have demonstrated promising performance of ChatGPT and GPT-4\non several medical domain tasks. However, none have assessed its performance\nusing a large-scale real-world electronic health record database, nor have\nevaluated its utility in providing clinical diagnostic assistance for patients\nacross a full range of disease presentation. We performed two analyses using\nChatGPT and GPT-4, one to identify patients with specific medical diagnoses\nusing a real-world large electronic health record database and the other, in\nproviding diagnostic assistance to healthcare workers in the prospective\nevaluation of hypothetical patients. Our results show that GPT-4 across disease\nclassification tasks with chain of thought and few-shot prompting can achieve\nperformance as high as 96% F1 scores. For patient assessment, GPT-4 can\naccurately diagnose three out of four times. However, there were mentions of\nfactually incorrect statements, overlooking crucial medical findings,\nrecommendations for unnecessary investigations and overtreatment. These issues\ncoupled with privacy concerns, make these models currently inadequate for real\nworld clinical use. However, limited data and time needed for prompt\nengineering in comparison to configuration of conventional machine learning\nworkflows highlight their potential for scalability across healthcare\napplications.\n", "date": "2023-07-16", "categories": []}
{"id": "2307.08290", "title": "CoAD: Automatic Diagnosis through Symptom and Disease Collaborative\n  Generation", "abstract": "  Automatic diagnosis (AD), a critical application of AI in healthcare, employs\nmachine learning techniques to assist doctors in gathering patient symptom\ninformation for precise disease diagnosis. The Transformer-based method\nutilizes an input symptom sequence, predicts itself through auto-regression,\nand employs the hidden state of the final symptom to determine the disease.\nDespite its simplicity and superior performance demonstrated, a decline in\ndisease diagnosis accuracy is observed caused by 1) a mismatch between symptoms\nobserved during training and generation, and 2) the effect of different symptom\norders on disease prediction. To address the above obstacles, we introduce the\nCoAD, a novel disease and symptom collaborative generation framework, which\nincorporates several key innovations to improve AD: 1) aligning sentence-level\ndisease labels with multiple possible symptom inquiry steps to bridge the gap\nbetween training and generation; 2) expanding symptom labels for each\nsub-sequence of symptoms to enhance annotation and eliminate the effect of\nsymptom order; 3) developing a repeated symptom input schema to effectively and\nefficiently learn the expanded disease and symptom labels. We evaluate the CoAD\nframework using four datasets, including three public and one private, and\ndemonstrate that it achieves an average 2.3% improvement over previous\nstate-of-the-art results in automatic disease diagnosis. For reproducibility,\nwe release the code and data at https://github.com/KwanWaiChung/coad.\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": "2307.05796", "title": "Improved POS tagging for spontaneous, clinical speech using data\n  augmentation", "abstract": "  This paper addresses the problem of improving POS tagging of transcripts of\nspeech from clinical populations. In contrast to prior work on parsing and POS\ntagging of transcribed speech, we do not make use of an in domain treebank for\ntraining. Instead, we train on an out of domain treebank of newswire using data\naugmentation techniques to make these structures resemble natural, spontaneous\nspeech. We trained a parser with and without the augmented data and tested its\nperformance using manually validated POS tags in clinical speech produced by\npatients with various types of neurodegenerative conditions.\n", "date": "2023-07-11", "categories": []}
{"id": "2307.05162", "title": "SuryaKiran at MEDIQA-Sum 2023: Leveraging LoRA for Clinical Dialogue\n  Summarization", "abstract": "  Finetuning Large Language Models helps improve the results for\ndomain-specific use cases. End-to-end finetuning of large language models is\ntime and resource intensive and has high storage requirements to store the\nfinetuned version of the large language model. Parameter Efficient Fine Tuning\n(PEFT) methods address the time and resource challenges by keeping the large\nlanguage model as a fixed base and add additional layers, which the PEFT\nmethods finetune. This paper demonstrates the evaluation results for one such\nPEFT method Low Rank Adaptation (LoRA), for Clinical Dialogue Summarization.\nThe evaluation results show that LoRA works at par with end-to-end finetuning\nfor a large language model. The paper presents the evaluations done for solving\nboth the Subtask A and B from ImageCLEFmedical\n{https://www.imageclef.org/2023/medical}\n", "date": "2023-07-11", "categories": []}
{"id": "2306.05997", "title": "Automated Labeling of German Chest X-Ray Radiology Reports using Deep\n  Learning", "abstract": "  Radiologists are in short supply globally, and deep learning models offer a\npromising solution to address this shortage as part of clinical\ndecision-support systems. However, training such models often requires\nexpensive and time-consuming manual labeling of large datasets. Automatic label\nextraction from radiology reports can reduce the time required to obtain\nlabeled datasets, but this task is challenging due to semantically similar\nwords and missing annotated data. In this work, we explore the potential of\nweak supervision of a deep learning-based label prediction model, using a\nrule-based labeler. We propose a deep learning-based CheXpert label prediction\nmodel, pre-trained on reports labeled by a rule-based German CheXpert model and\nfine-tuned on a small dataset of manually labeled reports. Our results\ndemonstrate the effectiveness of our approach, which significantly outperformed\nthe rule-based model on all three tasks. Our findings highlight the benefits of\nemploying deep learning-based models even in scenarios with sparse data and the\nuse of the rule-based labeler as a tool for weak supervision.\n", "date": "2023-06-09", "categories": []}
{"id": "2303.08262", "title": "Clinical Concept and Relation Extraction Using Prompt-based Machine\n  Reading Comprehension", "abstract": "  Objective: To develop a natural language processing system that solves both\nclinical concept extraction and relation extraction in a unified prompt-based\nmachine reading comprehension (MRC) architecture with good generalizability for\ncross-institution applications.\n  Methods: We formulate both clinical concept extraction and relation\nextraction using a unified prompt-based MRC architecture and explore\nstate-of-the-art transformer models. We compare our MRC models with existing\ndeep learning models for concept extraction and end-to-end relation extraction\nusing two benchmark datasets developed by the 2018 National NLP Clinical\nChallenges (n2c2) challenge (medications and adverse drug events) and the 2022\nn2c2 challenge (relations of social determinants of health [SDoH]). We also\nevaluate the transfer learning ability of the proposed MRC models in a\ncross-institution setting. We perform error analyses and examine how different\nprompting strategies affect the performance of MRC models.\n  Results and Conclusion: The proposed MRC models achieve state-of-the-art\nperformance for clinical concept and relation extraction on the two benchmark\ndatasets, outperforming previous non-MRC transformer models. GatorTron-MRC\nachieves the best strict and lenient F1-scores for concept extraction,\noutperforming previous deep learning models on the two datasets by 1%~3% and\n0.7%~1.3%, respectively. For end-to-end relation extraction, GatorTron-MRC and\nBERT-MIMIC-MRC achieve the best F1-scores, outperforming previous deep learning\nmodels by 0.9%~2.4% and 10%-11%, respectively. For cross-institution\nevaluation, GatorTron-MRC outperforms traditional GatorTron by 6.4% and 16% for\nthe two datasets, respectively. The proposed method is better at handling\nnested/overlapped concepts, extracting relations, and has good portability for\ncross-institute applications.\n", "date": "2023-03-14", "categories": []}
{"id": "2307.00266", "title": "Hierarchical Pretraining for Biomedical Term Embeddings", "abstract": "  Electronic health records (EHR) contain narrative notes that provide\nextensive details on the medical condition and management of patients. Natural\nlanguage processing (NLP) of clinical notes can use observed frequencies of\nclinical terms as predictive features for downstream applications such as\nclinical decision making and patient trajectory prediction. However, due to the\nvast number of highly similar and related clinical concepts, a more effective\nmodeling strategy is to represent clinical terms as semantic embeddings via\nrepresentation learning and use the low dimensional embeddings as feature\nvectors for predictive modeling. To achieve efficient representation,\nfine-tuning pretrained language models with biomedical knowledge graphs may\ngenerate better embeddings for biomedical terms than those from standard\nlanguage models alone. These embeddings can effectively discriminate synonymous\npairs of from those that are unrelated. However, they often fail to capture\ndifferent degrees of similarity or relatedness for concepts that are\nhierarchical in nature. To overcome this limitation, we propose HiPrBERT, a\nnovel biomedical term representation model trained on additionally complied\ndata that contains hierarchical structures for various biomedical terms. We\nmodify an existing contrastive loss function to extract information from these\nhierarchies. Our numerical experiments demonstrate that HiPrBERT effectively\nlearns the pair-wise distance from hierarchical information, resulting in a\nsubstantially more informative embeddings for further biomedical applications\n", "date": "2023-07-01", "categories": []}
{"id": "2306.17180", "title": "Replace and Report: NLP Assisted Radiology Report Generation", "abstract": "  Clinical practice frequently uses medical imaging for diagnosis and\ntreatment. A significant challenge for automatic radiology report generation is\nthat the radiology reports are long narratives consisting of multiple sentences\nfor both abnormal and normal findings. Therefore, applying conventional image\ncaptioning approaches to generate the whole report proves to be insufficient,\nas these are designed to briefly describe images with short sentences. We\npropose a template-based approach to generate radiology reports from\nradiographs. Our approach involves the following: i) using a multilabel image\nclassifier, produce the tags for the input radiograph; ii) using a\ntransformer-based model, generate pathological descriptions (a description of\nabnormal findings seen on radiographs) from the tags generated in step (i);\niii) using a BERT-based multi-label text classifier, find the spans in the\nnormal report template to replace with the generated pathological descriptions;\nand iv) using a rule-based system, replace the identified span with the\ngenerated pathological description. We performed experiments with the two most\npopular radiology report datasets, IU Chest X-ray and MIMIC-CXR and\ndemonstrated that the BLEU-1, ROUGE-L, METEOR, and CIDEr scores are better than\nthe State-of-the-Art models by 25%, 36%, 44% and 48% respectively, on the IU\nX-RAY dataset. To the best of our knowledge, this is the first attempt to\ngenerate chest X-ray radiology reports by first creating small sentences for\nabnormal findings and then replacing them in the normal report template.\n", "date": "2023-06-19", "categories": []}
{"id": "2306.17384", "title": "SummQA at MEDIQA-Chat 2023:In-Context Learning with GPT-4 for Medical\n  Summarization", "abstract": "  Medical dialogue summarization is challenging due to the unstructured nature\nof medical conversations, the use of medical terminology in gold summaries, and\nthe need to identify key information across multiple symptom sets. We present a\nnovel system for the Dialogue2Note Medical Summarization tasks in the MEDIQA\n2023 Shared Task. Our approach for section-wise summarization (Task A) is a\ntwo-stage process of selecting semantically similar dialogues and using the\ntop-k similar dialogues as in-context examples for GPT-4. For full-note\nsummarization (Task B), we use a similar solution with k=1. We achieved 3rd\nplace in Task A (2nd among all teams), 4th place in Task B Division Wise\nSummarization (2nd among all teams), 15th place in Task A Section Header\nClassification (9th among all teams), and 8th place among all teams in Task B.\nOur results highlight the effectiveness of few-shot prompting for this task,\nthough we also identify several weaknesses of prompting-based approaches. We\ncompare GPT-4 performance with several finetuned baselines. We find that GPT-4\nsummaries are more abstractive and shorter. We make our code publicly\navailable.\n", "date": "2023-06-30", "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": "2306.16931", "title": "UMASS_BioNLP at MEDIQA-Chat 2023: Can LLMs generate high-quality\n  synthetic note-oriented doctor-patient conversations?", "abstract": "  This paper presents UMASS_BioNLP team participation in the MEDIQA-Chat 2023\nshared task for Task-A and Task-C. We focus especially on Task-C and propose a\nnovel LLMs cooperation system named a doctor-patient loop to generate\nhigh-quality conversation data sets. The experiment results demonstrate that\nour approaches yield reasonable performance as evaluated by automatic metrics\nsuch as ROUGE, medical concept recall, BLEU, and Self-BLEU. Furthermore, we\nconducted a comparative analysis between our proposed method and ChatGPT and\nGPT-4. This analysis also investigates the potential of utilizing cooperation\nLLMs to generate high-quality datasets.\n", "date": "2023-06-29", "categories": []}
{"id": "2306.15887", "title": "Beyond the Hype: Assessing the Performance, Trustworthiness, and\n  Clinical Suitability of GPT3.5", "abstract": "  The use of large language models (LLMs) in healthcare is gaining popularity,\nbut their practicality and safety in clinical settings have not been thoroughly\nassessed. In high-stakes environments like medical settings, trust and safety\nare critical issues for LLMs. To address these concerns, we present an approach\nto evaluate the performance and trustworthiness of a GPT3.5 model for medical\nimage protocol assignment. We compare it with a fine-tuned BERT model and a\nradiologist. In addition, we have a radiologist review the GPT3.5 output to\nevaluate its decision-making process. Our evaluation dataset consists of 4,700\nphysician entries across 11 imaging protocol classes spanning the entire head.\nOur findings suggest that the GPT3.5 performance falls behind BERT and a\nradiologist. However, GPT3.5 outperforms BERT in its ability to explain its\ndecision, detect relevant word indicators, and model calibration. Furthermore,\nby analyzing the explanations of GPT3.5 for misclassifications, we reveal\nsystematic errors that need to be resolved to enhance its safety and\nsuitability for clinical use.\n", "date": "2023-06-28", "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": "2305.13250", "title": "Copy Recurrent Neural Network Structure Network", "abstract": "  Electronic Health Record (EHR) coding involves automatically classifying EHRs\ninto diagnostic codes. While most previous research treats this as a\nmulti-label classification task, generating probabilities for each code and\nselecting those above a certain threshold as labels, these approaches often\noverlook the challenge of identifying complex diseases. In this study, our\nfocus is on detecting complication diseases within EHRs.\n  We propose a novel coarse-to-fine ICD path generation framework called the\nCopy Recurrent Neural Network Structure Network (CRNNet), which employs a Path\nGenerator (PG) and a Path Discriminator (PD) for EHR coding. By using RNNs to\ngenerate sequential outputs and incorporating a copy module, we efficiently\nidentify complication diseases. Our method achieves a 57.30\\% ratio of complex\ndiseases in predictions, outperforming state-of-the-art and previous\napproaches.\n  Additionally, through an ablation study, we demonstrate that the copy\nmechanism plays a crucial role in detecting complex diseases.\n", "date": "2023-05-22", "categories": []}
{"id": "2303.14070", "title": "ChatDoctor: A Medical Chat Model Fine-Tuned on a Large Language Model\n  Meta-AI (LLaMA) Using Medical Domain Knowledge", "abstract": "  The primary aim of this research was to address the limitations observed in\nthe medical knowledge of prevalent large language models (LLMs) such as\nChatGPT, by creating a specialized language model with enhanced accuracy in\nmedical advice. We achieved this by adapting and refining the large language\nmodel meta-AI (LLaMA) using a large dataset of 100,000 patient-doctor dialogues\nsourced from a widely used online medical consultation platform. These\nconversations were cleaned and anonymized to respect privacy concerns. In\naddition to the model refinement, we incorporated a self-directed information\nretrieval mechanism, allowing the model to access and utilize real-time\ninformation from online sources like Wikipedia and data from curated offline\nmedical databases. The fine-tuning of the model with real-world patient-doctor\ninteractions significantly improved the model's ability to understand patient\nneeds and provide informed advice. By equipping the model with self-directed\ninformation retrieval from reliable online and offline sources, we observed\nsubstantial improvements in the accuracy of its responses. Our proposed\nChatDoctor, represents a significant advancement in medical LLMs, demonstrating\na significant improvement in understanding patient inquiries and providing\naccurate advice. Given the high stakes and low error tolerance in the medical\nfield, such enhancements in providing accurate and reliable information are not\nonly beneficial but essential.\n", "date": "2023-03-24", "categories": []}
{"id": "2306.00013", "title": "Machine Learning Approach for Cancer Entities Association and\n  Classification", "abstract": "  According to the World Health Organization (WHO), cancer is the second\nleading cause of death globally. Scientific research on different types of\ncancers grows at an ever-increasing rate, publishing large volumes of research\narticles every year. The insight information and the knowledge of the drug,\ndiagnostics, risk, symptoms, treatments, etc., related to genes are significant\nfactors that help explore and advance the cancer research progression. Manual\nscreening of such a large volume of articles is very laborious and\ntime-consuming to formulate any hypothesis. The study uses the two most\nnon-trivial NLP, Natural Language Processing functions, Entity Recognition, and\ntext classification to discover knowledge from biomedical literature. Named\nEntity Recognition (NER) recognizes and extracts the predefined entities\nrelated to cancer from unstructured text with the support of a user-friendly\ninterface and built-in dictionaries. Text classification helps to explore the\ninsights into the text and simplifies data categorization, querying, and\narticle screening. Machine learning classifiers are also used to build the\nclassification model and Structured Query Languages (SQL) is used to identify\nthe hidden relations that may lead to significant predictions.\n", "date": "2023-05-30", "categories": []}
{"id": "2306.12834", "title": "Natural Language Processing in Electronic Health Records in Relation to\n  Healthcare Decision-making: A Systematic Review", "abstract": "  Background: Natural Language Processing (NLP) is widely used to extract\nclinical insights from Electronic Health Records (EHRs). However, the lack of\nannotated data, automated tools, and other challenges hinder the full\nutilisation of NLP for EHRs. Various Machine Learning (ML), Deep Learning (DL)\nand NLP techniques are studied and compared to understand the limitations and\nopportunities in this space comprehensively.\n  Methodology: After screening 261 articles from 11 databases, we included 127\npapers for full-text review covering seven categories of articles: 1) medical\nnote classification, 2) clinical entity recognition, 3) text summarisation, 4)\ndeep learning (DL) and transfer learning architecture, 5) information\nextraction, 6) Medical language translation and 7) other NLP applications. This\nstudy follows the Preferred Reporting Items for Systematic Reviews and\nMeta-Analyses (PRISMA) guidelines.\n  Result and Discussion: EHR was the most commonly used data type among the\nselected articles, and the datasets were primarily unstructured. Various ML and\nDL methods were used, with prediction or classification being the most common\napplication of ML or DL. The most common use cases were: the International\nClassification of Diseases, Ninth Revision (ICD-9) classification, clinical\nnote analysis, and named entity recognition (NER) for clinical descriptions and\nresearch on psychiatric disorders.\n  Conclusion: We find that the adopted ML models were not adequately assessed.\nIn addition, the data imbalance problem is quite important, yet we must find\ntechniques to address this underlining problem. Future studies should address\nkey limitations in studies, primarily identifying Lupus Nephritis, Suicide\nAttempts, perinatal self-harmed and ICD-9 classification.\n", "date": "2023-06-22", "categories": []}
{"id": "2305.19981", "title": "MedNgage: A Dataset for Understanding Engagement in Patient-Nurse\n  Conversations", "abstract": "  Patients who effectively manage their symptoms often demonstrate higher\nlevels of engagement in conversations and interventions with healthcare\npractitioners. This engagement is multifaceted, encompassing cognitive and\nsocio-affective dimensions. Consequently, it is crucial for AI systems to\nunderstand the engagement in natural conversations between patients and\npractitioners to better contribute toward patient care. In this paper, we\npresent a novel dataset (MedNgage), which consists of patient-nurse\nconversations about cancer symptom management. We manually annotate the dataset\nwith a novel framework of categories of patient engagement from two different\nangles, namely: i) socio-affective (3.1K spans), and ii) cognitive use of\nlanguage (1.8K spans). Through statistical analysis of the data that is\nannotated using our framework, we show a positive correlation between patient\nsymptom management outcomes and their engagement in conversations.\nAdditionally, we demonstrate that pre-trained transformer models fine-tuned on\nour dataset can reliably predict engagement classes in patient-nurse\nconversations. Lastly, we use LIME (Ribeiro et al., 2016) to analyze the\nunderlying challenges of the tasks that state-of-the-art transformer models\nencounter. The de-identified data is available for research purposes upon\nrequest.\n", "date": "2023-05-31", "categories": []}
{"id": "2306.00024", "title": "Self-Verification Improves Few-Shot Clinical Information Extraction", "abstract": "  Extracting patient information from unstructured text is a critical task in\nhealth decision-support and clinical research. Large language models (LLMs)\nhave shown the potential to accelerate clinical curation via few-shot\nin-context learning, in contrast to supervised learning which requires much\nmore costly human annotations. However, despite drastic advances in modern LLMs\nsuch as GPT-4, they still struggle with issues regarding accuracy and\ninterpretability, especially in mission-critical domains such as health. Here,\nwe explore a general mitigation framework using self-verification, which\nleverages the LLM to provide provenance for its own extraction and check its\nown outputs. This is made possible by the asymmetry between verification and\ngeneration, where the latter is often much easier than the former. Experimental\nresults show that our method consistently improves accuracy for various LLMs in\nstandard clinical information extraction tasks. Additionally, self-verification\nyields interpretations in the form of a short text span corresponding to each\noutput, which makes it very efficient for human experts to audit the results,\npaving the way towards trustworthy extraction of clinical information in\nresource-constrained scenarios. To facilitate future research in this\ndirection, we release our code and prompts.\n", "date": "2023-05-30", "categories": []}
{"id": "2306.09544", "title": "Building blocks for complex tasks: Robust generative event extraction\n  for radiology reports under domain shifts", "abstract": "  This paper explores methods for extracting information from radiology reports\nthat generalize across exam modalities to reduce requirements for annotated\ndata. We demonstrate that multi-pass T5-based text-to-text generative models\nexhibit better generalization across exam modalities compared to approaches\nthat employ BERT-based task-specific classification layers. We then develop\nmethods that reduce the inference cost of the model, making large-scale corpus\nprocessing more feasible for clinical applications. Specifically, we introduce\na generative technique that decomposes complex tasks into smaller subtask\nblocks, which improves a single-pass model when combined with multitask\ntraining. In addition, we leverage target-domain contexts during inference to\nenhance domain adaptation, enabling use of smaller models. Analyses offer\ninsights into the benefits of different cost reduction strategies.\n", "date": "2023-06-15", "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": "2306.09968", "title": "ClinicalGPT: Large Language Models Finetuned with Diverse Medical Data\n  and Comprehensive Evaluation", "abstract": "  Large language models have exhibited exceptional performance on various\nNatural Language Processing (NLP) tasks, leveraging techniques such as the\npre-training, and instruction fine-tuning. Despite these advances, their\neffectiveness in medical applications is limited, due to challenges such as\nfactual inaccuracies, reasoning abilities, and lack grounding in real-world\nexperience. In this study, we present ClinicalGPT, a language model explicitly\ndesigned and optimized for clinical scenarios. By incorporating extensive and\ndiverse real-world data, such as medical records, domain-specific knowledge,\nand multi-round dialogue consultations in the training process, ClinicalGPT is\nbetter prepared to handle multiple clinical task. Furthermore, we introduce a\ncomprehensive evaluation framework that includes medical knowledge\nquestion-answering, medical exams, patient consultations, and diagnostic\nanalysis of medical records. Our results demonstrate that ClinicalGPT\nsignificantly outperforms other models in these tasks, highlighting the\neffectiveness of our approach in adapting large language models to the critical\ndomain of healthcare.\n", "date": "2023-06-16", "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": "2306.07297", "title": "Medical Data Augmentation via ChatGPT: A Case Study on Medication\n  Identification and Medication Event Classification", "abstract": "  The identification of key factors such as medications, diseases, and\nrelationships within electronic health records and clinical notes has a wide\nrange of applications in the clinical field. In the N2C2 2022 competitions,\nvarious tasks were presented to promote the identification of key factors in\nelectronic health records (EHRs) using the Contextualized Medication Event\nDataset (CMED). Pretrained large language models (LLMs) demonstrated\nexceptional performance in these tasks. This study aims to explore the\nutilization of LLMs, specifically ChatGPT, for data augmentation to overcome\nthe limited availability of annotated data for identifying the key factors in\nEHRs. Additionally, different pre-trained BERT models, initially trained on\nextensive datasets like Wikipedia and MIMIC, were employed to develop models\nfor identifying these key variables in EHRs through fine-tuning on augmented\ndatasets. The experimental results of two EHR analysis tasks, namely medication\nidentification and medication event classification, indicate that data\naugmentation based on ChatGPT proves beneficial in improving performance for\nboth medication identification and medication event classification.\n", "date": "2023-06-10", "categories": []}
{"id": "2306.07373", "title": "EriBERTa: A Bilingual Pre-Trained Language Model for Clinical Natural\n  Language Processing", "abstract": "  The utilization of clinical reports for various secondary purposes, including\nhealth research and treatment monitoring, is crucial for enhancing patient\ncare. Natural Language Processing (NLP) tools have emerged as valuable assets\nfor extracting and processing relevant information from these reports. However,\nthe availability of specialized language models for the clinical domain in\nSpanish has been limited.\n  In this paper, we introduce EriBERTa, a bilingual domain-specific language\nmodel pre-trained on extensive medical and clinical corpora. We demonstrate\nthat EriBERTa outperforms previous Spanish language models in the clinical\ndomain, showcasing its superior capabilities in understanding medical texts and\nextracting meaningful information. Moreover, EriBERTa exhibits promising\ntransfer learning abilities, allowing for knowledge transfer from one language\nto another. This aspect is particularly beneficial given the scarcity of\nSpanish clinical data.\n", "date": "2023-06-12", "categories": []}
{"id": "2208.08417", "title": "Extracting Medication Changes in Clinical Narratives using Pre-trained\n  Language Models", "abstract": "  An accurate and detailed account of patient medications, including medication\nchanges within the patient timeline, is essential for healthcare providers to\nprovide appropriate patient care. Healthcare providers or the patients\nthemselves may initiate changes to patient medication. Medication changes take\nmany forms, including prescribed medication and associated dosage modification.\nThese changes provide information about the overall health of the patient and\nthe rationale that led to the current care. Future care can then build on the\nresulting state of the patient. This work explores the automatic extraction of\nmedication change information from free-text clinical notes. The Contextual\nMedication Event Dataset (CMED) is a corpus of clinical notes with annotations\nthat characterize medication changes through multiple change-related\nattributes, including the type of change (start, stop, increase, etc.),\ninitiator of the change, temporality, change likelihood, and negation. Using\nCMED, we identify medication mentions in clinical text and propose three novel\nhigh-performing BERT-based systems that resolve the annotated medication change\ncharacteristics. We demonstrate that our proposed systems improve medication\nchange classification performance over the initial work exploring CMED.\n", "date": "2022-08-17", "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": "2306.07170", "title": "Prompt-based Extraction of Social Determinants of Health Using Few-shot\n  Learning", "abstract": "  Social determinants of health (SDOH) documented in the electronic health\nrecord through unstructured text are increasingly being studied to understand\nhow SDOH impacts patient health outcomes. In this work, we utilize the Social\nHistory Annotation Corpus (SHAC), a multi-institutional corpus of de-identified\nsocial history sections annotated for SDOH, including substance use,\nemployment, and living status information. We explore the automatic extraction\nof SDOH information with SHAC in both standoff and inline annotation formats\nusing GPT-4 in a one-shot prompting setting. We compare GPT-4 extraction\nperformance with a high-performing supervised approach and perform thorough\nerror analyses. Our prompt-based GPT-4 method achieved an overall 0.652 F1 on\nthe SHAC test set, similar to the 7th best-performing system among all teams in\nthe n2c2 challenge with SHAC.\n", "date": "2023-06-12", "categories": []}
{"id": "2306.05052", "title": "Interpretable Medical Diagnostics with Structured Data Extraction by\n  Large Language Models", "abstract": "  Tabular data is often hidden in text, particularly in medical diagnostic\nreports. Traditional machine learning (ML) models designed to work with tabular\ndata, cannot effectively process information in such form. On the other hand,\nlarge language models (LLMs) which excel at textual tasks, are probably not the\nbest tool for modeling tabular data. Therefore, we propose a novel, simple, and\neffective methodology for extracting structured tabular data from textual\nmedical reports, called TEMED-LLM. Drawing upon the reasoning capabilities of\nLLMs, TEMED-LLM goes beyond traditional extraction techniques, accurately\ninferring tabular features, even when their names are not explicitly mentioned\nin the text. This is achieved by combining domain-specific reasoning guidelines\nwith a proposed data validation and reasoning correction feedback loop. By\napplying interpretable ML models such as decision trees and logistic regression\nover the extracted and validated data, we obtain end-to-end interpretable\npredictions. We demonstrate that our approach significantly outperforms\nstate-of-the-art text classification models in medical diagnostics. Given its\npredictive performance, simplicity, and interpretability, TEMED-LLM underscores\nthe potential of leveraging LLMs to improve the performance and trustworthiness\nof ML models in medical applications.\n", "date": "2023-06-08", "categories": []}
{"id": "2306.05270", "title": "Overview of the Problem List Summarization (ProbSum) 2023 Shared Task on\n  Summarizing Patients' Active Diagnoses and Problems from Electronic Health\n  Record Progress Notes", "abstract": "  The BioNLP Workshop 2023 initiated the launch of a shared task on Problem\nList Summarization (ProbSum) in January 2023. The aim of this shared task is to\nattract future research efforts in building NLP models for real-world\ndiagnostic decision support applications, where a system generating relevant\nand accurate diagnoses will augment the healthcare providers decision-making\nprocess and improve the quality of care for patients. The goal for participants\nis to develop models that generated a list of diagnoses and problems using\ninput from the daily care notes collected from the hospitalization of\ncritically ill patients. Eight teams submitted their final systems to the\nshared task leaderboard. In this paper, we describe the tasks, datasets,\nevaluation metrics, and baseline systems. Additionally, the techniques and\nresults of the evaluation of the different approaches tried by the\nparticipating teams are summarized.\n", "date": "2023-06-08", "categories": []}
{"id": "2306.05276", "title": "Extensive Evaluation of Transformer-based Architectures for Adverse Drug\n  Events Extraction", "abstract": "  Adverse Event (ADE) extraction is one of the core tasks in digital\npharmacovigilance, especially when applied to informal texts. This task has\nbeen addressed by the Natural Language Processing community using large\npre-trained language models, such as BERT. Despite the great number of\nTransformer-based architectures used in the literature, it is unclear which of\nthem has better performances and why. Therefore, in this paper we perform an\nextensive evaluation and analysis of 19 Transformer-based models for ADE\nextraction on informal texts. We compare the performance of all the considered\nmodels on two datasets with increasing levels of informality (forums posts and\ntweets). We also combine the purely Transformer-based models with two\ncommonly-used additional processing layers (CRF and LSTM), and analyze their\neffect on the models performance. Furthermore, we use a well-established\nfeature importance technique (SHAP) to correlate the performance of the models\nwith a set of features that describe them: model category (AutoEncoding,\nAutoRegressive, Text-to-Text), pretraining domain, training from scratch, and\nmodel size in number of parameters. At the end of our analyses, we identify a\nlist of take-home messages that can be derived from the experimental data.\n", "date": "2023-06-08", "categories": []}
{"id": "2210.09440", "title": "Using Bottleneck Adapters to Identify Cancer in Clinical Notes under\n  Low-Resource Constraints", "abstract": "  Processing information locked within clinical health records is a challenging\ntask that remains an active area of research in biomedical NLP. In this work,\nwe evaluate a broad set of machine learning techniques ranging from simple RNNs\nto specialised transformers such as BioBERT on a dataset containing clinical\nnotes along with a set of annotations indicating whether a sample is\ncancer-related or not.\n  Furthermore, we specifically employ efficient fine-tuning methods from NLP,\nnamely, bottleneck adapters and prompt tuning, to adapt the models to our\nspecialised task. Our evaluations suggest that fine-tuning a frozen BERT model\npre-trained on natural language and with bottleneck adapters outperforms all\nother strategies, including full fine-tuning of the specialised BioBERT model.\nBased on our findings, we suggest that using bottleneck adapters in\nlow-resource situations with limited access to labelled data or processing\ncapacity could be a viable strategy in biomedical text mining. The code used in\nthe experiments are going to be made available at\nhttps://github.com/omidrohanian/bottleneck-adapters.\n", "date": "2022-10-17", "categories": []}
{"id": "2306.04328", "title": "IUTEAM1 at MEDIQA-Chat 2023: Is simple fine tuning effective for\n  multilayer summarization of clinical conversations?", "abstract": "  Clinical conversation summarization has become an important application of\nNatural language Processing. In this work, we intend to analyze summarization\nmodel ensembling approaches, that can be utilized to improve the overall\naccuracy of the generated medical report called chart note. The work starts\nwith a single summarization model creating the baseline. Then leads to an\nensemble of summarization models trained on a separate section of the chart\nnote. This leads to the final approach of passing the generated results to\nanother summarization model in a multi-layer/stage fashion for better coherency\nof the generated text. Our results indicate that although an ensemble of models\nspecialized in each section produces better results, the multi-layer/stage\napproach does not improve accuracy. The code for the above paper is available\nat https://github.com/dhananjay-srivastava/MEDIQA-Chat-2023-iuteam1.git\n", "date": "2023-06-07", "categories": []}
{"id": "2306.04384", "title": "Multilingual Clinical NER: Translation or Cross-lingual Transfer?", "abstract": "  Natural language tasks like Named Entity Recognition (NER) in the clinical\ndomain on non-English texts can be very time-consuming and expensive due to the\nlack of annotated data. Cross-lingual transfer (CLT) is a way to circumvent\nthis issue thanks to the ability of multilingual large language models to be\nfine-tuned on a specific task in one language and to provide high accuracy for\nthe same task in another language. However, other methods leveraging\ntranslation models can be used to perform NER without annotated data in the\ntarget language, by either translating the training set or test set. This paper\ncompares cross-lingual transfer with these two alternative methods, to perform\nclinical NER in French and in German without any training data in those\nlanguages. To this end, we release MedNERF a medical NER test set extracted\nfrom French drug prescriptions and annotated with the same guidelines as an\nEnglish dataset. Through extensive experiments on this dataset and on a German\nmedical dataset (Frei and Kramer, 2021), we show that translation-based methods\ncan achieve similar performance to CLT but require more care in their design.\nAnd while they can take advantage of monolingual clinical language models,\nthose do not guarantee better results than large general-purpose multilingual\nmodels, whether with cross-lingual transfer or translation.\n", "date": "2023-06-07", "categories": []}
{"id": "2306.03264", "title": "shs-nlp at RadSum23: Domain-Adaptive Pre-training of Instruction-tuned\n  LLMs for Radiology Report Impression Generation", "abstract": "  Instruction-tuned generative Large language models (LLMs) like ChatGPT and\nBloomz possess excellent generalization abilities, but they face limitations in\nunderstanding radiology reports, particularly in the task of generating the\nIMPRESSIONS section from the FINDINGS section. They tend to generate either\nverbose or incomplete IMPRESSIONS, mainly due to insufficient exposure to\nmedical text data during training. We present a system which leverages\nlarge-scale medical text data for domain-adaptive pre-training of\ninstruction-tuned LLMs to enhance its medical knowledge and performance on\nspecific medical tasks. We show that this system performs better in a zero-shot\nsetting than a number of pretrain-and-finetune adaptation methods on the\nIMPRESSIONS generation task, and ranks 1st among participating systems in Task\n1B: Radiology Report Summarization at the BioNLP 2023 workshop.\n", "date": "2023-06-05", "categories": []}
{"id": "2305.02220", "title": "WangLab at MEDIQA-Chat 2023: Clinical Note Generation from\n  Doctor-Patient Conversations using Large Language Models", "abstract": "  This paper describes our submission to the MEDIQA-Chat 2023 shared task for\nautomatic clinical note generation from doctor-patient conversations. We report\nresults for two approaches: the first fine-tunes a pre-trained language model\n(PLM) on the shared task data, and the second uses few-shot in-context learning\n(ICL) with a large language model (LLM). Both achieve high performance as\nmeasured by automatic metrics (e.g. ROUGE, BERTScore) and ranked second and\nfirst, respectively, of all submissions to the shared task. Expert human\nscrutiny indicates that notes generated via the ICL-based approach with GPT-4\nare preferred about as often as human-written notes, making it a promising path\ntoward automated note generation from doctor-patient conversations.\n", "date": "2023-05-03", "categories": []}
{"id": "2306.02022", "title": "ACI-BENCH: a Novel Ambient Clinical Intelligence Dataset for\n  Benchmarking Automatic Visit Note Generation", "abstract": "  Recent immense breakthroughs in generative models such as in GPT4 have\nprecipitated re-imagined ubiquitous usage of these models in all applications.\nOne area that can benefit by improvements in artificial intelligence (AI) is\nhealthcare. The note generation task from doctor-patient encounters, and its\nassociated electronic medical record documentation, is one of the most arduous\ntime-consuming tasks for physicians. It is also a natural prime potential\nbeneficiary to advances in generative models. However with such advances,\nbenchmarking is more critical than ever. Whether studying model weaknesses or\ndeveloping new evaluation metrics, shared open datasets are an imperative part\nof understanding the current state-of-the-art. Unfortunately as clinic\nencounter conversations are not routinely recorded and are difficult to\nethically share due to patient confidentiality, there are no sufficiently large\nclinic dialogue-note datasets to benchmark this task. Here we present the\nAmbient Clinical Intelligence Benchmark (ACI-BENCH) corpus, the largest dataset\nto date tackling the problem of AI-assisted note generation from visit\ndialogue. We also present the benchmark performances of several common\nstate-of-the-art approaches.\n", "date": "2023-06-03", "categories": []}
{"id": "2306.02492", "title": "RadLing: Towards Efficient Radiology Report Understanding", "abstract": "  Most natural language tasks in the radiology domain use language models\npre-trained on biomedical corpus. There are few pretrained language models\ntrained specifically for radiology, and fewer still that have been trained in a\nlow data setting and gone on to produce comparable results in fine-tuning\ntasks. We present RadLing, a continuously pretrained language model using\nElectra-small (Clark et al., 2020) architecture, trained using over 500K\nradiology reports, that can compete with state-of-the-art results for fine\ntuning tasks in radiology domain. Our main contribution in this paper is\nknowledge-aware masking which is a taxonomic knowledge-assisted pretraining\ntask that dynamically masks tokens to inject knowledge during pretraining. In\naddition, we also introduce an knowledge base-aided vocabulary extension to\nadapt the general tokenization vocabulary to radiology domain.\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": "2306.02754", "title": "PULSAR: Pre-training with Extracted Healthcare Terms for Summarising\n  Patients' Problems and Data Augmentation with Black-box Large Language Models", "abstract": "  Medical progress notes play a crucial role in documenting a patient's\nhospital journey, including his or her condition, treatment plan, and any\nupdates for healthcare providers. Automatic summarisation of a patient's\nproblems in the form of a problem list can aid stakeholders in understanding a\npatient's condition, reducing workload and cognitive bias. BioNLP 2023 Shared\nTask 1A focuses on generating a list of diagnoses and problems from the\nprovider's progress notes during hospitalisation. In this paper, we introduce\nour proposed approach to this task, which integrates two complementary\ncomponents. One component employs large language models (LLMs) for data\naugmentation; the other is an abstractive summarisation LLM with a novel\npre-training objective for generating the patients' problems summarised as a\nlist. Our approach was ranked second among all submissions to the shared task.\nThe performance of our model on the development and test datasets shows that\nour approach is more robust on unknown data, with an improvement of up to 3.1\npoints over the same size of the larger model.\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": "2305.08010", "title": "ProKnow: Process Knowledge for Safety Constrained and Explainable\n  Question Generation for Mental Health Diagnostic Assistance", "abstract": "  Current Virtual Mental Health Assistants (VMHAs) provide counseling and\nsuggestive care. They refrain from patient diagnostic assistance because they\nlack training in safety-constrained and specialized clinical process knowledge.\nIn this work, we define Proknow as an ordered set of information that maps to\nevidence-based guidelines or categories of conceptual understanding to experts\nin a domain. We also introduce a new dataset of diagnostic conversations guided\nby safety constraints and Proknow that healthcare professionals use. We develop\na method for natural language question generation (NLG) that collects\ndiagnostic information from the patient interactively. We demonstrate the\nlimitations of using state-of-the-art large-scale language models (LMs) on this\ndataset. Our algorithm models the process knowledge through explicitly modeling\nsafety, knowledge capture, and explainability. LMs augmented with ProKnow\nguided method generated 89% safer questions in the depression and anxiety\ndomain. The Explainability of the generated question is assessed by computing\nsimilarity with concepts in depression and anxiety knowledge bases. Overall,\nirrespective of the type of LMs augmented with our ProKnow, we achieved an\naverage 82% improvement over simple pre-trained LMs on safety, explainability,\nand process-guided question generation. We qualitatively and quantitatively\nevaluate the efficacy of the proposed ProKnow-guided methods by introducing\nthree new evaluation metrics for safety, explainability, and process knowledge\nadherence.\n", "date": "2023-05-13", "categories": []}
{"id": "2306.01245", "title": "THiFLY Research at SemEval-2023 Task 7: A Multi-granularity System for\n  CTR-based Textual Entailment and Evidence Retrieval", "abstract": "  The NLI4CT task aims to entail hypotheses based on Clinical Trial Reports\n(CTRs) and retrieve the corresponding evidence supporting the justification.\nThis task poses a significant challenge, as verifying hypotheses in the NLI4CT\ntask requires the integration of multiple pieces of evidence from one or two\nCTR(s) and the application of diverse levels of reasoning, including textual\nand numerical. To address these problems, we present a multi-granularity system\nfor CTR-based textual entailment and evidence retrieval in this paper.\nSpecifically, we construct a Multi-granularity Inference Network (MGNet) that\nexploits sentence-level and token-level encoding to handle both textual\nentailment and evidence retrieval tasks. Moreover, we enhance the numerical\ninference capability of the system by leveraging a T5-based model, SciFive,\nwhich is pre-trained on the medical corpus. Model ensembling and a joint\ninference method are further utilized in the system to increase the stability\nand consistency of inference. The system achieves f1-scores of 0.856 and 0.853\non textual entailment and evidence retrieval tasks, resulting in the best\nperformance on both subtasks. The experimental results corroborate the\neffectiveness of our proposed method. Our code is publicly available at\nhttps://github.com/THUMLP/NLI4CT.\n", "date": "2023-06-02", "categories": []}
{"id": "2306.01499", "title": "Can LLMs like GPT-4 outperform traditional AI tools in dementia\n  diagnosis? Maybe, but not today", "abstract": "  Recent investigations show that large language models (LLMs), specifically\nGPT-4, not only have remarkable capabilities in common Natural Language\nProcessing (NLP) tasks but also exhibit human-level performance on various\nprofessional and academic benchmarks. However, whether GPT-4 can be directly\nused in practical applications and replace traditional artificial intelligence\n(AI) tools in specialized domains requires further experimental validation. In\nthis paper, we explore the potential of LLMs such as GPT-4 to outperform\ntraditional AI tools in dementia diagnosis. Comprehensive comparisons between\nGPT-4 and traditional AI tools are conducted to examine their diagnostic\naccuracy in a clinical setting. Experimental results on two real clinical\ndatasets show that, although LLMs like GPT-4 demonstrate potential for future\nadvancements in dementia diagnosis, they currently do not surpass the\nperformance of traditional AI tools. The interpretability and faithfulness of\nGPT-4 are also evaluated by comparison with real doctors. We discuss the\nlimitations of GPT-4 in its current state and propose future research\ndirections to enhance GPT-4 in dementia diagnosis.\n", "date": "2023-06-02", "categories": []}
{"id": "2303.01229", "title": "Almanac: Retrieval-Augmented Language Models for Clinical Medicine", "abstract": "  Large-language models have recently demonstrated impressive zero-shot\ncapabilities in a variety of natural language tasks such as summarization,\ndialogue generation, and question-answering. Despite many promising\napplications in clinical medicine, adoption of these models in real-world\nsettings has been largely limited by their tendency to generate incorrect and\nsometimes even toxic statements. In this study, we develop Almanac, a large\nlanguage model framework augmented with retrieval capabilities for medical\nguideline and treatment recommendations. Performance on a novel dataset of\nclinical scenarios (n = 130) evaluated by a panel of 5 board-certified and\nresident physicians demonstrates significant increases in factuality (mean of\n18% at p-value < 0.05) across all specialties, with improvements in\ncompleteness and safety. Our results demonstrate the potential for large\nlanguage models to be effective tools in the clinical decision-making process,\nwhile also emphasizing the importance of careful testing and deployment to\nmitigate their shortcomings.\n", "date": "2023-03-01", "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": "2306.00665", "title": "Automatic Glossary of Clinical Terminology: a Large-Scale Dictionary of\n  Biomedical Definitions Generated from Ontological Knowledge", "abstract": "  Background: More than 400,000 biomedical concepts and some of their\nrelationships are contained in SnomedCT, a comprehensive biomedical ontology.\nHowever, their concept names are not always readily interpretable by\nnon-experts, or patients looking at their own electronic health records (EHR).\nClear definitions or descriptions in understandable language are often not\navailable. Therefore, generating human-readable definitions for biomedical\nconcepts might help make the information they encode more accessible and\nunderstandable to a wider public.\n  Objective: In this article, we introduce the Automatic Glossary of Clinical\nTerminology (AGCT), a large-scale biomedical dictionary of clinical concepts\ngenerated using high-quality information extracted from the biomedical\nknowledge contained in SnomedCT.\n  Methods: We generate a novel definition for every SnomedCT concept, after\nprompting the OpenAI Turbo model, a variant of GPT 3.5, using a high-quality\nverbalization of the SnomedCT relationships of the to-be-defined concept. A\nsignificant subset of the generated definitions was subsequently judged by NLP\nresearchers with biomedical expertise on 5-point scales along the following\nthree axes: factuality, insight, and fluency.\n  Results: AGCT contains 422,070 computer-generated definitions for SnomedCT\nconcepts, covering various domains such as diseases, procedures, drugs, and\nanatomy. The average length of the definitions is 49 words. The definitions\nwere assigned average scores of over 4.5 out of 5 on all three axes, indicating\na majority of factual, insightful, and fluent definitions.\n  Conclusion: AGCT is a novel and valuable resource for biomedical tasks that\nrequire human-readable definitions for SnomedCT concepts. It can also serve as\na base for developing robust biomedical retrieval models or other applications\nthat leverage natural language understanding of biomedical knowledge.\n", "date": "2023-06-01", "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": "2305.18576", "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": "2305.16833", "title": "KNSE: A Knowledge-aware Natural Language Inference Framework for\n  Dialogue Symptom Status Recognition", "abstract": "  Symptom diagnosis in medical conversations aims to correctly extract both\nsymptom entities and their status from the doctor-patient dialogue. In this\npaper, we propose a novel framework called KNSE for symptom status recognition\n(SSR), where the SSR is formulated as a natural language inference (NLI) task.\nFor each mentioned symptom in a dialogue window, we first generate knowledge\nabout the symptom and hypothesis about status of the symptom, to form a\n(premise, knowledge, hypothesis) triplet. The BERT model is then used to encode\nthe triplet, which is further processed by modules including utterance\naggregation, self-attention, cross-attention, and GRU to predict the symptom\nstatus. Benefiting from the NLI formalization, the proposed framework can\nencode more informative prior knowledge to better localize and track symptom\nstatus, which can effectively improve the performance of symptom status\nrecognition. Preliminary experiments on Chinese medical dialogue datasets show\nthat KNSE outperforms previous competitive baselines and has advantages in\ncross-disease and cross-symptom scenarios.\n", "date": "2023-05-26", "categories": []}
{"id": "2208.07989", "title": "DICE: Data-Efficient Clinical Event Extraction with Generative Models", "abstract": "  Event extraction for the clinical domain is an under-explored research area.\nThe lack of training data along with the high volume of domain-specific\nterminologies with vague entity boundaries makes the task especially\nchallenging. In this paper, we introduce DICE, a robust and data-efficient\ngenerative model for clinical event extraction. DICE frames event extraction as\na conditional generation problem and introduces a contrastive learning\nobjective to accurately decide the boundaries of biomedical mentions. DICE also\ntrains an auxiliary mention identification task jointly with event extraction\ntasks to better identify entity mention boundaries, and further introduces\nspecial markers to incorporate identified entity mentions as trigger and\nargument candidates for their respective tasks. To benchmark clinical event\nextraction, we compose MACCROBAT-EE, the first clinical event extraction\ndataset with argument annotation, based on an existing clinical information\nextraction dataset MACCROBAT. Our experiments demonstrate state-of-the-art\nperformances of DICE for clinical and news domain event extraction, especially\nunder low data settings.\n", "date": "2022-08-16", "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": "2305.11948", "title": "Eye-SpatialNet: Spatial Information Extraction from Ophthalmology Notes", "abstract": "  We introduce an annotated corpus of 600 ophthalmology notes labeled with\ndetailed spatial and contextual information of ophthalmic entities. We extend\nour previously proposed frame semantics-based spatial representation schema,\nRad-SpatialNet, to represent spatial language in ophthalmology text, resulting\nin the Eye-SpatialNet schema. The spatially-grounded entities are findings,\nprocedures, and drugs. To accurately capture all spatial details, we add some\ndomain-specific elements in Eye-SpatialNet. The annotated corpus contains 1715\nspatial triggers, 7308 findings, 2424 anatomies, and 9914 descriptors. To\nautomatically extract the spatial information, we employ a two-turn question\nanswering approach based on the transformer language model BERT. The results\nare promising, with F1 scores of 89.31, 74.86, and 88.47 for spatial triggers,\nFigure, and Ground frame elements, respectively. This is the first work to\nrepresent and extract a wide variety of clinical information in ophthalmology.\nExtracting detailed information can benefit ophthalmology applications and\nresearch targeted toward disease progression and screening.\n", "date": "2023-05-19", "categories": []}
{"id": "2305.13120", "title": "Partial Annotation Learning for Biomedical Entity Recognition", "abstract": "  Motivation: Named Entity Recognition (NER) is a key task to support\nbiomedical research. In Biomedical Named Entity Recognition (BioNER), obtaining\nhigh-quality expert annotated data is laborious and expensive, leading to the\ndevelopment of automatic approaches such as distant supervision. However,\nmanually and automatically generated data often suffer from the unlabeled\nentity problem, whereby many entity annotations are missing, degrading the\nperformance of full annotation NER models. Results: To address this problem, we\nsystematically study the effectiveness of partial annotation learning methods\nfor biomedical entity recognition over different simulated scenarios of missing\nentity annotations. Furthermore, we propose a TS-PubMedBERT-Partial-CRF partial\nannotation learning model. We harmonize 15 biomedical NER corpora encompassing\nfive entity types to serve as a gold standard and compare against two commonly\nused partial annotation learning models, BiLSTM-Partial-CRF and EER-PubMedBERT,\nand the state-of-the-art full annotation learning BioNER model PubMedBERT\ntagger. Results show that partial annotation learning-based methods can\neffectively learn from biomedical corpora with missing entity annotations. Our\nproposed model outperforms alternatives and, specifically, the PubMedBERT\ntagger by 38% in F1-score under high missing entity rates. The recall of entity\nmentions in our model is also competitive with the upper bound on the fully\nannotated dataset.\n", "date": "2023-05-22", "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": "2305.08300", "title": "\"Nothing Abnormal\": Disambiguating Medical Reports via Contrastive\n  Knowledge Infusion", "abstract": "  Sharing medical reports is essential for patient-centered care. A recent line\nof work has focused on automatically generating reports with NLP methods.\nHowever, different audiences have different purposes when writing/reading\nmedical reports -- for example, healthcare professionals care more about\npathology, whereas patients are more concerned with the diagnosis (\"Is there\nany abnormality?\"). The expectation gap results in a common situation where\npatients find their medical reports to be ambiguous and therefore unsure about\nthe next steps. In this work, we explore the audience expectation gap in\nhealthcare and summarize common ambiguities that lead patients to be confused\nabout their diagnosis into three categories: medical jargon, contradictory\nfindings, and misleading grammatical errors. Based on our analysis, we define a\ndisambiguation rewriting task to regenerate an input to be unambiguous while\npreserving information about the original content. We further propose a\nrewriting algorithm based on contrastive pretraining and perturbation-based\nrewriting. In addition, we create two datasets, OpenI-Annotated based on chest\nreports and VA-Annotated based on general medical reports, with available\nbinary labels for ambiguity and abnormality presence annotated by radiology\nspecialists. Experimental results on these datasets show that our proposed\nalgorithm effectively rewrites input sentences in a less ambiguous way with\nhigh content fidelity. Our code and annotated data are released to facilitate\nfuture research.\n", "date": "2023-05-15", "categories": []}
{"id": "2305.02993", "title": "SemEval-2023 Task 7: Multi-Evidence Natural Language Inference for\n  Clinical Trial Data", "abstract": "  This paper describes the results of SemEval 2023 task 7 -- Multi-Evidence\nNatural Language Inference for Clinical Trial Data (NLI4CT) -- consisting of 2\ntasks, a Natural Language Inference (NLI) task, and an evidence selection task\non clinical trial data. The proposed challenges require multi-hop biomedical\nand numerical reasoning, which are of significant importance to the development\nof systems capable of large-scale interpretation and retrieval of medical\nevidence, to provide personalized evidence-based care.\n  Task 1, the entailment task, received 643 submissions from 40 participants,\nand Task 2, the evidence selection task, received 364 submissions from 23\nparticipants. The tasks are challenging, with the majority of submitted systems\nfailing to significantly outperform the majority class baseline on the\nentailment task, and we observe significantly better performance on the\nevidence selection task than on the entailment task. Increasing the number of\nmodel parameters leads to a direct increase in performance, far more\nsignificant than the effect of biomedical pre-training. Future works could\nexplore the limitations of large models for generalization and numerical\ninference, and investigate methods to augment clinical datasets to allow for\nmore rigorous testing and to facilitate fine-tuning.\n  We envisage that the dataset, models, and results of this task will be useful\nto the biomedical NLI and evidence retrieval communities. The dataset,\ncompetition leaderboard, and website are publicly available.\n", "date": "2023-05-04", "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": "2303.08290", "title": "Rediscovery of CNN's Versatility for Text-based Encoding of Raw\n  Electronic Health Records", "abstract": "  Making the most use of abundant information in electronic health records\n(EHR) is rapidly becoming an important topic in the medical domain. Recent work\npresented a promising framework that embeds entire features in raw EHR data\nregardless of its form and medical code standards. The framework, however, only\nfocuses on encoding EHR with minimal preprocessing and fails to consider how to\nlearn efficient EHR representation in terms of computation and memory usage. In\nthis paper, we search for a versatile encoder not only reducing the large data\ninto a manageable size but also well preserving the core information of\npatients to perform diverse clinical tasks. We found that hierarchically\nstructured Convolutional Neural Network (CNN) often outperforms the\nstate-of-the-art model on diverse tasks such as reconstruction, prediction, and\ngeneration, even with fewer parameters and less training time. Moreover, it\nturns out that making use of the inherent hierarchy of EHR data can boost the\nperformance of any kind of backbone models and clinical tasks performed.\nThrough extensive experiments, we present concrete evidence to generalize our\nresearch findings into real-world practice. We give a clear guideline on\nbuilding the encoder based on the research findings captured while exploring\nnumerous settings.\n", "date": "2023-03-15", "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": "2303.08259", "title": "Contextualized Medication Information Extraction Using Transformer-based\n  Deep Learning Architectures", "abstract": "  Objective: To develop a natural language processing (NLP) system to extract\nmedications and contextual information that help understand drug changes. This\nproject is part of the 2022 n2c2 challenge.\n  Materials and methods: We developed NLP systems for medication mention\nextraction, event classification (indicating medication changes discussed or\nnot), and context classification to classify medication changes context into 5\northogonal dimensions related to drug changes. We explored 6 state-of-the-art\npretrained transformer models for the three subtasks, including GatorTron, a\nlarge language model pretrained using >90 billion words of text (including >80\nbillion words from >290 million clinical notes identified at the University of\nFlorida Health). We evaluated our NLP systems using annotated data and\nevaluation scripts provided by the 2022 n2c2 organizers.\n  Results:Our GatorTron models achieved the best F1-scores of 0.9828 for\nmedication extraction (ranked 3rd), 0.9379 for event classification (ranked\n2nd), and the best micro-average accuracy of 0.9126 for context classification.\nGatorTron outperformed existing transformer models pretrained using smaller\ngeneral English text and clinical text corpora, indicating the advantage of\nlarge language models.\n  Conclusion: This study demonstrated the advantage of using large transformer\nmodels for contextual medication information extraction from clinical\nnarratives.\n", "date": "2023-03-14", "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": "2305.03788", "title": "Harnessing the Power of BERT in the Turkish Clinical Domain: Pretraining\n  Approaches for Limited Data Scenarios", "abstract": "  In recent years, major advancements in natural language processing (NLP) have\nbeen driven by the emergence of large language models (LLMs), which have\nsignificantly revolutionized research and development within the field.\nBuilding upon this progress, our study delves into the effects of various\npre-training methodologies on Turkish clinical language models' performance in\na multi-label classification task involving radiology reports, with a focus on\naddressing the challenges posed by limited language resources. Additionally, we\nevaluated the simultaneous pretraining approach by utilizing limited clinical\ntask data for the first time. We developed four models, including\nTurkRadBERT-task v1, TurkRadBERT-task v2, TurkRadBERT-sim v1, and\nTurkRadBERT-sim v2. Our findings indicate that the general Turkish BERT model\n(BERTurk) and TurkRadBERT-task v1, both of which utilize knowledge from a\nsubstantial general-domain corpus, demonstrate the best overall performance.\nAlthough the task-adaptive pre-training approach has the potential to capture\ndomain-specific patterns, it is constrained by the limited task-specific corpus\nand may be susceptible to overfitting. Furthermore, our results underscore the\nsignificance of domain-specific vocabulary during pre-training for enhancing\nmodel performance. Ultimately, we observe that the combination of\ngeneral-domain knowledge and task-specific fine-tuning is essential for\nachieving optimal performance across a range of categories. This study offers\nvaluable insights for developing effective Turkish clinical language models and\ncan guide future research on pre-training techniques for other low-resource\nlanguages within the clinical domain.\n", "date": "2023-05-05", "categories": []}
{"id": "2305.04905", "title": "What Do Patients Say About Their Disease Symptoms? Deep Multilabel Text\n  Classification With Human-in-the-Loop Curation for Automatic Labeling of\n  Patient Self Reports of Problems", "abstract": "  The USA Food and Drug Administration has accorded increasing importance to\npatient-reported problems in clinical and research settings. In this paper, we\nexplore one of the largest online datasets comprising 170,141 open-ended\nself-reported responses (called \"verbatims\") from patients with Parkinson's\n(PwPs) to questions about what bothers them about their Parkinson's Disease and\nhow it affects their daily functioning, also known as the Parkinson's Disease\nPatient Report of Problems. Classifying such verbatims into multiple clinically\nrelevant symptom categories is an important problem and requires multiple steps\n- expert curation, a multi-label text classification (MLTC) approach and large\namounts of labelled training data. Further, human annotation of such large\ndatasets is tedious and expensive. We present a novel solution to this problem\nwhere we build a baseline dataset using 2,341 (of the 170,141) verbatims\nannotated by nine curators including clinical experts and PwPs. We develop a\nrules based linguistic-dictionary using NLP techniques and graph database-based\nexpert phrase-query system to scale the annotation to the remaining cohort\ngenerating the machine annotated dataset, and finally build a Keras-Tensorflow\nbased MLTC model for both datasets. The machine annotated model significantly\noutperforms the baseline model with a F1-score of 95% across 65 symptom\ncategories on a held-out test set.\n", "date": "2023-05-08", "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": "2304.00958", "title": "DrBERT: A Robust Pre-trained Model in French for Biomedical and Clinical\n  domains", "abstract": "  In recent years, pre-trained language models (PLMs) achieve the best\nperformance on a wide range of natural language processing (NLP) tasks. While\nthe first models were trained on general domain data, specialized ones have\nemerged to more effectively treat specific domains. In this paper, we propose\nan original study of PLMs in the medical domain on French language. We compare,\nfor the first time, the performance of PLMs trained on both public data from\nthe web and private data from healthcare establishments. We also evaluate\ndifferent learning strategies on a set of biomedical tasks. In particular, we\nshow that we can take advantage of already existing biomedical PLMs in a\nforeign language by further pre-train it on our targeted data. Finally, we\nrelease the first specialized PLMs for the biomedical field in French, called\nDrBERT, as well as the largest corpus of medical data under free license on\nwhich these models are trained.\n", "date": "2023-04-03", "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": "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": "2304.13180", "title": "Sebis at SemEval-2023 Task 7: A Joint System for Natural Language\n  Inference and Evidence Retrieval from Clinical Trial Reports", "abstract": "  With the increasing number of clinical trial reports generated every day, it\nis becoming hard to keep up with novel discoveries that inform evidence-based\nhealthcare recommendations. To help automate this process and assist medical\nexperts, NLP solutions are being developed. This motivated the SemEval-2023\nTask 7, where the goal was to develop an NLP system for two tasks: evidence\nretrieval and natural language inference from clinical trial data. In this\npaper, we describe our two developed systems. The first one is a pipeline\nsystem that models the two tasks separately, while the second one is a joint\nsystem that learns the two tasks simultaneously with a shared representation\nand a multi-task learning approach. The final system combines their outputs in\nan ensemble system. We formalize the models, present their characteristics and\nchallenges, and provide an analysis of achieved results. Our system ranked 3rd\nout of 40 participants with a final submission.\n", "date": "2023-04-25", "categories": []}
{"id": "2304.13714", "title": "Evaluation of GPT-3.5 and GPT-4 for supporting real-world information\n  needs in healthcare delivery", "abstract": "  Despite growing interest in using large language models (LLMs) in healthcare,\ncurrent explorations do not assess the real-world utility and safety of LLMs in\nclinical settings. Our objective was to determine whether two LLMs can serve\ninformation needs submitted by physicians as questions to an informatics\nconsultation service in a safe and concordant manner. Sixty six questions from\nan informatics consult service were submitted to GPT-3.5 and GPT-4 via simple\nprompts. 12 physicians assessed the LLM responses' possibility of patient harm\nand concordance with existing reports from an informatics consultation service.\nPhysician assessments were summarized based on majority vote. For no questions\ndid a majority of physicians deem either LLM response as harmful. For GPT-3.5,\nresponses to 8 questions were concordant with the informatics consult report,\n20 discordant, and 9 were unable to be assessed. There were 29 responses with\nno majority on \"Agree\", \"Disagree\", and \"Unable to assess\". For GPT-4,\nresponses to 13 questions were concordant, 15 discordant, and 3 were unable to\nbe assessed. There were 35 responses with no majority. Responses from both LLMs\nwere largely devoid of overt harm, but less than 20% of the responses agreed\nwith an answer from an informatics consultation service, responses contained\nhallucinated references, and physicians were divided on what constitutes harm.\nThese results suggest that while general purpose LLMs are able to provide safe\nand credible responses, they often do not meet the specific information need of\na given question. A definitive evaluation of the usefulness of LLMs in\nhealthcare settings will likely require additional research on prompt\nengineering, calibration, and custom-tailoring of general purpose models.\n", "date": "2023-04-26", "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": "2304.13875", "title": "MasonNLP+ at SemEval-2023 Task 8: Extracting Medical Questions,\n  Experiences and Claims from Social Media using Knowledge-Augmented\n  Pre-trained Language Models", "abstract": "  In online forums like Reddit, users share their experiences with medical\nconditions and treatments, including making claims, asking questions, and\ndiscussing the effects of treatments on their health. Building systems to\nunderstand this information can effectively monitor the spread of\nmisinformation and verify user claims. The Task-8 of the 2023 International\nWorkshop on Semantic Evaluation focused on medical applications, specifically\nextracting patient experience- and medical condition-related entities from user\nposts on social media. The Reddit Health Online Talk (RedHot) corpus contains\nposts from medical condition-related subreddits with annotations characterizing\nthe patient experience and medical conditions. In Subtask-1, patient experience\nis characterized by personal experience, questions, and claims. In Subtask-2,\nmedical conditions are characterized by population, intervention, and outcome.\nFor the automatic extraction of patient experiences and medical condition\ninformation, as a part of the challenge, we proposed language-model-based\nextraction systems that ranked $3^{rd}$ on both subtasks' leaderboards. In this\nwork, we describe our approach and, in addition, explore the automatic\nextraction of this information using domain-specific language models and the\ninclusion of external knowledge.\n", "date": "2023-04-26", "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": "2304.10909", "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": "2304.09138", "title": "Exploring the Trade-Offs: Unified Large Language Models vs Local\n  Fine-Tuned Models for Highly-Specific Radiology NLI Task", "abstract": "  Recently, ChatGPT and GPT-4 have emerged and gained immense global attention\ndue to their unparalleled performance in language processing. Despite\ndemonstrating impressive capability in various open-domain tasks, their\nadequacy in highly specific fields like radiology remains untested. Radiology\npresents unique linguistic phenomena distinct from open-domain data due to its\nspecificity and complexity. Assessing the performance of large language models\n(LLMs) in such specific domains is crucial not only for a thorough evaluation\nof their overall performance but also for providing valuable insights into\nfuture model design directions: whether model design should be generic or\ndomain-specific. To this end, in this study, we evaluate the performance of\nChatGPT/GPT-4 on a radiology NLI task and compare it to other models fine-tuned\nspecifically on task-related data samples. We also conduct a comprehensive\ninvestigation on ChatGPT/GPT-4's reasoning ability by introducing varying\nlevels of inference difficulty. Our results show that 1) GPT-4 outperforms\nChatGPT in the radiology NLI task; 2) other specifically fine-tuned models\nrequire significant amounts of data samples to achieve comparable performance\nto ChatGPT/GPT-4. These findings demonstrate that constructing a generic model\nthat is capable of solving various tasks across different domains is feasible.\n", "date": "2023-04-18", "categories": []}
{"id": "2209.06453", "title": "Prompt Combines Paraphrase: Teaching Pre-trained Models to Understand\n  Rare Biomedical Words", "abstract": "  Prompt-based fine-tuning for pre-trained models has proven effective for many\nnatural language processing tasks under few-shot settings in general domain.\nHowever, tuning with prompt in biomedical domain has not been investigated\nthoroughly. Biomedical words are often rare in general domain, but quite\nubiquitous in biomedical contexts, which dramatically deteriorates the\nperformance of pre-trained models on downstream biomedical applications even\nafter fine-tuning, especially in low-resource scenarios. We propose a simple\nyet effective approach to helping models learn rare biomedical words during\ntuning with prompt. Experimental results show that our method can achieve up to\n6% improvement in biomedical natural language inference task without any extra\nparameters or training steps using few-shot vanilla prompt settings.\n", "date": "2022-09-14", "categories": []}
{"id": "2212.07538", "title": "Leveraging Natural Language Processing to Augment Structured Social\n  Determinants of Health Data in the Electronic Health Record", "abstract": "  Objective: Social determinants of health (SDOH) impact health outcomes and\nare documented in the electronic health record (EHR) through structured data\nand unstructured clinical notes. However, clinical notes often contain more\ncomprehensive SDOH information, detailing aspects such as status, severity, and\ntemporality. This work has two primary objectives: i) develop a natural\nlanguage processing (NLP) information extraction model to capture detailed SDOH\ninformation and ii) evaluate the information gain achieved by applying the SDOH\nextractor to clinical narratives and combining the extracted representations\nwith existing structured data.\n  Materials and Methods: We developed a novel SDOH extractor using a deep\nlearning entity and relation extraction architecture to characterize SDOH\nacross various dimensions. In an EHR case study, we applied the SDOH extractor\nto a large clinical data set with 225,089 patients and 430,406 notes with\nsocial history sections and compared the extracted SDOH information with\nexisting structured data.\n  Results: The SDOH extractor achieved 0.86 F1 on a withheld test set. In the\nEHR case study, we found extracted SDOH information complements existing\nstructured data with 32% of homeless patients, 19% of current tobacco users,\nand 10% of drug users only having these health risk factors documented in the\nclinical narrative.\n  Conclusions: Utilizing EHR data to identify SDOH health risk factors and\nsocial needs may improve patient care and outcomes. Semantic representations of\ntext-encoded SDOH information can augment existing structured data, and this\nmore comprehensive SDOH representation can assist health systems in identifying\nand addressing these social needs.\n", "date": "2022-12-14", "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": "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": "2304.02768", "title": "Application of Transformers based methods in Electronic Medical Records:\n  A Systematic Literature Review", "abstract": "  The combined growth of available data and their unstructured nature has\nreceived increased interest in natural language processing (NLP) techniques to\nmake value of these data assets since this format is not suitable for\nstatistical analysis. This work presents a systematic literature review of\nstate-of-the-art advances using transformer-based methods on electronic medical\nrecords (EMRs) in different NLP tasks. To the best of our knowledge, this work\nis unique in providing a comprehensive review of research on transformer-based\nmethods for NLP applied to the EMR field. In the initial query, 99 articles\nwere selected from three public databases and filtered into 65 articles for\ndetailed analysis. The papers were analyzed with respect to the business\nproblem, NLP task, models and techniques, availability of datasets,\nreproducibility of modeling, language, and exchange format. The paper presents\nsome limitations of current research and some recommendations for further\nresearch.\n", "date": "2023-04-05", "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": "2304.01240", "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": "2304.01233", "title": "Multi-Modal Perceiver Language Model for Outcome Prediction in Emergency\n  Department", "abstract": "  Language modeling have shown impressive progress in generating compelling\ntext with good accuracy and high semantic coherence. An interesting research\ndirection is to augment these powerful models for specific applications using\ncontextual information. In this work, we explore multi-modal language modeling\nfor healthcare applications. We are interested in outcome prediction and\npatient triage in hospital emergency department based on text information in\nchief complaints and vital signs recorded at triage. We adapt Perceiver - a\nmodality-agnostic transformer-based model that has shown promising results in\nseveral applications. Since vital-sign modality is represented in tabular\nformat, we modified Perceiver position encoding to ensure permutation\ninvariance. We evaluated the multi-modal language model for the task of\ndiagnosis code prediction using MIMIC-IV ED dataset on 120K visits. In the\nexperimental analysis, we show that mutli-modality improves the prediction\nperformance compared with models trained solely on text or vital signs. We\nidentified disease categories for which multi-modality leads to performance\nimprovement and show that for these categories, vital signs have added\npredictive power. By analyzing the cross-attention layer, we show how\nmulti-modality contributes to model predictions. This work gives interesting\ninsights on the development of multi-modal language models for healthcare\napplications.\n", "date": "2023-04-03", "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": "2303.17071", "title": "DERA: Enhancing Large Language Model Completions with Dialog-Enabled\n  Resolving Agents", "abstract": "  Large language models (LLMs) have emerged as valuable tools for many natural\nlanguage understanding tasks. In safety-critical applications such as\nhealthcare, the utility of these models is governed by their ability to\ngenerate outputs that are factually accurate and complete. In this work, we\npresent dialog-enabled resolving agents (DERA). DERA is a paradigm made\npossible by the increased conversational abilities of LLMs, namely GPT-4. It\nprovides a simple, interpretable forum for models to communicate feedback and\niteratively improve output. We frame our dialog as a discussion between two\nagent types - a Researcher, who processes information and identifies crucial\nproblem components, and a Decider, who has the autonomy to integrate the\nResearcher's information and makes judgments on the final output.\n  We test DERA against three clinically-focused tasks. For medical conversation\nsummarization and care plan generation, DERA shows significant improvement over\nthe base GPT-4 performance in both human expert preference evaluations and\nquantitative metrics. In a new finding, we also show that GPT-4's performance\n(70%) on an open-ended version of the MedQA question-answering (QA) dataset\n(Jin et al. 2021, USMLE) is well above the passing level (60%), with DERA\nshowing similar performance. We release the open-ended MEDQA dataset at\nhttps://github.com/curai/curai-research/tree/main/DERA.\n", "date": "2023-03-30", "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": "2303.15846", "title": "Soft-prompt tuning to predict lung cancer using primary care free-text\n  Dutch medical notes", "abstract": "  We investigate different natural language processing (NLP) approaches based\non contextualised word representations for the problem of early prediction of\nlung cancer using free-text patient medical notes of Dutch primary care\nphysicians. Because lung cancer has a low prevalence in primary care, we also\naddress the problem of classification under highly imbalanced classes.\nSpecifically, we use large Transformer-based pretrained language models (PLMs)\nand investigate: 1) how \\textit{soft prompt-tuning} -- an NLP technique used to\nadapt PLMs using small amounts of training data -- compares to standard model\nfine-tuning; 2) whether simpler static word embedding models (WEMs) can be more\nrobust compared to PLMs in highly imbalanced settings; and 3) how models fare\nwhen trained on notes from a small number of patients. We find that 1)\nsoft-prompt tuning is an efficient alternative to standard model fine-tuning;\n2) PLMs show better discrimination but worse calibration compared to simpler\nstatic word embedding models as the classification problem becomes more\nimbalanced; and 3) results when training models on small number of patients are\nmixed and show no clear differences between PLMs and WEMs. All our code is\navailable open source in\n\\url{https://bitbucket.org/aumc-kik/prompt_tuning_cancer_prediction/}.\n", "date": "2023-03-28", "categories": []}
{"id": "2302.09685", "title": "Intent Identification and Entity Extraction for Healthcare Queries in\n  Indic Languages", "abstract": "  Scarcity of data and technological limitations for resource-poor languages in\ndeveloping countries like India poses a threat to the development of\nsophisticated NLU systems for healthcare. To assess the current status of\nvarious state-of-the-art language models in healthcare, this paper studies the\nproblem by initially proposing two different Healthcare datasets, Indian\nHealthcare Query Intent-WebMD and 1mg (IHQID-WebMD and IHQID-1mg) and one real\nworld Indian hospital query data in English and multiple Indic languages\n(Hindi, Bengali, Tamil, Telugu, Marathi and Gujarati) which are annotated with\nthe query intents as well as entities. Our aim is to detect query intents and\nextract corresponding entities. We perform extensive experiments on a set of\nmodels in various realistic settings and explore two scenarios based on the\naccess to English data only (less costly) and access to target language data\n(more expensive). We analyze context specific practical relevancy through\nempirical analysis. The results, expressed in terms of overall F1 score show\nthat our approach is practically useful to identify intents and entities.\n", "date": "2023-02-19", "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": "2303.12898", "title": "Towards Understanding the Generalization of Medical Text-to-SQL Models\n  and Datasets", "abstract": "  Electronic medical records (EMRs) are stored in relational databases. It can\nbe challenging to access the required information if the user is unfamiliar\nwith the database schema or general database fundamentals. Hence, researchers\nhave explored text-to-SQL generation methods that provide healthcare\nprofessionals direct access to EMR data without needing a database expert.\nHowever, currently available datasets have been essentially \"solved\" with\nstate-of-the-art models achieving accuracy greater than or near 90%. In this\npaper, we show that there is still a long way to go before solving text-to-SQL\ngeneration in the medical domain. To show this, we create new splits of the\nexisting medical text-to-SQL dataset MIMICSQL that better measure the\ngeneralizability of the resulting models. We evaluate state-of-the-art language\nmodels on our new split showing substantial drops in performance with accuracy\ndropping from up to 92% to 28%, thus showing substantial room for improvement.\nMoreover, we introduce a novel data augmentation approach to improve the\ngeneralizability of the language models. Overall, this paper is the first step\ntowards developing more robust text-to-SQL models in the medical\ndomain.\\footnote{The dataset and code will be released upon acceptance.\n", "date": "2023-03-22", "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": "2303.13451", "title": "Development and validation of a natural language processing algorithm to\n  pseudonymize documents in the context of a clinical data warehouse", "abstract": "  The objective of this study is to address the critical issue of\nde-identification of clinical reports in order to allow access to data for\nresearch purposes, while ensuring patient privacy. The study highlights the\ndifficulties faced in sharing tools and resources in this domain and presents\nthe experience of the Greater Paris University Hospitals (AP-HP) in\nimplementing a systematic pseudonymization of text documents from its Clinical\nData Warehouse. We annotated a corpus of clinical documents according to 12\ntypes of identifying entities, and built a hybrid system, merging the results\nof a deep learning model as well as manual rules. Our results show an overall\nperformance of 0.99 of F1-score. We discuss implementation choices and present\nexperiments to better understand the effort involved in such a task, including\ndataset size, document types, language models, or rule addition. We share\nguidelines and code under a 3-Clause BSD license.\n", "date": "2023-03-23", "categories": []}
{"id": "2212.05546", "title": "Associations Between Natural Language Processing (NLP) Enriched Social\n  Determinants of Health and Suicide Death among US Veterans", "abstract": "  Importance: Social determinants of health (SDOH) are known to be associated\nwith increased risk of suicidal behaviors, but few studies utilized SDOH from\nunstructured electronic health record (EHR) notes.\n  Objective: To investigate associations between suicide and recent SDOH,\nidentified using structured and unstructured data.\n  Design: Nested case-control study.\n  Setting: EHR data from the US Veterans Health Administration (VHA).\n  Participants: 6,122,785 Veterans who received care in the US VHA between\nOctober 1, 2010, and September 30, 2015.\n  Exposures: Occurrence of SDOH over a maximum span of two years compared with\nno occurrence of SDOH.\n  Main Outcomes and Measures: Cases of suicide deaths were matched with 4\ncontrols on birth year, cohort entry date, sex, and duration of follow-up. We\ndeveloped an NLP system to extract SDOH from unstructured notes. Structured\ndata, NLP on unstructured data, and combining them yielded six, eight and nine\nSDOH respectively. Adjusted odds ratios (aORs) and 95% confidence intervals\n(CIs) were estimated using conditional logistic regression.\n  Results: In our cohort, 8,821 Veterans committed suicide during 23,725,382\nperson-years of follow-up (incidence rate 37.18/100,000 person-years). Our\ncohort was mostly male (92.23%) and white (76.99%). Across the five common SDOH\nas covariates, NLP-extracted SDOH, on average, covered 80.03% of all SDOH\noccurrences. All SDOH, measured by structured data and NLP, were significantly\nassociated with increased risk of suicide. The SDOH with the largest effects\nwas legal problems (aOR=2.66, 95% CI=.46-2.89), followed by violence (aOR=2.12,\n95% CI=1.98-2.27). NLP-extracted and structured SDOH were also associated with\nsuicide.\n  Conclusions and Relevance: NLP-extracted SDOH were always significantly\nassociated with increased risk of suicide among Veterans, suggesting the\npotential of NLP in public health studies.\n", "date": "2022-12-11", "categories": []}
{"id": "2303.08335", "title": "FactReranker: Fact-guided Reranker for Faithful Radiology Report\n  Summarization", "abstract": "  Automatic radiology report summarization is a crucial clinical task, whose\nkey challenge is to maintain factual accuracy between produced summaries and\nground truth radiology findings. Existing research adopts reinforcement\nlearning to directly optimize factual consistency metrics such as CheXBert or\nRadGraph score. However, their decoding method using greedy search or beam\nsearch considers no factual consistency when picking the optimal candidate,\nleading to limited factual consistency improvement. To address it, we propose a\nnovel second-stage summarizing approach FactReranker, the first attempt that\nlearns to choose the best summary from all candidates based on their estimated\nfactual consistency score. We propose to extract medical facts of the input\nmedical report, its gold summary, and candidate summaries based on the RadGraph\nschema and design the fact-guided reranker to efficiently incorporate the\nextracted medical facts for selecting the optimal summary. We decompose the\nfact-guided reranker into the factual knowledge graph generation and the\nfactual scorer, which allows the reranker to model the mapping between the\nmedical facts of the input text and its gold summary, thus can select the\noptimal summary even the gold summary can't be observed during inference. We\nalso present a fact-based ranking metric (RadMRR) for measuring the ability of\nthe reranker on selecting factual consistent candidates. Experimental results\non two benchmark datasets demonstrate the superiority of our method in\ngenerating summaries with higher factual consistency scores when compared with\nexisting methods.\n", "date": "2023-03-15", "categories": []}
{"id": "2303.08928", "title": "Applying unsupervised keyphrase methods on concepts extracted from\n  discharge sheets", "abstract": "  Clinical notes containing valuable patient information are written by\ndifferent health care providers with various scientific levels and writing\nstyles. It might be helpful for clinicians and researchers to understand what\ninformation is essential when dealing with extensive electronic medical\nrecords. Entities recognizing and mapping them to standard terminologies is\ncrucial in reducing ambiguity in processing clinical notes. Although named\nentity recognition and entity linking are critical steps in clinical natural\nlanguage processing, they can also result in the production of repetitive and\nlow-value concepts. In other hand, all parts of a clinical text do not share\nthe same importance or content in predicting the patient's condition. As a\nresult, it is necessary to identify the section in which each content is\nrecorded and also to identify key concepts to extract meaning from clinical\ntexts. In this study, these challenges have been addressed by using clinical\nnatural language processing techniques. In addition, in order to identify key\nconcepts, a set of popular unsupervised key phrase extraction methods has been\nverified and evaluated. Considering that most of the clinical concepts are in\nthe form of multi-word expressions and their accurate identification requires\nthe user to specify n-gram range, we have proposed a shortcut method to\npreserve the structure of the expression based on TF-IDF. In order to evaluate\nthe pre-processing method and select the concepts, we have designed two types\nof downstream tasks (multiple and binary classification) using the capabilities\nof transformer-based models. The obtained results show the superiority of\nproposed method in combination with SciBERT model, also offer an insight into\nthe efficacy of general extracting essential phrase methods for clinical notes.\n", "date": "2023-03-15", "categories": []}
{"id": "2302.07322", "title": "TRESTLE: Toolkit for Reproducible Execution of Speech, Text and Language\n  Experiments", "abstract": "  The evidence is growing that machine and deep learning methods can learn the\nsubtle differences between the language produced by people with various forms\nof cognitive impairment such as dementia and cognitively healthy individuals.\nValuable public data repositories such as TalkBank have made it possible for\nresearchers in the computational community to join forces and learn from each\nother to make significant advances in this area. However, due to variability in\napproaches and data selection strategies used by various researchers, results\nobtained by different groups have been difficult to compare directly. In this\npaper, we present TRESTLE (\\textbf{T}oolkit for \\textbf{R}eproducible\n\\textbf{E}xecution of \\textbf{S}peech \\textbf{T}ext and \\textbf{L}anguage\n\\textbf{E}xperiments), an open source platform that focuses on two datasets\nfrom the TalkBank repository with dementia detection as an illustrative domain.\nSuccessfully deployed in the hackallenge (Hackathon/Challenge) of the\nInternational Workshop on Health Intelligence at AAAI 2022, TRESTLE provides a\nprecise digital blueprint of the data pre-processing and selection strategies\nthat can be reused via TRESTLE by other researchers seeking comparable results\nwith their peers and current state-of-the-art (SOTA) approaches.\n", "date": "2023-02-14", "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": "2303.08038", "title": "Progress Note Understanding -- Assessment and Plan Reasoning: Overview\n  of the 2022 N2C2 Track 3 Shared Task", "abstract": "  Daily progress notes are common types in the electronic health record (EHR)\nwhere healthcare providers document the patient's daily progress and treatment\nplans. The EHR is designed to document all the care provided to patients, but\nit also enables note bloat with extraneous information that distracts from the\ndiagnoses and treatment plans. Applications of natural language processing\n(NLP) in the EHR is a growing field with the majority of methods in information\nextraction. Few tasks use NLP methods for downstream diagnostic decision\nsupport. We introduced the 2022 National NLP Clinical Challenge (N2C2) Track 3:\nProgress Note Understanding - Assessment and Plan Reasoning as one step towards\na new suite of tasks. The Assessment and Plan Reasoning task focuses on the\nmost critical components of progress notes, Assessment and Plan subsections\nwhere health problems and diagnoses are contained. The goal of the task was to\ndevelop and evaluate NLP systems that automatically predict causal relations\nbetween the overall status of the patient contained in the Assessment section\nand its relation to each component of the Plan section which contains the\ndiagnoses and treatment plans. The goal of the task was to identify and\nprioritize diagnoses as the first steps in diagnostic decision support to find\nthe most relevant information in long documents like daily progress notes. We\npresent the results of 2022 n2c2 Track 3 and provide a description of the data,\nevaluation, participation and system performance.\n", "date": "2023-03-14", "categories": []}
{"id": "2303.06002", "title": "Is In-hospital Meta-information Useful for Abstractive Discharge Summary\n  Generation?", "abstract": "  During the patient's hospitalization, the physician must record daily\nobservations of the patient and summarize them into a brief document called\n\"discharge summary\" when the patient is discharged. Automated generation of\ndischarge summary can greatly relieve the physicians' burden, and has been\naddressed recently in the research community. Most previous studies of\ndischarge summary generation using the sequence-to-sequence architecture focus\non only inpatient notes for input. However, electric health records (EHR) also\nhave rich structured metadata (e.g., hospital, physician, disease, length of\nstay, etc.) that might be useful. This paper investigates the effectiveness of\nmedical meta-information for summarization tasks. We obtain four types of\nmeta-information from the EHR systems and encode each meta-information into a\nsequence-to-sequence model. Using Japanese EHRs, meta-information encoded\nmodels increased ROUGE-1 by up to 4.45 points and BERTScore by 3.77 points over\nthe vanilla Longformer. Also, we found that the encoded meta-information\nimproves the precisions of its related terms in the outputs. Our results showed\nthe benefit of the use of medical meta-information.\n", "date": "2023-03-10", "categories": []}
{"id": "2303.05392", "title": "Automatically Summarizing Evidence from Clinical Trials: A Prototype\n  Highlighting Current Challenges", "abstract": "  We present TrialsSummarizer, a system that aims to automatically summarize\nevidence presented in the set of randomized controlled trials most relevant to\na given query. Building on prior work, the system retrieves trial publications\nmatching a query specifying a combination of condition, intervention(s), and\noutcome(s), and ranks these according to sample size and estimated study\nquality. The top-k such studies are passed through a neural multi-document\nsummarization system, yielding a synopsis of these trials. We consider two\narchitectures: A standard sequence-to-sequence model based on BART and a\nmulti-headed architecture intended to provide greater transparency to\nend-users. Both models produce fluent and relevant summaries of evidence\nretrieved for queries, but their tendency to introduce unsupported statements\nrender them inappropriate for use in this domain at present. The proposed\narchitecture may help users verify outputs allowing users to trace generated\ntokens back to inputs.\n", "date": "2023-03-07", "categories": []}
{"id": "2303.03948", "title": "A Meta-Evaluation of Faithfulness Metrics for Long-Form Hospital-Course\n  Summarization", "abstract": "  Long-form clinical summarization of hospital admissions has real-world\nsignificance because of its potential to help both clinicians and patients. The\nfaithfulness of summaries is critical to their safe usage in clinical settings.\nTo better understand the limitations of abstractive systems, as well as the\nsuitability of existing evaluation metrics, we benchmark faithfulness metrics\nagainst fine-grained human annotations for model-generated summaries of a\npatient's Brief Hospital Course. We create a corpus of patient hospital\nadmissions and summaries for a cohort of HIV patients, each with complex\nmedical histories. Annotators are presented with summaries and source notes,\nand asked to categorize manually highlighted summary elements (clinical\nentities like conditions and medications as well as actions like \"following\nup\") into one of three categories: ``Incorrect,'' ``Missing,'' and ``Not in\nNotes.'' We meta-evaluate a broad set of proposed faithfulness metrics and,\nacross metrics, explore the importance of domain adaptation (e.g. the impact of\nin-domain pre-training and metric fine-tuning), the use of source-summary\nalignments, and the effects of distilling a single metric from an ensemble of\npre-existing metrics. Off-the-shelf metrics with no exposure to clinical text\ncorrelate well yet overly rely on summary extractiveness. As a practical guide\nto long-form clinical narrative summarization, we find that most metrics\ncorrelate best to human judgments when provided with one summary sentence at a\ntime and a minimal set of relevant source context.\n", "date": "2023-03-07", "categories": []}
{"id": "2303.01258", "title": "Domain-adapted large language models for classifying nuclear medicine\n  reports", "abstract": "  With the growing use of transformer-based language models in medicine, it is\nunclear how well these models generalize to nuclear medicine which has\ndomain-specific vocabulary and unique reporting styles. In this study, we\nevaluated the value of domain adaptation in nuclear medicine by adapting\nlanguage models for the purpose of 5-point Deauville score prediction based on\nclinical 18F-fluorodeoxyglucose (FDG) PET/CT reports. We retrospectively\nretrieved 4542 text reports and 1664 images for FDG PET/CT lymphoma exams from\n2008-2018 in our clinical imaging database. Deauville scores were removed from\nthe reports and then the remaining text in the reports was used as the model\ninput. Multiple general-purpose transformer language models were used to\nclassify the reports into Deauville scores 1-5. We then adapted the models to\nthe nuclear medicine domain using masked language modeling and assessed its\nimpact on classification performance. The language models were compared against\nvision models, a multimodal vision language model, and a nuclear medicine\nphysician with seven-fold Monte Carlo cross validation, reported are the mean\nand standard deviations. Domain adaption improved all language models. For\nexample, BERT improved from 61.3% five-class accuracy to 65.7% following domain\nadaptation. The best performing model (domain-adapted RoBERTa) achieved a\nfive-class accuracy of 77.4%, which was better than the physician's performance\n(66%), the best vision model's performance (48.1), and was similar to the\nmultimodal model's performance (77.2). Domain adaptation improved the\nperformance of large language models in interpreting nuclear medicine text\nreports.\n", "date": "2023-03-01", "categories": []}
{"id": "2303.01341", "title": "Matching-based Term Semantics Pre-training for Spoken Patient Query\n  Understanding", "abstract": "  Medical Slot Filling (MSF) task aims to convert medical queries into\nstructured information, playing an essential role in diagnosis dialogue\nsystems. However, the lack of sufficient term semantics learning makes existing\napproaches hard to capture semantically identical but colloquial expressions of\nterms in medical conversations. In this work, we formalize MSF into a matching\nproblem and propose a Term Semantics Pre-trained Matching Network (TSPMN) that\ntakes both terms and queries as input to model their semantic interaction. To\nlearn term semantics better, we further design two self-supervised objectives,\nincluding Contrastive Term Discrimination (CTD) and Matching-based Mask Term\nModeling (MMTM). CTD determines whether it is the masked term in the dialogue\nfor each given term, while MMTM directly predicts the masked ones. Experimental\nresults on two Chinese benchmarks show that TSPMN outperforms strong baselines,\nespecially in few-shot settings.\n", "date": "2023-03-02", "categories": []}
{"id": "2209.09485", "title": "Generalizing through Forgetting -- Domain Generalization for Symptom\n  Event Extraction in Clinical Notes", "abstract": "  Symptom information is primarily documented in free-text clinical notes and\nis not directly accessible for downstream applications. To address this\nchallenge, information extraction approaches that can handle clinical language\nvariation across different institutions and specialties are needed. In this\npaper, we present domain generalization for symptom extraction using\npretraining and fine-tuning data that differs from the target domain in terms\nof institution and/or specialty and patient population. We extract symptom\nevents using a transformer-based joint entity and relation extraction method.\nTo reduce reliance on domain-specific features, we propose a domain\ngeneralization method that dynamically masks frequent symptoms words in the\nsource domain. Additionally, we pretrain the transformer language model (LM) on\ntask-related unlabeled texts for better representation. Our experiments\nindicate that masking and adaptive pretraining methods can significantly\nimprove performance when the source domain is more distant from the target\ndomain.\n", "date": "2022-09-20", "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": "2302.09155", "title": "Med-EASi: Finely Annotated Dataset and Models for Controllable\n  Simplification of Medical Texts", "abstract": "  Automatic medical text simplification can assist providers with\npatient-friendly communication and make medical texts more accessible, thereby\nimproving health literacy. But curating a quality corpus for this task requires\nthe supervision of medical experts. In this work, we present\n$\\textbf{Med-EASi}$ ($\\underline{\\textbf{Med}}$ical dataset for\n$\\underline{\\textbf{E}}$laborative and $\\underline{\\textbf{A}}$bstractive\n$\\underline{\\textbf{Si}}$mplification), a uniquely crowdsourced and finely\nannotated dataset for supervised simplification of short medical texts. Its\n$\\textit{expert-layman-AI collaborative}$ annotations facilitate\n$\\textit{controllability}$ over text simplification by marking four kinds of\ntextual transformations: elaboration, replacement, deletion, and insertion. To\nlearn medical text simplification, we fine-tune T5-large with four different\nstyles of input-output combinations, leading to two control-free and two\ncontrollable versions of the model. We add two types of\n$\\textit{controllability}$ into text simplification, by using a multi-angle\ntraining approach: $\\textit{position-aware}$, which uses in-place annotated\ninputs and outputs, and $\\textit{position-agnostic}$, where the model only\nknows the contents to be edited, but not their positions. Our results show that\nour fine-grained annotations improve learning compared to the unannotated\nbaseline. Furthermore, $\\textit{position-aware}$ control generates better\nsimplification than the $\\textit{position-agnostic}$ one. The data and code are\navailable at https://github.com/Chandrayee/CTRL-SIMP.\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": "2301.05571", "title": "The 2022 n2c2/UW Shared Task on Extracting Social Determinants of Health", "abstract": "  Objective: The n2c2/UW SDOH Challenge explores the extraction of social\ndeterminant of health (SDOH) information from clinical notes. The objectives\ninclude the advancement of natural language processing (NLP) information\nextraction techniques for SDOH and clinical information more broadly. This\npaper presents the shared task, data, participating teams, performance results,\nand considerations for future work.\n  Materials and Methods: The task used the Social History Annotated Corpus\n(SHAC), which consists of clinical text with detailed event-based annotations\nfor SDOH events such as alcohol, drug, tobacco, employment, and living\nsituation. Each SDOH event is characterized through attributes related to\nstatus, extent, and temporality. The task includes three subtasks related to\ninformation extraction (Subtask A), generalizability (Subtask B), and learning\ntransfer (Subtask C). In addressing this task, participants utilized a range of\ntechniques, including rules, knowledge bases, n-grams, word embeddings, and\npretrained language models (LM).\n  Results: A total of 15 teams participated, and the top teams utilized\npretrained deep learning LM. The top team across all subtasks used a\nsequence-to-sequence approach achieving 0.901 F1 for Subtask A, 0.774 F1\nSubtask B, and 0.889 F1 for Subtask C.\n  Conclusions: Similar to many NLP tasks and domains, pretrained LM yielded the\nbest performance, including generalizability and learning transfer. An error\nanalysis indicates extraction performance varies by SDOH, with lower\nperformance achieved for conditions, like substance use and homelessness, that\nincrease health risks (risk factors) and higher performance achieved for\nconditions, like substance abstinence and living with family, that reduce\nhealth risks (protective factors).\n", "date": "2023-01-13", "categories": []}
{"id": "2210.04191", "title": "CHARD: Clinical Health-Aware Reasoning Across Dimensions for Text\n  Generation Models", "abstract": "  We motivate and introduce CHARD: Clinical Health-Aware Reasoning across\nDimensions, to investigate the capability of text generation models to act as\nimplicit clinical knowledge bases and generate free-flow textual explanations\nabout various health-related conditions across several dimensions. We collect\nand present an associated dataset, CHARDat, consisting of explanations about 52\nhealth conditions across three clinical dimensions. We conduct extensive\nexperiments using BART and T5 along with data augmentation, and perform\nautomatic, human, and qualitative analyses. We show that while our models can\nperform decently, CHARD is very challenging with strong potential for further\nexploration.\n", "date": "2022-10-09", "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": "2302.04001", "title": "Leveraging Summary Guidance on Medical Report Summarization", "abstract": "  This study presents three deidentified large medical text datasets, named\nDISCHARGE, ECHO and RADIOLOGY, which contain 50K, 16K and 378K pairs of report\nand summary that are derived from MIMIC-III, respectively. We implement\nconvincing baselines of automated abstractive summarization on the proposed\ndatasets with pre-trained encoder-decoder language models, including BERT2BERT,\nT5-large and BART. Further, based on the BART model, we leverage the sampled\nsummaries from the train set as prior knowledge guidance, for encoding\nadditional contextual representations of the guidance with the encoder and\nenhancing the decoding representations in the decoder. The experimental results\nconfirm the improvement of ROUGE scores and BERTScore made by the proposed\nmethod, outperforming the larger model T5-large.\n", "date": "2023-02-08", "categories": []}
{"id": "2302.04185", "title": "Efficient Joint Learning for Clinical Named Entity Recognition and\n  Relation Extraction Using Fourier Networks: A Use Case in Adverse Drug Events", "abstract": "  Current approaches for clinical information extraction are inefficient in\nterms of computational costs and memory consumption, hindering their\napplication to process large-scale electronic health records (EHRs). We propose\nan efficient end-to-end model, the Joint-NER-RE-Fourier (JNRF), to jointly\nlearn the tasks of named entity recognition and relation extraction for\ndocuments of variable length. The architecture uses positional encoding and\nunitary batch sizes to process variable length documents and uses a\nweight-shared Fourier network layer for low-complexity token mixing. Finally,\nwe reach the theoretical computational complexity lower bound for relation\nextraction using a selective pooling strategy and distance-aware attention\nweights with trainable polynomial distance functions. We evaluated the JNRF\narchitecture using the 2018 N2C2 ADE benchmark to jointly extract\nmedication-related entities and relations in variable-length EHR summaries.\nJNRF outperforms rolling window BERT with selective pooling by 0.42%, while\nbeing twice as fast to train. Compared to state-of-the-art BiLSTM-CRF\narchitectures on the N2C2 ADE benchmark, results show that the proposed\napproach trains 22 times faster and reduces GPU memory consumption by 1.75\nfolds, with a reasonable performance tradeoff of 90%, without the use of\nexternal tools, hand-crafted rules or post-processing. Given the significant\ncarbon footprint of deep learning models and the current energy crises, these\nmethods could support efficient and cleaner information extraction in EHRs and\nother types of large-scale document databases.\n", "date": "2023-02-08", "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": "2302.01025", "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": "2211.00201", "title": "CCS Explorer: Relevance Prediction, Extractive Summarization, and Named\n  Entity Recognition from Clinical Cohort Studies", "abstract": "  Clinical Cohort Studies (CCS), such as randomized clinical trials, are a\ngreat source of documented clinical research. Ideally, a clinical expert\ninspects these articles for exploratory analysis ranging from drug discovery\nfor evaluating the efficacy of existing drugs in tackling emerging diseases to\nthe first test of newly developed drugs. However, more than 100 articles are\npublished daily on a single prevalent disease like COVID-19 in PubMed. As a\nresult, it can take days for a physician to find articles and extract relevant\ninformation. Can we develop a system to sift through the long list of these\narticles faster and document the crucial takeaways from each of these articles?\nIn this work, we propose CCS Explorer, an end-to-end system for relevance\nprediction of sentences, extractive summarization, and patient, outcome, and\nintervention entity detection from CCS. CCS Explorer is packaged in a web-based\ngraphical user interface where the user can provide any disease name. CCS\nExplorer then extracts and aggregates all relevant information from articles on\nPubMed based on the results of an automatically generated query produced on the\nback-end. For each task, CCS Explorer fine-tunes pre-trained language\nrepresentation models based on transformers with additional layers. The models\nare evaluated using two publicly available datasets. CCS Explorer obtains a\nrecall of 80.2%, AUC-ROC of 0.843, and an accuracy of 88.3% on sentence\nrelevance prediction using BioBERT and achieves an average Micro F1-Score of\n77.8% on Patient, Intervention, Outcome detection (PIO) using PubMedBERT. Thus,\nCCS Explorer can reliably extract relevant information to summarize articles,\nsaving time by $\\sim \\text{660}\\times$.\n", "date": "2022-11-01", "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": "2209.14901", "title": "DR.BENCH: Diagnostic Reasoning Benchmark for Clinical Natural Language\n  Processing", "abstract": "  The meaningful use of electronic health records (EHR) continues to progress\nin the digital era with clinical decision support systems augmented by\nartificial intelligence. A priority in improving provider experience is to\novercome information overload and reduce the cognitive burden so fewer medical\nerrors and cognitive biases are introduced during patient care. One major type\nof medical error is diagnostic error due to systematic or predictable errors in\njudgment that rely on heuristics. The potential for clinical natural language\nprocessing (cNLP) to model diagnostic reasoning in humans with forward\nreasoning from data to diagnosis and potentially reduce the cognitive burden\nand medical error has not been investigated. Existing tasks to advance the\nscience in cNLP have largely focused on information extraction and named entity\nrecognition through classification tasks. We introduce a novel suite of tasks\ncoined as Diagnostic Reasoning Benchmarks, DR.BENCH, as a new benchmark for\ndeveloping and evaluating cNLP models with clinical diagnostic reasoning\nability. The suite includes six tasks from ten publicly available datasets\naddressing clinical text understanding, medical knowledge reasoning, and\ndiagnosis generation. DR.BENCH is the first clinical suite of tasks designed to\nbe a natural language generation framework to evaluate pre-trained language\nmodels. Experiments with state-of-the-art pre-trained generative language\nmodels using large general domain models and models that were continually\ntrained on a medical corpus demonstrate opportunities for improvement when\nevaluated in DR. BENCH. We share DR. BENCH as a publicly available GitLab\nrepository with a systematic approach to load and evaluate models for the cNLP\ncommunity.\n", "date": "2022-09-29", "categories": []}
{"id": "2301.11386", "title": "Task formulation for Extracting Social Determinants of Health from\n  Clinical Narratives", "abstract": "  Objective: The 2022 n2c2 NLP Challenge posed identification of social\ndeterminants of health (SDOH) in clinical narratives. We present three systems\nthat we developed for the Challenge and discuss the distinctive task\nformulation used in each of the three systems. Materials and Methods: The first\nsystem identifies target pieces of information independently using machine\nlearning classifiers. The second system uses a large language model (LLM) to\nextract complete structured outputs per document. The third system extracts\ncandidate phrases using machine learning and identifies target relations with\nhand-crafted rules. Results: The three systems achieved F1 scores of 0.884,\n0.831, and 0.663 in the Subtask A of the Challenge, which are ranked third,\nseventh, and eighth among the 15 participating teams. The review of the\nextraction results from our systems reveals characteristics of each approach\nand those of the SODH extraction task. Discussion: Phrases and relations\nannotated in the task is unique and diverse, not conforming to the conventional\nevent extraction task. These annotations are difficult to model with limited\ntraining data. The system that extracts information independently, ignoring the\nannotated relations, achieves the highest F1 score. Meanwhile, LLM with its\nversatile capability achieves the high F1 score, while respecting the annotated\nrelations. The rule-based system tackling relation extraction obtains the low\nF1 score, while it is the most explainable approach. Conclusion: The F1 scores\nof the three systems vary in this challenge setting, but each approach has\nadvantages and disadvantages in a practical application. The selection of the\napproach depends not only on the F1 score but also on the requirements in the\napplication.\n", "date": "2023-01-26", "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": "2301.11847", "title": "A Comparative Study of Pretrained Language Models for Long Clinical Text", "abstract": "  Objective: Clinical knowledge enriched transformer models (e.g.,\nClinicalBERT) have state-of-the-art results on clinical NLP (natural language\nprocessing) tasks. One of the core limitations of these transformer models is\nthe substantial memory consumption due to their full self-attention mechanism,\nwhich leads to the performance degradation in long clinical texts. To overcome\nthis, we propose to leverage long-sequence transformer models (e.g., Longformer\nand BigBird), which extend the maximum input sequence length from 512 to 4096,\nto enhance the ability to model long-term dependencies in long clinical texts.\n  Materials and Methods: Inspired by the success of long sequence transformer\nmodels and the fact that clinical notes are mostly long, we introduce two\ndomain enriched language models, Clinical-Longformer and Clinical-BigBird,\nwhich are pre-trained on a large-scale clinical corpus. We evaluate both\nlanguage models using 10 baseline tasks including named entity recognition,\nquestion answering, natural language inference, and document classification\ntasks.\n  Results: The results demonstrate that Clinical-Longformer and\nClinical-BigBird consistently and significantly outperform ClinicalBERT and\nother short-sequence transformers in all 10 downstream tasks and achieve new\nstate-of-the-art results.\n  Discussion: Our pre-trained language models provide the bedrock for clinical\nNLP using long texts. We have made our source code available at\nhttps://github.com/luoyuanlab/Clinical-Longformer, and the pre-trained models\navailable for public download at:\nhttps://huggingface.co/yikuan8/Clinical-Longformer.\n  Conclusion: This study demonstrates that clinical knowledge enriched\nlong-sequence transformers are able to learn long-term dependencies in long\nclinical text. Our methods can also inspire the development of other\ndomain-enriched long-sequence transformers.\n", "date": "2023-01-27", "categories": []}
{"id": "2209.06727", "title": "Automated Fidelity Assessment for Strategy Training in Inpatient\n  Rehabilitation using Natural Language Processing", "abstract": "  Strategy training is a multidisciplinary rehabilitation approach that teaches\nskills to reduce disability among those with cognitive impairments following a\nstroke. Strategy training has been shown in randomized, controlled clinical\ntrials to be a more feasible and efficacious intervention for promoting\nindependence than traditional rehabilitation approaches. A standardized\nfidelity assessment is used to measure adherence to treatment principles by\nexamining guided and directed verbal cues in video recordings of rehabilitation\nsessions. Although the fidelity assessment for detecting guided and directed\nverbal cues is valid and feasible for single-site studies, it can become labor\nintensive, time consuming, and expensive in large, multi-site pragmatic trials.\nTo address this challenge to widespread strategy training implementation, we\nleveraged natural language processing (NLP) techniques to automate the strategy\ntraining fidelity assessment, i.e., to automatically identify guided and\ndirected verbal cues from video recordings of rehabilitation sessions. We\ndeveloped a rule-based NLP algorithm, a long-short term memory (LSTM) model,\nand a bidirectional encoder representation from transformers (BERT) model for\nthis task. The best performance was achieved by the BERT model with a 0.8075\nF1-score. This BERT model was verified on an external validation dataset\ncollected from a separate major regional health system and achieved an F1 score\nof 0.8259, which shows that the BERT model generalizes well. The findings from\nthis study hold widespread promise in psychology and rehabilitation\nintervention research and practice.\n", "date": "2022-09-14", "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": "2301.09908", "title": "Cross-lingual German Biomedical Information Extraction: from Zero-shot\n  to Human-in-the-Loop", "abstract": "  This paper presents our project proposal for extracting biomedical\ninformation from German clinical narratives with limited amounts of\nannotations. We first describe the applied strategies in transfer learning and\nactive learning for solving our problem. After that, we discuss the design of\nthe user interface for both supplying model inspection and obtaining user\nannotations in the interactive environment.\n", "date": "2023-01-24", "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": "2212.13408", "title": "NEEDED: Introducing Hierarchical Transformer to Eye Diseases Diagnosis", "abstract": "  With the development of natural language processing techniques(NLP),\nautomatic diagnosis of eye diseases using ophthalmology electronic medical\nrecords (OEMR) has become possible. It aims to evaluate the condition of both\neyes of a patient respectively, and we formulate it as a particular multi-label\nclassification task in this paper. Although there are a few related studies in\nother diseases, automatic diagnosis of eye diseases exhibits unique\ncharacteristics. First, descriptions of both eyes are mixed up in OEMR\ndocuments, with both free text and templated asymptomatic descriptions,\nresulting in sparsity and clutter of information. Second, OEMR documents\ncontain multiple parts of descriptions and have long document lengths. Third,\nit is critical to provide explainability to the disease diagnosis model. To\novercome those challenges, we present an effective automatic eye disease\ndiagnosis framework, NEEDED. In this framework, a preprocessing module is\nintegrated to improve the density and quality of information. Then, we design a\nhierarchical transformer structure for learning the contextualized\nrepresentations of each sentence in the OEMR document. For the diagnosis part,\nwe propose an attention-based predictor that enables traceable diagnosis by\nobtaining disease-specific information. Experiments on the real dataset and\ncomparison with several baseline models show the advantage and explainability\nof our framework.\n", "date": "2022-12-27", "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": "2212.14882", "title": "ChatGPT Makes Medicine Easy to Swallow: An Exploratory Case Study on\n  Simplified Radiology Reports", "abstract": "  The release of ChatGPT, a language model capable of generating text that\nappears human-like and authentic, has gained significant attention beyond the\nresearch community. We expect that the convincing performance of ChatGPT\nincentivizes users to apply it to a variety of downstream tasks, including\nprompting the model to simplify their own medical reports. To investigate this\nphenomenon, we conducted an exploratory case study. In a questionnaire, we\nasked 15 radiologists to assess the quality of radiology reports simplified by\nChatGPT. Most radiologists agreed that the simplified reports were factually\ncorrect, complete, and not potentially harmful to the patient. Nevertheless,\ninstances of incorrect statements, missed key medical findings, and potentially\nharmful passages were reported. While further studies are needed, the initial\ninsights of this study indicate a great potential in using large language\nmodels like ChatGPT to improve patient-centered care in radiology and other\nmedical domains.\n", "date": "2022-12-30", "categories": []}
{"id": "2212.12800", "title": "A Marker-based Neural Network System for Extracting Social Determinants\n  of Health", "abstract": "  Objective. The impact of social determinants of health (SDoH) on patients'\nhealthcare quality and the disparity is well-known. Many SDoH items are not\ncoded in structured forms in electronic health records. These items are often\ncaptured in free-text clinical notes, but there are limited methods for\nautomatically extracting them. We explore a multi-stage pipeline involving\nnamed entity recognition (NER), relation classification (RC), and text\nclassification methods to extract SDoH information from clinical notes\nautomatically.\n  Materials and Methods. The study uses the N2C2 Shared Task data, which was\ncollected from two sources of clinical notes: MIMIC-III and University of\nWashington Harborview Medical Centers. It contains 4480 social history sections\nwith full annotation for twelve SDoHs. In order to handle the issue of\noverlapping entities, we developed a novel marker-based NER model. We used it\nin a multi-stage pipeline to extract SDoH information from clinical notes.\n  Results. Our marker-based system outperformed the state-of-the-art span-based\nmodels at handling overlapping entities based on the overall Micro-F1 score\nperformance. It also achieved state-of-the-art performance compared to the\nshared task methods.\n  Conclusion. The major finding of this study is that the multi-stage pipeline\neffectively extracts SDoH information from clinical notes. This approach can\npotentially improve the understanding and tracking of SDoHs in clinical\nsettings. However, error propagation may be an issue, and further research is\nneeded to improve the extraction of entities with complex semantic meanings and\nlow-resource entities using external knowledge.\n", "date": "2022-12-24", "categories": []}
{"id": "2212.13138", "title": "Large Language Models Encode Clinical Knowledge", "abstract": "  Large language models (LLMs) have demonstrated impressive capabilities in\nnatural language understanding and generation, but the quality bar for medical\nand clinical applications is high. Today, attempts to assess models' clinical\nknowledge typically rely on automated evaluations on limited benchmarks. There\nis no standard to evaluate model predictions and reasoning across a breadth of\ntasks. To address this, we present MultiMedQA, a benchmark combining six\nexisting open question answering datasets spanning professional medical exams,\nresearch, and consumer queries; and HealthSearchQA, a new free-response dataset\nof medical questions searched online. We propose a framework for human\nevaluation of model answers along multiple axes including factuality,\nprecision, possible harm, and bias. In addition, we evaluate PaLM (a\n540-billion parameter LLM) and its instruction-tuned variant, Flan-PaLM, on\nMultiMedQA. Using a combination of prompting strategies, Flan-PaLM achieves\nstate-of-the-art accuracy on every MultiMedQA multiple-choice dataset (MedQA,\nMedMCQA, PubMedQA, MMLU clinical topics), including 67.6% accuracy on MedQA (US\nMedical License Exam questions), surpassing prior state-of-the-art by over 17%.\nHowever, human evaluation reveals key gaps in Flan-PaLM responses. To resolve\nthis we introduce instruction prompt tuning, a parameter-efficient approach for\naligning LLMs to new domains using a few exemplars. The resulting model,\nMed-PaLM, performs encouragingly, but remains inferior to clinicians. We show\nthat comprehension, recall of knowledge, and medical reasoning improve with\nmodel scale and instruction prompt tuning, suggesting the potential utility of\nLLMs in medicine. Our human evaluations reveal important limitations of today's\nmodels, reinforcing the importance of both evaluation frameworks and method\ndevelopment in creating safe, helpful LLM models for clinical applications.\n", "date": "2022-12-26", "categories": []}
{"id": "2209.10041", "title": "Exploring Optimal Granularity for Extractive Summarization of\n  Unstructured Health Records: Analysis of the Largest Multi-Institutional\n  Archive of Health Records in Japan", "abstract": "  Automated summarization of clinical texts can reduce the burden of medical\nprofessionals. \"Discharge summaries\" are one promising application of the\nsummarization, because they can be generated from daily inpatient records. Our\npreliminary experiment suggests that 20-31% of the descriptions in discharge\nsummaries overlap with the content of the inpatient records. However, it\nremains unclear how the summaries should be generated from the unstructured\nsource. To decompose the physician's summarization process, this study aimed to\nidentify the optimal granularity in summarization. We first defined three types\nof summarization units with different granularities to compare the performance\nof the discharge summary generation: whole sentences, clinical segments, and\nclauses. We defined clinical segments in this study, aiming to express the\nsmallest medically meaningful concepts. To obtain the clinical segments, it was\nnecessary to automatically split the texts in the first stage of the pipeline.\nAccordingly, we compared rule-based methods and a machine learning method, and\nthe latter outperformed the formers with an F1 score of 0.846 in the splitting\ntask. Next, we experimentally measured the accuracy of extractive summarization\nusing the three types of units, based on the ROUGE-1 metric, on a\nmulti-institutional national archive of health records in Japan. The measured\naccuracies of extractive summarization using whole sentences, clinical\nsegments, and clauses were 31.91, 36.15, and 25.18, respectively. We found that\nthe clinical segments yielded higher accuracy than sentences and clauses. This\nresult indicates that summarization of inpatient records demands finer\ngranularity than sentence-oriented processing. Although we used only Japanese\nhealth records, it can be interpreted as follows: physicians extract \"concepts\nof medical significance\" from patient records and recombine them ...\n", "date": "2022-09-20", "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": "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": "2212.05150", "title": "Improving Precancerous Case Characterization via Transformer-based\n  Ensemble Learning", "abstract": "  The application of natural language processing (NLP) to cancer pathology\nreports has been focused on detecting cancer cases, largely ignoring\nprecancerous cases. Improving the characterization of precancerous adenomas\nassists in developing diagnostic tests for early cancer detection and\nprevention, especially for colorectal cancer (CRC). Here we developed\ntransformer-based deep neural network NLP models to perform the CRC\nphenotyping, with the goal of extracting precancerous lesion attributes and\ndistinguishing cancer and precancerous cases. We achieved 0.914 macro-F1 scores\nfor classifying patients into negative, non-advanced adenoma, advanced adenoma\nand CRC. We further improved the performance to 0.923 using an ensemble of\nclassifiers for cancer status classification and lesion size named entity\nrecognition (NER). Our results demonstrated the potential of using NLP to\nleverage real-world health record data to facilitate the development of\ndiagnostic tests for early cancer prevention.\n", "date": "2022-12-10", "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": "2212.04734", "title": "MED-SE: Medical Entity Definition-based Sentence Embedding", "abstract": "  We propose Medical Entity Definition-based Sentence Embedding (MED-SE), a\nnovel unsupervised contrastive learning framework designed for clinical texts,\nwhich exploits the definitions of medical entities. To this end, we conduct an\nextensive analysis of multiple sentence embedding techniques in clinical\nsemantic textual similarity (STS) settings. In the entity-centric setting that\nwe have designed, MED-SE achieves significantly better performance, while the\nexisting unsupervised methods including SimCSE show degraded performance. Our\nexperiments elucidate the inherent discrepancies between the general- and\nclinical-domain texts, and suggest that entity-centric contrastive approaches\nmay help bridge this gap and lead to a better representation of clinical\nsentences.\n", "date": "2022-12-09", "categories": []}
{"id": "2212.04891", "title": "HieNet: Bidirectional Hierarchy Framework for Automated ICD Coding", "abstract": "  International Classification of Diseases (ICD) is a set of classification\ncodes for medical records. Automated ICD coding, which assigns unique\nInternational Classification of Diseases codes with each medical record, is\nwidely used recently for its efficiency and error-prone avoidance. However,\nthere are challenges that remain such as heterogeneity, label unbalance, and\ncomplex relationships between ICD codes. In this work, we proposed a novel\nBidirectional Hierarchy Framework(HieNet) to address the challenges.\nSpecifically, a personalized PageRank routine is developed to capture the\nco-relation of codes, a bidirectional hierarchy passage encoder to capture the\ncodes' hierarchical representations, and a progressive predicting method is\nthen proposed to narrow down the semantic searching space of prediction. We\nvalidate our method on two widely used datasets. Experimental results on two\nauthoritative public datasets demonstrate that our proposed method boosts\nstate-of-the-art performance by a large margin.\n", "date": "2022-12-09", "categories": []}
{"id": "2212.03044", "title": "On the Importance of Clinical Notes in Multi-modal Learning for EHR Data", "abstract": "  Understanding deep learning model behavior is critical to accepting machine\nlearning-based decision support systems in the medical community. Previous\nresearch has shown that jointly using clinical notes with electronic health\nrecord (EHR) data improved predictive performance for patient monitoring in the\nintensive care unit (ICU). In this work, we explore the underlying reasons for\nthese improvements. While relying on a basic attention-based model to allow for\ninterpretability, we first confirm that performance significantly improves over\nstate-of-the-art EHR data models when combining EHR data and clinical notes. We\nthen provide an analysis showing improvements arise almost exclusively from a\nsubset of notes containing broader context on patient state rather than\nclinician notes. We believe such findings highlight deep learning models for\nEHR data to be more limited by partially-descriptive data than by modeling\nchoice, motivating a more data-centric approach in the field.\n", "date": "2022-12-06", "categories": []}
{"id": "2212.01462", "title": "Topic Modeling on Clinical Social Work Notes for Exploring Social\n  Determinants of Health Factors", "abstract": "  Most research studying social determinants of health (SDoH) has focused on\nphysician notes or structured elements of the electronic medical record (EMR).\nWe hypothesize that clinical notes from social workers, whose role is to\nameliorate social and economic factors, might provide a richer source of data\non SDoH. We sought to perform topic modeling to identify robust topics of\ndiscussion within a large cohort of social work notes. We retrieved a diverse,\ndeidentified corpus of 0.95 million clinical social work notes from 181,644\npatients at the University of California, San Francisco. We used word frequency\nanalysis and Latent Dirichlet Allocation (LDA) topic modeling analysis to\ncharacterize this corpus and identify potential topics of discussion. Word\nfrequency analysis identified both medical and non-medical terms associated\nwith specific ICD10 chapters. The LDA topic modeling analysis extracted 11\ntopics related to social determinants of health risk factors including\nfinancial status, abuse history, social support, risk of death, and mental\nhealth. In addition, the topic modeling approach captured the variation between\ndifferent types of social work notes and across patients with different types\nof diseases or conditions. We demonstrated that social work notes contain rich,\nunique, and otherwise unobtainable information on an individual's SDoH.\n", "date": "2022-12-02", "categories": []}
{"id": "2212.01552", "title": "Meta Learning for Few-Shot Medical Text Classification", "abstract": "  Medical professionals frequently work in a data constrained setting to\nprovide insights across a unique demographic. A few medical observations, for\ninstance, informs the diagnosis and treatment of a patient. This suggests a\nunique setting for meta-learning, a method to learn models quickly on new\ntasks, to provide insights unattainable by other methods. We investigate the\nuse of meta-learning and robustness techniques on a broad corpus of benchmark\ntext and medical data. To do this, we developed new data pipelines, combined\nlanguage models with meta-learning approaches, and extended existing\nmeta-learning algorithms to minimize worst case loss. We find that\nmeta-learning on text is a suitable framework for text-based data, providing\nbetter data efficiency and comparable performance to few-shot language models\nand can be successfully applied to medical note data. Furthermore,\nmeta-learning models coupled with DRO can improve worst case loss across\ndisease codes.\n", "date": "2022-12-03", "categories": []}
{"id": "2212.01669", "title": "A Survey on Medical Document Summarization", "abstract": "  The internet has had a dramatic effect on the healthcare industry, allowing\ndocuments to be saved, shared, and managed digitally. This has made it easier\nto locate and share important data, improving patient care and providing more\nopportunities for medical studies. As there is so much data accessible to\ndoctors and patients alike, summarizing it has become increasingly necessary -\nthis has been supported through the introduction of deep learning and\ntransformer-based networks, which have boosted the sector significantly in\nrecent years. This paper gives a comprehensive survey of the current techniques\nand trends in medical summarization\n", "date": "2022-12-03", "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": "2211.13813", "title": "Multi-label Few-shot ICD Coding as Autoregressive Generation with Prompt", "abstract": "  Automatic International Classification of Diseases (ICD) coding aims to\nassign multiple ICD codes to a medical note with an average of 3,000+ tokens.\nThis task is challenging due to the high-dimensional space of multi-label\nassignment (155,000+ ICD code candidates) and the long-tail challenge - Many\nICD codes are infrequently assigned yet infrequent ICD codes are important\nclinically. This study addresses the long-tail challenge by transforming this\nmulti-label classification task into an autoregressive generation task.\nSpecifically, we first introduce a novel pretraining objective to generate free\ntext diagnoses and procedure using the SOAP structure, the medical logic\nphysicians use for note documentation. Second, instead of directly predicting\nthe high dimensional space of ICD codes, our model generates the lower\ndimension of text descriptions, which then infer ICD codes. Third, we designed\na novel prompt template for multi-label classification. We evaluate our\nGeneration with Prompt model with the benchmark of all code assignment\n(MIMIC-III-full) and few shot ICD code assignment evaluation benchmark\n(MIMIC-III-few). Experiments on MIMIC-III-few show that our model performs with\na marco F1 30.2, which substantially outperforms the previous MIMIC-III-full\nSOTA model (marco F1 4.3) and the model specifically designed for few/zero shot\nsetting (marco F1 18.7). Finally, we design a novel ensemble learner, a cross\nattention reranker with prompts, to integrate previous SOTA and our best\nfew-shot coding predictions. Experiments on MIMIC-III-full show that our\nensemble learner substantially improves both macro and micro F1, from 10.4 to\n14.6 and from 58.2 to 59.1, respectively.\n", "date": "2022-11-24", "categories": []}
{"id": "2211.14539", "title": "An Automatic SOAP Classification System Using Weakly Supervision And\n  Transfer Learning", "abstract": "  In this paper, we introduce a comprehensive framework for developing a\nmachine learning-based SOAP (Subjective, Objective, Assessment, and Plan)\nclassification system without manually SOAP annotated training data or with\nless manually SOAP annotated training data. The system is composed of the\nfollowing two parts: 1) Data construction, 2) A neural network-based SOAP\nclassifier, and 3) Transfer learning framework. In data construction, since a\nmanual construction of a large size training dataset is expensive, we propose a\nrule-based weak labeling method utilizing the structured information of an EHR\nnote. Then, we present a SOAP classifier composed of a pre-trained language\nmodel and bi-directional long-short term memory with conditional random field\n(Bi-LSTM-CRF). Finally, we propose a transfer learning framework that re-uses\nthe trained parameters of the SOAP classifier trained with the weakly labeled\ndataset for datasets collected from another hospital. The proposed weakly\nlabel-based learning model successfully performed SOAP classification (89.99\nF1-score) on the notes collected from the target hospital. Otherwise, in the\nnotes collected from other hospitals and departments, the performance\ndramatically decreased. Meanwhile, we verified that the transfer learning\nframework is advantageous for inter-hospital adaptation of the model increasing\nthe models' performance in every cases. In particular, the transfer learning\napproach was more efficient when the manually annotated data size was smaller.\nWe showed that SOAP classification models trained with our weakly labeling\nalgorithm can perform SOAP classification without manually annotated data on\nthe EHR notes from the same hospital. The transfer learning framework helps\nSOAP classification model's inter-hospital migration with a minimal size of the\nmanually annotated dataset.\n", "date": "2022-11-26", "categories": []}
{"id": "2211.12512", "title": "NLP meets psychotherapy: Using predicted client emotions and\n  self-reported client emotions to measure emotional coherence", "abstract": "  Emotions are experienced and expressed through various response systems.\nCoherence between emotional experience and emotional expression is considered\nimportant to clients' well being. To date, emotional coherence (EC) has been\nstudied at a single time point using lab-based tasks with relatively small\ndatasets. No study has examined EC between the subjective experience of\nemotions and emotion expression in therapy or whether this coherence is\nassociated with clients' well being. Natural language Processing (NLP)\napproaches have been applied to identify emotions from psychotherapy dialogue,\nwhich can be implemented to study emotional processes on a larger scale.\nHowever, these methods have yet to be used to study coherence between emotional\nexperience and emotional expression over the course of therapy and whether it\nrelates to clients' well-being. This work presents an end-to-end approach where\nwe use emotion predictions from our transformer based emotion recognition model\nto study emotional coherence and its diagnostic potential in psychotherapy\nresearch. We first employ our transformer based approach on a Hebrew\npsychotherapy dataset to automatically label clients' emotions at utterance\nlevel in psychotherapy dialogues. We subsequently investigate the emotional\ncoherence between clients' self-reported emotional states and our model-based\nemotion predictions. We also examine the association between emotional\ncoherence and clients' well being. Our findings indicate a significant\ncorrelation between clients' self-reported emotions and positive and negative\nemotions expressed verbally during psychotherapy sessions. Coherence in\npositive emotions was also highly correlated with clients well-being. These\nresults illustrate how NLP can be applied to identify important emotional\nprocesses in psychotherapy to improve diagnosis and treatment for clients\nsuffering from mental-health problems.\n", "date": "2022-11-22", "categories": []}
{"id": "2207.05817", "title": "OSLAT: Open Set Label Attention Transformer for Medical Entity Retrieval\n  and Span Extraction", "abstract": "  Medical entity span extraction and linking are critical steps for many\nhealthcare NLP tasks. Most existing entity extraction methods either have a\nfixed vocabulary of medical entities or require span annotations. In this\npaper, we propose a method for linking an open set of entities that does not\nrequire any span annotations. Our method, Open Set Label Attention Transformer\n(OSLAT), uses the label-attention mechanism to learn candidate-entity\ncontextualized text representations. We find that OSLAT can not only link\nentities but is also able to implicitly learn spans associated with entities.\nWe evaluate OSLAT on two tasks: (1) span extraction trained without explicit\nspan annotations, and (2) entity linking trained without span-level annotation.\nWe test the generalizability of our method by training two separate models on\ntwo datasets with low entity overlap and comparing cross-dataset performance.\n", "date": "2022-07-12", "categories": []}
{"id": "2211.09455", "title": "Consultation Checklists: Standardising the Human Evaluation of Medical\n  Note Generation", "abstract": "  Evaluating automatically generated text is generally hard due to the\ninherently subjective nature of many aspects of the output quality. This\ndifficulty is compounded in automatic consultation note generation by differing\nopinions between medical experts both about which patient statements should be\nincluded in generated notes and about their respective importance in arriving\nat a diagnosis. Previous real-world evaluations of note-generation systems saw\nsubstantial disagreement between expert evaluators. In this paper we propose a\nprotocol that aims to increase objectivity by grounding evaluations in\nConsultation Checklists, which are created in a preliminary step and then used\nas a common point of reference during quality assessment. We observed good\nlevels of inter-annotator agreement in a first evaluation study using the\nprotocol; further, using Consultation Checklists produced in the study as\nreference for automatic metrics such as ROUGE or BERTScore improves their\ncorrelation with human judgements compared to using the original human note.\n", "date": "2022-11-17", "categories": []}
{"id": "2211.09722", "title": "Federated Multilingual Models for Medical Transcript Analysis", "abstract": "  Federated Learning (FL) is a novel machine learning approach that allows the\nmodel trainer to access more data samples, by training the model across\nmultiple decentralized data sources, while data access constraints are in\nplace. Such trained models can achieve significantly higher performance beyond\nwhat can be done when trained on a single data source. As part of FL's\npromises, none of the training data is ever transmitted to any central\nlocation, ensuring that sensitive data remains local and private. These\ncharacteristics make FL perfectly suited for large-scale applications in\nhealthcare, where a variety of compliance constraints restrict how data may be\nhandled, processed, and stored. Despite the apparent benefits of federated\nlearning, the heterogeneity in the local data distributions pose significant\nchallenges, and such challenges are even more pronounced in the case of\nmultilingual data providers. In this paper we present a federated learning\nsystem for training a large-scale multi-lingual model suitable for fine-tuning\non downstream tasks such as medical entity tagging. Our work represents one of\nthe first such production-scale systems, capable of training across multiple\nhighly heterogeneous data providers, and achieving levels of accuracy that\ncould not be otherwise achieved by using central training with public data.\nFinally, we show that the global model performance can be further improved by a\ntraining step performed locally.\n", "date": "2022-11-04", "categories": []}
{"id": "2211.07047", "title": "Language Model Classifier Aligns Better with Physician Word Sensitivity\n  than XGBoost on Readmission Prediction", "abstract": "  Traditional evaluation metrics for classification in natural language\nprocessing such as accuracy and area under the curve fail to differentiate\nbetween models with different predictive behaviors despite their similar\nperformance metrics. We introduce sensitivity score, a metric that scrutinizes\nmodels' behaviors at the vocabulary level to provide insights into disparities\nin their decision-making logic. We assess the sensitivity score on a set of\nrepresentative words in the test set using two classifiers trained for hospital\nreadmission classification with similar performance statistics. Our experiments\ncompare the decision-making logic of clinicians and classifiers based on rank\ncorrelations of sensitivity scores. The results indicate that the language\nmodel's sensitivity score aligns better with the professionals than the xgboost\nclassifier on tf-idf embeddings, which suggests that xgboost uses some spurious\nfeatures. Overall, this metric offers a novel perspective on assessing models'\nrobustness by quantifying their discrepancy with professional opinions. Our\ncode is available on GitHub (https://github.com/nyuolab/Model_Sensitivity).\n", "date": "2022-11-13", "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": "2211.07703", "title": "Alzheimer's Diagnosis and Generation-Based Chatbot Using Hierarchical\n  Attention and Transformer", "abstract": "  In this paper, we propose a natural language processing architecture that can\nhandle tasks that previously required two models as one model. With a single\nmodel, we analyze the language patterns and conversational context of\nAlzheimer's patients and derive answers from two results: patient\nclassification and chatbot. If the patient's language characteristics are\nidentified by chatbots in daily life, doctors can plan more precise diagnosis\nand treatment for early diagnosis. The proposed model is used to develop\nchatbots that replace questionnaires that required experts. There are two\nnatural language processing tasks performed by the model. The first is a\n'natural language classification' that indicates with probability whether the\npatient has an illness, and the second is to generate the next 'answer' of the\nchatbot to the patient's answer. In the first half, a context vector, which is\na characteristic of patient utterance, is extracted through a self-attention\nneural network. This context vector and chatbot (expert, moderator) questions\nare entered together into the encoder to obtain a matrix containing the\ncharacteristics of the interaction between the questioner and the patient. The\nvectorized matrix becomes a probability value for classification of patients.\nEnter the matrix into the decoder with the next answer from the chatbot (the\nmoderator) to generate the next utterance. As a result of learning this\nstructure with DmentiaBank's cookie theft description corpus, it was confirmed\nthat the value of the loss function of the encoder and decoder was\nsignificantly reduced and converged. This shows that capturing the speech\nlanguage pattern of Alzheimer's disease patients can contribute to early\ndiagnosis and longitudinal studies of the disease in the future.\n", "date": "2022-10-11", "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": "2211.06778", "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": "2211.05980", "title": "Hardness-guided domain adaptation to recognise biomedical named entities\n  under low-resource scenarios", "abstract": "  Domain adaptation is an effective solution to data scarcity in low-resource\nscenarios. However, when applied to token-level tasks such as bioNER, domain\nadaptation methods often suffer from the challenging linguistic characteristics\nthat clinical narratives possess, which leads to unsatisfactory performance. In\nthis paper, we present a simple yet effective hardness-guided domain adaptation\n(HGDA) framework for bioNER tasks that can effectively leverage the domain\nhardness information to improve the adaptability of the learnt model in\nlow-resource scenarios. Experimental results on biomedical datasets show that\nour model can achieve significant performance improvement over the recently\npublished state-of-the-art (SOTA) MetaNER model\n", "date": "2022-11-11", "categories": []}
{"id": "2211.05557", "title": "Assistive Completion of Agrammatic Aphasic Sentences: A Transfer\n  Learning Approach using Neurolinguistics-based Synthetic Dataset", "abstract": "  Damage to the inferior frontal gyrus (Broca's area) can cause agrammatic\naphasia wherein patients, although able to comprehend, lack the ability to form\ncomplete sentences. This inability leads to communication gaps which cause\ndifficulties in their daily lives. The usage of assistive devices can help in\nmitigating these issues and enable the patients to communicate effectively.\nHowever, due to lack of large scale studies of linguistic deficits in aphasia,\nresearch on such assistive technology is relatively limited. In this work, we\npresent two contributions that aim to re-initiate research and development in\nthis field. Firstly, we propose a model that uses linguistic features from\nsmall scale studies on aphasia patients and generates large scale datasets of\nsynthetic aphasic utterances from grammatically correct datasets. We show that\nthe mean length of utterance, the noun/verb ratio, and the simple/complex\nsentence ratio of our synthetic datasets correspond to the reported features of\naphasic speech. Further, we demonstrate how the synthetic datasets may be\nutilized to develop assistive devices for aphasia patients. The pre-trained T5\ntransformer is fine-tuned using the generated dataset to suggest 5 corrected\nsentences given an aphasic utterance as input. We evaluate the efficacy of the\nT5 model using the BLEU and cosine semantic similarity scores. Affirming\nresults with BLEU score of 0.827/1.00 and semantic similarity of 0.904/1.00\nwere obtained. These results provide a strong foundation for the concept that a\nsynthetic dataset based on small scale studies on aphasia can be used to\ndevelop effective assistive technology.\n", "date": "2022-11-10", "categories": []}
{"id": "2211.04569", "title": "Toward a Neural Semantic Parsing System for EHR Question Answering", "abstract": "  Clinical semantic parsing (SP) is an important step toward identifying the\nexact information need (as a machine-understandable logical form) from a\nnatural language query aimed at retrieving information from electronic health\nrecords (EHRs). Current approaches to clinical SP are largely based on\ntraditional machine learning and require hand-building a lexicon. The recent\nadvancements in neural SP show a promise for building a robust and flexible\nsemantic parser without much human effort. Thus, in this paper, we aim to\nsystematically assess the performance of two such neural SP models for EHR\nquestion answering (QA). We found that the performance of these advanced neural\nmodels on two clinical SP datasets is promising given their ease of application\nand generalizability. Our error analysis surfaces the common types of errors\nmade by these models and has the potential to inform future research into\nimproving the performance of neural SP models for EHR QA.\n", "date": "2022-11-08", "categories": []}
{"id": "2211.04759", "title": "Nested Named Entity Recognition from Medical Texts: An Adaptive Shared\n  Network Architecture with Attentive CRF", "abstract": "  Recognizing useful named entities plays a vital role in medical information\nprocessing, which helps drive the development of medical area research. Deep\nlearning methods have achieved good results in medical named entity recognition\n(NER). However, we find that existing methods face great challenges when\ndealing with the nested named entities. In this work, we propose a novel\nmethod, referred to as ASAC, to solve the dilemma caused by the nested\nphenomenon, in which the core idea is to model the dependency between different\ncategories of entity recognition. The proposed method contains two key modules:\nthe adaptive shared (AS) part and the attentive conditional random field (ACRF)\nmodule. The former part automatically assigns adaptive weights across each task\nto achieve optimal recognition accuracy in the multi-layer network. The latter\nmodule employs the attention operation to model the dependency between\ndifferent entities. In this way, our model could learn better entity\nrepresentations by capturing the implicit distinctions and relationships\nbetween different categories of entities. Extensive experiments on public\ndatasets verify the effectiveness of our method. Besides, we also perform\nablation analyses to deeply understand our methods.\n", "date": "2022-11-09", "categories": []}
{"id": "2211.02519", "title": "BERT for Long Documents: A Case Study of Automated ICD Coding", "abstract": "  Transformer models have achieved great success across many NLP problems.\nHowever, previous studies in automated ICD coding concluded that these models\nfail to outperform some of the earlier solutions such as CNN-based models. In\nthis paper we challenge this conclusion. We present a simple and scalable\nmethod to process long text with the existing transformer models such as BERT.\nWe show that this method significantly improves the previous results reported\nfor transformer models in ICD coding, and is able to outperform one of the\nprominent CNN-based methods.\n", "date": "2022-11-04", "categories": []}
{"id": "2210.11947", "title": "Generalizing over Long Tail Concepts for Medical Term Normalization", "abstract": "  Medical term normalization consists in mapping a piece of text to a large\nnumber of output classes. Given the small size of the annotated datasets and\nthe extremely long tail distribution of the concepts, it is of utmost\nimportance to develop models that are capable to generalize to scarce or unseen\nconcepts. An important attribute of most target ontologies is their\nhierarchical structure. In this paper we introduce a simple and effective\nlearning strategy that leverages such information to enhance the\ngeneralizability of both discriminative and generative models. The evaluation\nshows that the proposed strategy produces state-of-the-art performance on seen\nconcepts and consistent improvements on unseen ones, allowing also for\nefficient zero-shot knowledge transfer across text typologies and datasets.\n", "date": "2022-10-21", "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": "2210.17161", "title": "Improving Cause-of-Death Classification from Verbal Autopsy Reports", "abstract": "  In many lower-and-middle income countries including South Africa, data access\nin health facilities is restricted due to patient privacy and confidentiality\npolicies. Further, since clinical data is unique to individual institutions and\nlaboratories, there are insufficient data annotation standards and conventions.\nAs a result of the scarcity of textual data, natural language processing (NLP)\ntechniques have fared poorly in the health sector. A cause of death (COD) is\noften determined by a verbal autopsy (VA) report in places without reliable\ndeath registration systems. A non-clinician field worker does a VA report using\na set of standardized questions as a guide to uncover symptoms of a COD. This\nanalysis focuses on the textual part of the VA report as a case study to\naddress the challenge of adapting NLP techniques in the health domain. We\npresent a system that relies on two transfer learning paradigms of monolingual\nlearning and multi-source domain adaptation to improve VA narratives for the\ntarget task of the COD classification. We use the Bidirectional Encoder\nRepresentations from Transformers (BERT) and Embeddings from Language Models\n(ELMo) models pre-trained on the general English and health domains to extract\nfeatures from the VA narratives. Our findings suggest that this transfer\nlearning system improves the COD classification tasks and that the narrative\ntext contains valuable information for figuring out a COD. Our results further\nshow that combining binary VA features and narrative text features learned via\nthis framework boosts the classification task of COD.\n", "date": "2022-10-31", "categories": []}
{"id": "2210.15882", "title": "Can Current Explainability Help Provide References in Clinical Notes to\n  Support Humans Annotate Medical Codes?", "abstract": "  The medical codes prediction problem from clinical notes has received\nsubstantial interest in the NLP community, and several recent studies have\nshown the state-of-the-art (SOTA) code prediction results of full-fledged deep\nlearning-based methods. However, most previous SOTA works based on deep\nlearning are still in early stages in terms of providing textual references and\nexplanations of the predicted codes, despite the fact that this level of\nexplainability of the prediction outcomes is critical to gaining trust from\nprofessional medical coders. This raises the important question of how well\ncurrent explainability methods apply to advanced neural network models such as\ntransformers to predict correct codes and present references in clinical notes\nthat support code prediction. First, we present an explainable Read, Attend,\nand Code (xRAC) framework and assess two approaches, attention score-based\nxRAC-ATTN and model-agnostic knowledge-distillation-based xRAC-KD, through\nsimplified but thorough human-grounded evaluations with SOTA transformer-based\nmodel, RAC. We find that the supporting evidence text highlighted by xRAC-ATTN\nis of higher quality than xRAC-KD whereas xRAC-KD has potential advantages in\nproduction deployment scenarios. More importantly, we show for the first time\nthat, given the current state of explainability methodologies, using the SOTA\nmedical codes prediction system still requires the expertise and competencies\nof professional coders, even though its prediction accuracy is superior to that\nof human coders. This, we believe, is a very meaningful step toward developing\nexplainable and accurate machine learning systems for fully autonomous medical\ncode prediction from clinical notes.\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": "2210.15603", "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": "2210.14254", "title": "Leveraging Open Data and Task Augmentation to Automated Behavioral\n  Coding of Psychotherapy Conversations in Low-Resource Scenarios", "abstract": "  In psychotherapy interactions, the quality of a session is assessed by\ncodifying the communicative behaviors of participants during the conversation\nthrough manual observation and annotation. Developing computational approaches\nfor automated behavioral coding can reduce the burden on human coders and\nfacilitate the objective evaluation of the intervention. In the real world,\nhowever, implementing such algorithms is associated with data sparsity\nchallenges since privacy concerns lead to limited available in-domain data. In\nthis paper, we leverage a publicly available conversation-based dataset and\ntransfer knowledge to the low-resource behavioral coding task by performing an\nintermediate language model training via meta-learning. We introduce a task\naugmentation method to produce a large number of \"analogy tasks\" - tasks\nsimilar to the target one - and demonstrate that the proposed framework\npredicts target behaviors more accurately than all the other baseline models.\n", "date": "2022-10-25", "categories": []}
{"id": "2210.12186", "title": "Improving the Factual Correctness of Radiology Report Generation with\n  Semantic Rewards", "abstract": "  Neural image-to-text radiology report generation systems offer the potential\nto improve radiology reporting by reducing the repetitive process of report\ndrafting and identifying possible medical errors. These systems have achieved\npromising performance as measured by widely used NLG metrics such as BLEU and\nCIDEr. However, the current systems face important limitations. First, they\npresent an increased complexity in architecture that offers only marginal\nimprovements on NLG metrics. Secondly, these systems that achieve high\nperformance on these metrics are not always factually complete or consistent\ndue to both inadequate training and evaluation. Recent studies have shown the\nsystems can be substantially improved by using new methods encouraging 1) the\ngeneration of domain entities consistent with the reference and 2) describing\nthese entities in inferentially consistent ways. So far, these methods rely on\nweakly-supervised approaches (rule-based) and named entity recognition systems\nthat are not specific to the chest X-ray domain. To overcome this limitation,\nwe propose a new method, the RadGraph reward, to further improve the factual\ncompleteness and correctness of generated radiology reports. More precisely, we\nleverage the RadGraph dataset containing annotated chest X-ray reports with\nentities and relations between entities. On two open radiology report datasets,\nour system substantially improves the scores up to 14.2% and 25.3% on metrics\nevaluating the factual correctness and completeness of reports.\n", "date": "2022-10-21", "categories": []}
{"id": "2210.12560", "title": "PHEE: A Dataset for Pharmacovigilance Event Extraction from Text", "abstract": "  The primary goal of drug safety researchers and regulators is to promptly\nidentify adverse drug reactions. Doing so may in turn prevent or reduce the\nharm to patients and ultimately improve public health. Evaluating and\nmonitoring drug safety (i.e., pharmacovigilance) involves analyzing an ever\ngrowing collection of spontaneous reports from health professionals,\nphysicians, and pharmacists, and information voluntarily submitted by patients.\nIn this scenario, facilitating analysis of such reports via automation has the\npotential to rapidly identify safety signals. Unfortunately, public resources\nfor developing natural language models for this task are scant. We present\nPHEE, a novel dataset for pharmacovigilance comprising over 5000 annotated\nevents from medical case reports and biomedical literature, making it the\nlargest such public dataset to date. We describe the hierarchical event schema\ndesigned to provide coarse and fine-grained information about patients'\ndemographics, treatments and (side) effects. Along with the discussion of the\ndataset, we present a thorough experimental evaluation of current\nstate-of-the-art approaches for biomedical event extraction, point out their\nlimitations, and highlight open challenges to foster future research in this\narea.\n", "date": "2022-10-22", "categories": []}
{"id": "2210.11892", "title": "BioLORD: Learning Ontological Representations from Definitions (for\n  Biomedical Concepts and their Textual Descriptions)", "abstract": "  This work introduces BioLORD, a new pre-training strategy for producing\nmeaningful representations for clinical sentences and biomedical concepts.\nState-of-the-art methodologies operate by maximizing the similarity in\nrepresentation of names referring to the same concept, and preventing collapse\nthrough contrastive learning. However, because biomedical names are not always\nself-explanatory, it sometimes results in non-semantic representations. BioLORD\novercomes this issue by grounding its concept representations using\ndefinitions, as well as short descriptions derived from a multi-relational\nknowledge graph consisting of biomedical ontologies. Thanks to this grounding,\nour model produces more semantic concept representations that match more\nclosely the hierarchical structure of ontologies. BioLORD establishes a new\nstate of the art for text similarity on both clinical sentences (MedSTS) and\nbiomedical concepts (MayoSRS).\n", "date": "2022-10-21", "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": "2210.03304", "title": "Knowledge Injected Prompt Based Fine-tuning for Multi-label Few-shot ICD\n  Coding", "abstract": "  Automatic International Classification of Diseases (ICD) coding aims to\nassign multiple ICD codes to a medical note with average length of 3,000+\ntokens. This task is challenging due to a high-dimensional space of multi-label\nassignment (tens of thousands of ICD codes) and the long-tail challenge: only a\nfew codes (common diseases) are frequently assigned while most codes (rare\ndiseases) are infrequently assigned. This study addresses the long-tail\nchallenge by adapting a prompt-based fine-tuning technique with label\nsemantics, which has been shown to be effective under few-shot setting. To\nfurther enhance the performance in medical domain, we propose a\nknowledge-enhanced longformer by injecting three domain-specific knowledge:\nhierarchy, synonym, and abbreviation with additional pretraining using\ncontrastive learning. Experiments on MIMIC-III-full, a benchmark dataset of\ncode assignment, show that our proposed method outperforms previous\nstate-of-the-art method in 14.5% in marco F1 (from 10.3 to 11.8, P<0.001). To\nfurther test our model on few-shot setting, we created a new rare diseases\ncoding dataset, MIMIC-III-rare50, on which our model improves marco F1 from\n17.1 to 30.4 and micro F1 from 17.2 to 32.6 compared to previous method.\n", "date": "2022-10-07", "categories": []}
{"id": "2210.06340", "title": "Improving Radiology Report Generation Systems by Removing Hallucinated\n  References to Non-existent Priors", "abstract": "  Current deep learning models trained to generate radiology reports from chest\nradiographs are capable of producing clinically accurate, clear, and actionable\ntext that can advance patient care. However, such systems all succumb to the\nsame problem: making hallucinated references to non-existent prior reports.\nSuch hallucinations occur because these models are trained on datasets of\nreal-world patient reports that inherently refer to priors. To this end, we\npropose two methods to remove references to priors in radiology reports: (1) a\nGPT-3-based few-shot approach to rewrite medical reports without references to\npriors; and (2) a BioBERT-based token classification approach to directly\nremove words referring to priors. We use the aforementioned approaches to\nmodify MIMIC-CXR, a publicly available dataset of chest X-rays and their\nassociated free-text radiology reports; we then retrain CXR-RePaiR, a radiology\nreport generation system, on the adapted MIMIC-CXR dataset. We find that our\nre-trained model--which we call CXR-ReDonE--outperforms previous report\ngeneration methods on clinical metrics, achieving an average BERTScore of\n0.2351 (2.57% absolute improvement). We expect our approach to be broadly\nvaluable in enabling current radiology report generation systems to be more\ndirectly integrated into clinical pipelines.\n", "date": "2022-09-27", "categories": []}
{"id": "2210.06566", "title": "Developing a general-purpose clinical language inference model from a\n  large corpus of clinical notes", "abstract": "  Several biomedical language models have already been developed for clinical\nlanguage inference. However, these models typically utilize general\nvocabularies and are trained on relatively small clinical corpora. We sought to\nevaluate the impact of using a domain-specific vocabulary and a large clinical\ntraining corpus on the performance of these language models in clinical\nlanguage inference. We trained a Bidirectional Encoder Decoder from\nTransformers (BERT) model using a diverse, deidentified corpus of 75 million\ndeidentified clinical notes authored at the University of California, San\nFrancisco (UCSF). We evaluated this model on several clinical language\ninference benchmark tasks: clinical and temporal concept recognition, relation\nextraction and medical language inference. We also evaluated our model on two\ntasks using discharge summaries from UCSF: diagnostic code assignment and\ntherapeutic class inference. Our model performs at par with the best publicly\navailable biomedical language models of comparable sizes on the public\nbenchmark tasks, and is significantly better than these models in a\nwithin-system evaluation on the two tasks using UCSF data. The use of in-domain\nvocabulary appears to improve the encoding of longer documents. The use of\nlarge clinical corpora appears to enhance document encoding and inferential\naccuracy. However, further research is needed to improve abbreviation\nresolution, and numerical, temporal, and implicitly causal inference.\n", "date": "2022-10-12", "categories": []}
{"id": "2210.03970", "title": "KG-MTT-BERT: Knowledge Graph Enhanced BERT for Multi-Type Medical Text\n  Classification", "abstract": "  Medical text learning has recently emerged as a promising area to improve\nhealthcare due to the wide adoption of electronic health record (EHR) systems.\nThe complexity of the medical text such as diverse length, mixed text types,\nand full of medical jargon, poses a great challenge for developing effective\ndeep learning models. BERT has presented state-of-the-art results in many NLP\ntasks, such as text classification and question answering. However, the\nstandalone BERT model cannot deal with the complexity of the medical text,\nespecially the lengthy clinical notes. Herein, we develop a new model called\nKG-MTT-BERT (Knowledge Graph Enhanced Multi-Type Text BERT) by extending the\nBERT model for long and multi-type text with the integration of the medical\nknowledge graph. Our model can outperform all baselines and other\nstate-of-the-art models in diagnosis-related group (DRG) classification, which\nrequires comprehensive medical text for accurate classification. We also\ndemonstrated that our model can effectively handle multi-type text and the\nintegration of medical knowledge graph can significantly improve the\nperformance.\n", "date": "2022-10-08", "categories": []}
{"id": "2210.02487", "title": "Token Classification for Disambiguating Medical Abbreviations", "abstract": "  Abbreviations are unavoidable yet critical parts of the medical text. Using\nabbreviations, especially in clinical patient notes, can save time and space,\nprotect sensitive information, and help avoid repetitions. However, most\nabbreviations might have multiple senses, and the lack of a standardized\nmapping system makes disambiguating abbreviations a difficult and\ntime-consuming task. The main objective of this study is to examine the\nfeasibility of token classification methods for medical abbreviation\ndisambiguation. Specifically, we explore the capability of token classification\nmethods to deal with multiple unique abbreviations in a single text. We use two\npublic datasets to compare and contrast the performance of several transformer\nmodels pre-trained on different scientific and medical corpora. Our proposed\ntoken classification approach outperforms the more commonly used text\nclassification models for the abbreviation disambiguation task. In particular,\nthe SciBERT model shows a strong performance for both token and text\nclassification tasks over the two considered datasets. Furthermore, we find\nthat abbreviation disambiguation performance for the text classification models\nbecomes comparable to that of token classification only when postprocessing is\napplied to their predictions, which involves filtering possible labels for an\nabbreviation based on the training data.\n", "date": "2022-10-05", "categories": []}
{"id": "2209.02765", "title": "Depression Symptoms Modelling from Social Media Text: A Semi-supervised\n  Learning Approach", "abstract": "  A fundamental component of user-level social media language based clinical\ndepression modelling is depression symptoms detection (DSD). Unfortunately,\nthere does not exist any DSD dataset that reflects both the clinical insights\nand the distribution of depression symptoms from the samples of self-disclosed\ndepressed population. In our work, we describe a Semi-supervised Learning (SSL)\nframework which uses an initial supervised learning model that leverages 1) a\nstate-of-the-art large mental health forum text pre-trained language model\nfurther fine-tuned on a clinician annotated DSD dataset, 2) a Zero-Shot\nlearning model for DSD, and couples them together to harvest depression\nsymptoms related samples from our large self-curated Depression Tweets\nRepository (DTR). Our clinician annotated dataset is the largest of its kind.\nFurthermore, DTR is created from the samples of tweets in self-disclosed\ndepressed users Twitter timeline from two datasets, including one of the\nlargest benchmark datasets for user-level depression detection from Twitter.\nThis further helps preserve the depression symptoms distribution of\nself-disclosed Twitter users tweets. Subsequently, we iteratively retrain our\ninitial DSD model with the harvested data. We discuss the stopping criteria and\nlimitations of this SSL process, and elaborate the underlying constructs which\nplay a vital role in the overall SSL process. We show that we can produce a\nfinal dataset which is the largest of its kind. Furthermore, a DSD and a\nDepression Post Detection (DPD) model trained on it achieves significantly\nbetter accuracy than their initial version.\n", "date": "2022-09-06", "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": "2209.12177", "title": "Application of Deep Learning in Generating Structured Radiology Reports:\n  A Transformer-Based Technique", "abstract": "  Since radiology reports needed for clinical practice and research are written\nand stored in free-text narrations, extraction of relative information for\nfurther analysis is difficult. In these circumstances, natural language\nprocessing (NLP) techniques can facilitate automatic information extraction and\ntransformation of free-text formats to structured data. In recent years, deep\nlearning (DL)-based models have been adapted for NLP experiments with promising\nresults. Despite the significant potential of DL models based on artificial\nneural networks (ANN) and convolutional neural networks (CNN), the models face\nsome limitations to implement in clinical practice. Transformers, another new\nDL architecture, have been increasingly applied to improve the process.\nTherefore, in this study, we propose a transformer-based fine-grained named\nentity recognition (NER) architecture for clinical information extraction. We\ncollected 88 abdominopelvic sonography reports in free-text formats and\nannotated them based on our developed information schema. The text-to-text\ntransfer transformer model (T5) and Scifive, a pre-trained domain-specific\nadaptation of the T5 model, were applied for fine-tuning to extract entities\nand relations and transform the input into a structured format. Our\ntransformer-based model in this study outperformed previously applied\napproaches such as ANN and CNN models based on ROUGE-1, ROUGE-2, ROUGE-L, and\nBLEU scores of 0.816, 0.668, 0.528, and 0.743, respectively, while providing an\ninterpretable structured report.\n", "date": "2022-09-25", "categories": []}
{"id": "2209.12652", "title": "AI-powered Language Assessment Tools for Dementia", "abstract": "  The main objective of this paper is to propose an approach for developing an\nArtificial Intelligence (AI)-powered Language Assessment (LA) tool. Such tools\ncan be used to assess language impairments associated with dementia in older\nadults. The Machine Learning (ML) classifiers are the main parts of our\nproposed approach, therefore to develop an accurate tool with high sensitivity\nand specificity, we consider different binary classifiers and evaluate their\nperformances. We also assess the reliability and validity of our approach by\ncomparing the impact of different types of language tasks, features, and\nrecording media on the performance of ML classifiers.\n", "date": "2022-09-13", "categories": []}
{"id": "2209.12831", "title": "Adaptation of Autoencoder for Sparsity Reduction From Clinical Notes\n  Representation Learning", "abstract": "  When dealing with clinical text classification on a small dataset recent\nstudies have confirmed that a well-tuned multilayer perceptron outperforms\nother generative classifiers, including deep learning ones. To increase the\nperformance of the neural network classifier, feature selection for the\nlearning representation can effectively be used. However, most feature\nselection methods only estimate the degree of linear dependency between\nvariables and select the best features based on univariate statistical tests.\nFurthermore, the sparsity of the feature space involved in the learning\nrepresentation is ignored. Goal: Our aim is therefore to access an alternative\napproach to tackle the sparsity by compressing the clinical representation\nfeature space, where limited French clinical notes can also be dealt with\neffectively. Methods: This study proposed an autoencoder learning algorithm to\ntake advantage of sparsity reduction in clinical note representation. The\nmotivation was to determine how to compress sparse, high-dimensional data by\nreducing the dimension of the clinical note representation feature space. The\nclassification performance of the classifiers was then evaluated in the trained\nand compressed feature space. Results: The proposed approach provided overall\nperformance gains of up to 3% for each evaluation. Finally, the classifier\nachieved a 92% accuracy, 91% recall, 91% precision, and 91% f1-score in\ndetecting the patient's condition. Furthermore, the compression working\nmechanism and the autoencoder prediction process were demonstrated by applying\nthe theoretic information bottleneck framework.\n", "date": "2022-09-26", "categories": []}
{"id": "2209.10477", "title": "Transition to Adulthood for Young People with Intellectual or\n  Developmental Disabilities: Emotion Detection and Topic Modeling", "abstract": "  Transition to Adulthood is an essential life stage for many families. The\nprior research has shown that young people with intellectual or development\ndisabil-ities (IDD) have more challenges than their peers. This study is to\nexplore how to use natural language processing (NLP) methods, especially\nunsupervised machine learning, to assist psychologists to analyze emotions and\nsentiments and to use topic modeling to identify common issues and challenges\nthat young people with IDD and their families have. Additionally, the results\nwere compared to those obtained from young people without IDD who were in\ntran-sition to adulthood. The findings showed that NLP methods can be very\nuseful for psychologists to analyze emotions, conduct cross-case analysis, and\nsum-marize key topics from conversational data. Our Python code is available at\nhttps://github.com/mlaricheva/emotion_topic_modeling.\n", "date": "2022-09-21", "categories": []}
{"id": "2209.09814", "title": "PainPoints: A Framework for Language-based Detection of Chronic Pain and\n  Expert-Collaborative Text-Summarization", "abstract": "  Chronic pain is a pervasive disorder which is often very disabling and is\nassociated with comorbidities such as depression and anxiety. Neuropathic Pain\n(NP) is a common sub-type which is often caused due to nerve damage and has a\nknown pathophysiology. Another common sub-type is Fibromyalgia (FM) which is\ndescribed as musculoskeletal, diffuse pain that is widespread through the body.\nThe pathophysiology of FM is poorly understood, making it very hard to\ndiagnose. Standard medications and treatments for FM and NP differ from one\nanother and if misdiagnosed it can cause an increase in symptom severity. To\novercome this difficulty, we propose a novel framework, PainPoints, which\naccurately detects the sub-type of pain and generates clinical notes via\nsummarizing the patient interviews. Specifically, PainPoints makes use of large\nlanguage models to perform sentence-level classification of the text obtained\nfrom interviews of FM and NP patients with a reliable AUC of 0.83. Using a\nsufficiency-based interpretability approach, we explain how the fine-tuned\nmodel accurately picks up on the nuances that patients use to describe their\npain. Finally, we generate summaries of these interviews via expert\ninterventions by introducing a novel facet-based approach. PainPoints thus\nenables practitioners to add/drop facets and generate a custom summary based on\nthe notion of \"facet-coverage\" which is also introduced in this work.\n", "date": "2022-09-14", "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": "2209.06723", "title": "Toward Improving Health Literacy in Patient Education Materials with\n  Neural Machine Translation Models", "abstract": "  Health literacy is the central focus of Healthy People 2030, the fifth\niteration of the U.S. national goals and objectives. People with low health\nliteracy usually have trouble understanding health information, following\npost-visit instructions, and using prescriptions, which results in worse health\noutcomes and serious health disparities. In this study, we propose to leverage\nnatural language processing techniques to improve health literacy in patient\neducation materials by automatically translating illiterate languages in a\ngiven sentence. We scraped patient education materials from four online health\ninformation websites: MedlinePlus.gov, Drugs.com, Mayoclinic.org and\nReddit.com. We trained and tested the state-of-the-art neural machine\ntranslation (NMT) models on a silver standard training dataset and a gold\nstandard testing dataset, respectively. The experimental results showed that\nthe Bidirectional Long Short-Term Memory (BiLSTM) NMT model outperformed\nBidirectional Encoder Representations from Transformers (BERT)-based NMT\nmodels. We also verified the effectiveness of NMT models in translating health\nilliterate languages by comparing the ratio of health illiterate language in\nthe sentence. The proposed NMT models were able to identify the correct\ncomplicated words and simplify into layman language while at the same time the\nmodels suffer from sentence completeness, fluency, readability, and have\ndifficulty in translating certain medical terms.\n", "date": "2022-09-14", "categories": []}
{"id": "2209.03528", "title": "CLaCLab at SocialDisNER: Using Medical Gazetteers for Named-Entity\n  Recognition of Disease Mentions in Spanish Tweets", "abstract": "  This paper summarizes the CLaC submission for SMM4H 2022 Task 10 which\nconcerns the recognition of diseases mentioned in Spanish tweets. Before\nclassifying each token, we encode each token with a transformer encoder using\nfeatures from Multilingual RoBERTa Large, UMLS gazetteer, and DISTEMIST\ngazetteer, among others. We obtain a strict F1 score of 0.869, with competition\nmean of 0.675, standard deviation of 0.245, and median of 0.761.\n", "date": "2022-09-08", "categories": []}
{"id": "2209.06085", "title": "Acoustic-Linguistic Features for Modeling Neurological Task Score in\n  Alzheimer's", "abstract": "  The average life expectancy is increasing globally due to advancements in\nmedical technology, preventive health care, and a growing emphasis on\ngerontological health. Therefore, developing technologies that detect and track\naging-associated disease in cognitive function among older adult populations is\nimperative. In particular, research related to automatic detection and\nevaluation of Alzheimer's disease (AD) is critical given the disease's\nprevalence and the cost of current methods. As AD impacts the acoustics of\nspeech and vocabulary, natural language processing and machine learning provide\npromising techniques for reliably detecting AD. We compare and contrast the\nperformance of ten linear regression models for predicting Mini-Mental Status\nExam scores on the ADReSS challenge dataset. We extracted 13000+ handcrafted\nand learned features that capture linguistic and acoustic phenomena. Using a\nsubset of 54 top features selected by two methods: (1) recursive elimination\nand (2) correlation scores, we outperform a state-of-the-art baseline for the\nsame task. Upon scoring and evaluating the statistical significance of each of\nthe selected subset of features for each model, we find that, for the given\ntask, handcrafted linguistic features are more significant than acoustic and\nlearned features.\n", "date": "2022-09-13", "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": "2209.05286", "title": "DECK: Behavioral Tests to Improve Interpretability and Generalizability\n  of BERT Models Detecting Depression from Text", "abstract": "  Models that accurately detect depression from text are important tools for\naddressing the post-pandemic mental health crisis. BERT-based classifiers'\npromising performance and the off-the-shelf availability make them great\ncandidates for this task. However, these models are known to suffer from\nperformance inconsistencies and poor generalization. In this paper, we\nintroduce the DECK (DEpression ChecKlist), depression-specific model\nbehavioural tests that allow better interpretability and improve\ngeneralizability of BERT classifiers in depression domain. We create 23 tests\nto evaluate BERT, RoBERTa and ALBERT depression classifiers on three datasets,\ntwo Twitter-based and one clinical interview-based. Our evaluation shows that\nthese models: 1) are robust to certain gender-sensitive variations in text; 2)\nrely on the important depressive language marker of the increased use of first\nperson pronouns; 3) fail to detect some other depression symptoms like suicidal\nideation. We also demonstrate that DECK tests can be used to incorporate\nsymptom-specific information in the training data and consistently improve\ngeneralizability of all three BERT models, with an out-of-distribution F1-score\nincrease of up to 53.93%.\n", "date": "2022-09-12", "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": "2209.02812", "title": "Increasing Adverse Drug Events extraction robustness on social media:\n  case study on negation and speculation", "abstract": "  In the last decade, an increasing number of users have started reporting\nAdverse Drug Events (ADE) on social media platforms, blogs, and health forums.\nGiven the large volume of reports, pharmacovigilance has focused on ways to use\nNatural Language Processing (NLP) techniques to rapidly examine these large\ncollections of text, detecting mentions of drug-related adverse reactions to\ntrigger medical investigations. However, despite the growing interest in the\ntask and the advances in NLP, the robustness of these models in face of\nlinguistic phenomena such as negations and speculations is an open research\nquestion. Negations and speculations are pervasive phenomena in natural\nlanguage, and can severely hamper the ability of an automated system to\ndiscriminate between factual and nonfactual statements in text. In this paper\nwe take into consideration four state-of-the-art systems for ADE detection on\nsocial media texts. We introduce SNAX, a benchmark to test their performance\nagainst samples containing negated and speculated ADEs, showing their fragility\nagainst these phenomena. We then introduce two possible strategies to increase\nthe robustness of these models, showing that both of them bring significant\nincreases in performance, lowering the number of spurious entities predicted by\nthe models by 60% for negation and 80% for speculations.\n", "date": "2022-09-06", "categories": []}
{"id": "2209.02022", "title": "How Much User Context Do We Need? Privacy by Design in Mental Health NLP\n  Application", "abstract": "  Clinical NLP tasks such as mental health assessment from text, must take\nsocial constraints into account - the performance maximization must be\nconstrained by the utmost importance of guaranteeing privacy of user data.\nConsumer protection regulations, such as GDPR, generally handle privacy by\nrestricting data availability, such as requiring to limit user data to 'what is\nnecessary' for a given purpose. In this work, we reason that providing stricter\nformal privacy guarantees, while increasing the volume of user data in the\nmodel, in most cases increases benefit for all parties involved, especially for\nthe user. We demonstrate our arguments on two existing suicide risk assessment\ndatasets of Twitter and Reddit posts. We present the first analysis juxtaposing\nuser history length and differential privacy budgets and elaborate how modeling\nadditional user context enables utility preservation while maintaining\nacceptable user privacy guarantees.\n", "date": "2022-09-05", "categories": []}
{"id": "2209.00262", "title": "Which anonymization technique is best for which NLP task? -- It depends.\n  A Systematic Study on Clinical Text Processing", "abstract": "  Clinical text processing has gained more and more attention in recent years.\nThe access to sensitive patient data, on the other hand, is still a big\nchallenge, as text cannot be shared without legal hurdles and without removing\npersonal information. There are many techniques to modify or remove patient\nrelated information, each with different strengths. This paper investigates the\ninfluence of different anonymization techniques on the performance of ML models\nusing multiple datasets corresponding to five different NLP tasks. Several\nlearnings and recommendations are presented. This work confirms that\nparticularly stronger anonymization techniques lead to a significant drop of\nperformance. In addition to that, most of the presented techniques are not\nsecure against a re-identification attack based on similarity search.\n", "date": "2022-09-01", "categories": []}
{"id": "2209.00470", "title": "Negation detection in Dutch clinical texts: an evaluation of rule-based\n  and machine learning methods", "abstract": "  As structured data are often insufficient, labels need to be extracted from\nfree text in electronic health records when developing models for clinical\ninformation retrieval and decision support systems. One of the most important\ncontextual properties in clinical text is negation, which indicates the absence\nof findings. We aimed to improve large scale extraction of labels by comparing\nthree methods for negation detection in Dutch clinical notes. We used the\nErasmus Medical Center Dutch Clinical Corpus to compare a rule-based method\nbased on ContextD, a biLSTM model using MedCAT and (finetuned) RoBERTa-based\nmodels. We found that both the biLSTM and RoBERTa models consistently\noutperform the rule-based model in terms of F1 score, precision and recall. In\naddition, we systematically categorized the classification errors for each\nmodel, which can be used to further improve model performance in particular\napplications. Combining the three models naively was not beneficial in terms of\nperformance. We conclude that the biLSTM and RoBERTa-based models in particular\nare highly accurate accurate in detecting clinical negations, but that\nultimately all three approaches can be viable depending on the use case at\nhand.\n", "date": "2022-09-01", "categories": []}
{"id": "2208.14493", "title": "Annotated Dataset Creation through General Purpose Language Models for\n  non-English Medical NLP", "abstract": "  Obtaining text datasets with semantic annotations is an effortful process,\nyet crucial for supervised training in natural language processsing (NLP). In\ngeneral, developing and applying new NLP pipelines in domain-specific contexts\nfor tasks often requires custom designed datasets to address NLP tasks in\nsupervised machine learning fashion. When operating in non-English languages\nfor medical data processing, this exposes several minor and major,\ninterconnected problems such as lack of task-matching datasets as well as\ntask-specific pre-trained models. In our work we suggest to leverage pretrained\nlanguage models for training data acquisition in order to retrieve sufficiently\nlarge datasets for training smaller and more efficient models for use-case\nspecific tasks. To demonstrate the effectiveness of your approach, we create a\ncustom dataset which we use to train a medical NER model for German texts,\nGPTNERMED, yet our method remains language-independent in principle. Our\nobtained dataset as well as our pre-trained models are publicly available at:\nhttps://github.com/frankkramer-lab/GPTNERMED\n", "date": "2022-08-30", "categories": []}
{"id": "2208.13949", "title": "NEAR: Named Entity and Attribute Recognition of clinical concepts", "abstract": "  Named Entity Recognition (NER) or the extraction of concepts from clinical\ntext is the task of identifying entities in text and slotting them into\ncategories such as problems, treatments, tests, clinical departments,\noccurrences (such as admission and discharge) and others. NER forms a critical\ncomponent of processing and leveraging unstructured data from Electronic Health\nRecords (EHR). While identifying the spans and categories of concepts is itself\na challenging task, these entities could also have attributes such as negation\nthat pivot their meanings implied to the consumers of the named entities. There\nhas been little research dedicated to identifying the entities and their\nqualifying attributes together. This research hopes to contribute to the area\nof detecting entities and their corresponding attributes by modelling the NER\ntask as a supervised, multi-label tagging problem with each of the attributes\nassigned tagging sequence labels. In this paper, we propose 3 architectures to\nachieve this multi-label entity tagging: BiLSTM n-CRF, BiLSTM-CRF-Smax-TF and\nBiLSTM n-CRF-TF. We evaluate these methods on the 2010 i2b2/VA and the i2b2\n2012 shared task datasets. Our different models obtain best NER F1 scores of 0.\n894 and 0.808 on the i2b2 2010/VA and i2b2 2012 respectively. The highest span\nbased micro-averaged F1 polarity scores obtained were 0.832 and 0.836 on the\ni2b2 2010/VA and i2b2 2012 datasets respectively, and the highest\nmacro-averaged F1 polarity scores obtained were 0.924 and 0.888 respectively.\nThe modality studies conducted on i2b2 2012 dataset revealed high scores of\n0.818 and 0.501 for span based micro-averaged F1 and macro-averaged F1\nrespectively.\n", "date": "2022-08-30", "categories": []}
{"id": "2208.13472", "title": "Supporting Medical Relation Extraction via Causality-Pruned Semantic\n  Dependency Forest", "abstract": "  Medical Relation Extraction (MRE) task aims to extract relations between\nentities in medical texts. Traditional relation extraction methods achieve\nimpressive success by exploring the syntactic information, e.g., dependency\ntree. However, the quality of the 1-best dependency tree for medical texts\nproduced by an out-of-domain parser is relatively limited so that the\nperformance of medical relation extraction method may degenerate. To this end,\nwe propose a method to jointly model semantic and syntactic information from\nmedical texts based on causal explanation theory. We generate dependency\nforests consisting of the semantic-embedded 1-best dependency tree. Then, a\ntask-specific causal explainer is adopted to prune the dependency forests,\nwhich are further fed into a designed graph convolutional network to learn the\ncorresponding representation for downstream task. Empirically, the various\ncomparisons on benchmark medical datasets demonstrate the effectiveness of our\nmodel.\n", "date": "2022-08-29", "categories": []}
{"id": "2208.11701", "title": "Ontology-Driven Self-Supervision for Adverse Childhood Experiences\n  Identification Using Social Media Datasets", "abstract": "  Adverse Childhood Experiences (ACEs) are defined as a collection of highly\nstressful, and potentially traumatic, events or circumstances that occur\nthroughout childhood and/or adolescence. They have been shown to be associated\nwith increased risks of mental health diseases or other abnormal behaviours in\nlater lives. However, the identification of ACEs from textual data with Natural\nLanguage Processing (NLP) is challenging because (a) there are no NLP ready ACE\nontologies; (b) there are few resources available for machine learning,\nnecessitating the data annotation from clinical experts; (c) costly annotations\nby domain experts and large number of documents for supporting large machine\nlearning models. In this paper, we present an ontology-driven self-supervised\napproach (derive concept embeddings using an auto-encoder from baseline NLP\nresults) for producing a publicly available resource that would support\nlarge-scale machine learning (e.g., training transformer based large language\nmodels) on social media corpus. This resource as well as the proposed approach\nare aimed to facilitate the community in training transferable NLP models for\neffectively surfacing ACEs in low-resource scenarios like NLP on clinical notes\nwithin Electronic Health Records. The resource including a list of ACE ontology\nterms, ACE concept embeddings and the NLP annotated corpus is available at\nhttps://github.com/knowlab/ACE-NLP.\n", "date": "2022-08-24", "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": "2208.11466", "title": "Adverse Childhood Experiences Identification from Clinical Notes with\n  Ontologies and NLP", "abstract": "  Adverse Childhood Experiences (ACEs) are defined as a collection of highly\nstressful, and potentially traumatic, events or circumstances that occur\nthroughout childhood and/or adolescence. They have been shown to be associated\nwith increased risks of mental health diseases or other abnormal behaviours in\nlater lives. However, the identification of ACEs from free-text Electronic\nHealth Records (EHRs) with Natural Language Processing (NLP) is challenging\nbecause (a) there is no NLP ready ACE ontologies; (b) there are limited cases\navailable for machine learning, necessitating the data annotation from clinical\nexperts. We are currently developing a tool that would use NLP techniques to\nassist us in surfacing ACEs from clinical notes. This will enable us further\nresearch in identifying evidence of the relationship between ACEs and the\nsubsequent developments of mental illness (e.g., addictions) in large-scale and\nlongitudinal free-text EHRs, which has previously not been possible.\n", "date": "2022-08-24", "categories": []}
{"id": "2208.10250", "title": "Multi-Task Learning for Depression Detection in Dialogs", "abstract": "  Depression is a serious mental illness that impacts the way people\ncommunicate, especially through their emotions, and, allegedly, the way they\ninteract with others. This work examines depression signals in dialogs, a less\nstudied setting that suffers from data sparsity. We hypothesize that depression\nand emotion can inform each other, and we propose to explore the influence of\ndialog structure through topic and dialog act prediction. We investigate a\nMulti-Task Learning (MTL) approach, where all tasks mentioned above are learned\njointly with dialog-tailored hierarchical modeling. We experiment on the DAIC\nand DailyDialog corpora-both contain dialogs in English-and show important\nimprovements over state-ofthe-art on depression detection (at best 70.6% F 1),\nwhich demonstrates the correlation of depression with emotion and dialog\norganization and the power of MTL to leverage information from different\nsources.\n", "date": "2022-07-21", "categories": []}
{"id": "2208.09354", "title": "End-to-end Clinical Event Extraction from Chinese Electronic Health\n  Record", "abstract": "  Event extraction is an important work of medical text processing. According\nto the complex characteristics of medical text annotation, we use the\nend-to-end event extraction model to enhance the output formatting information\nof events. Through pre training and fine-tuning, we can extract the attributes\nof the four dimensions of medical text: anatomical position, subject word,\ndescription word and occurrence state. On the test set, the accuracy rate was\n0.4511, the recall rate was 0.3928, and the F1 value was 0.42. The method of\nthis model is simple, and it has won the second place in the task of mining\nclinical discovery events (task2) in the Chinese electronic medical record of\nthe seventh China health information processing Conference (chip2021).\n", "date": "2022-08-19", "categories": []}
{"id": "2208.09437", "title": "Graph-Augmented Cyclic Learning Framework for Similarity Estimation of\n  Medical Clinical Notes", "abstract": "  Semantic textual similarity (STS) in the clinical domain helps improve\ndiagnostic efficiency and produce concise texts for downstream data mining\ntasks. However, given the high degree of domain knowledge involved in clinic\ntext, it remains challenging for general language models to infer implicit\nmedical relationships behind clinical sentences and output similarities\ncorrectly. In this paper, we present a graph-augmented cyclic learning\nframework for similarity estimation in the clinical domain. The framework can\nbe conveniently implemented on a state-of-art backbone language model, and\nimprove its performance by leveraging domain knowledge through co-training with\nan auxiliary graph convolution network (GCN) based network. We report the\nsuccess of introducing domain knowledge in GCN and the co-training framework by\nimproving the Bio-clinical BERT baseline by 16.3% and 27.9%, respectively.\n", "date": "2022-08-19", "categories": []}
{"id": "2208.07444", "title": "Entity Anchored ICD Coding", "abstract": "  Medical coding is a complex task, requiring assignment of a subset of over\n72,000 ICD codes to a patient's notes. Modern natural language processing\napproaches to these tasks have been challenged by the length of the input and\nsize of the output space. We limit our model inputs to a small window around\nmedical entities found in our documents. From those local contexts, we build\ncontextualized representations of both ICD codes and entities, and aggregate\nover these representations to form document-level predictions. In contrast to\nexisting methods which use a representation fixed either in size or by codes\nseen in training, we represent ICD codes by encoding the code description with\nlocal context. We discuss metrics appropriate to deploying coding systems in\npractice. We show that our approach is superior to existing methods in both\nstandard and deployable measures, including performance on rare and unseen\ncodes.\n", "date": "2022-08-15", "categories": []}
{"id": "2207.03885", "title": "A Medical Information Extraction Workbench to Process German Clinical\n  Text", "abstract": "  Background: In the information extraction and natural language processing\ndomain, accessible datasets are crucial to reproduce and compare results.\nPublicly available implementations and tools can serve as benchmark and\nfacilitate the development of more complex applications. However, in the\ncontext of clinical text processing the number of accessible datasets is scarce\n-- and so is the number of existing tools. One of the main reasons is the\nsensitivity of the data. This problem is even more evident for non-English\nlanguages.\n  Approach: In order to address this situation, we introduce a workbench: a\ncollection of German clinical text processing models. The models are trained on\na de-identified corpus of German nephrology reports.\n  Result: The presented models provide promising results on in-domain data.\nMoreover, we show that our models can be also successfully applied to other\nbiomedical text in German. Our workbench is made publicly available so it can\nbe used out of the box, as a benchmark or transferred to related problems.\n", "date": "2022-07-08", "categories": []}
{"id": "2208.01341", "title": "Gender bias in (non)-contextual clinical word embeddings for\n  stereotypical medical categories", "abstract": "  Clinical word embeddings are extensively used in various Bio-NLP problems as\na state-of-the-art feature vector representation. Although they are quite\nsuccessful at the semantic representation of words, due to the dataset - which\npotentially carries statistical and societal bias - on which they are trained,\nthey might exhibit gender stereotypes. This study analyses gender bias of\nclinical embeddings on three medical categories: mental disorders, sexually\ntransmitted diseases, and personality traits. To this extent, we analyze two\ndifferent pre-trained embeddings namely (contextualized) clinical-BERT and\n(non-contextualized) BioWordVec. We show that both embeddings are biased\ntowards sensitive gender groups but BioWordVec exhibits a higher bias than\nclinical-BERT for all three categories. Moreover, our analyses show that\nclinical embeddings carry a high degree of bias for some medical terms and\ndiseases which is conflicting with medical literature. Having such an\nill-founded relationship might cause harm in downstream applications that use\nclinical embeddings.\n", "date": "2022-08-02", "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": "2208.01912", "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": "2208.02031", "title": "Cross-lingual Approaches for the Detection of Adverse Drug Reactions in\n  German from a Patient's Perspective", "abstract": "  In this work, we present the first corpus for German Adverse Drug Reaction\n(ADR) detection in patient-generated content. The data consists of 4,169 binary\nannotated documents from a German patient forum, where users talk about health\nissues and get advice from medical doctors. As is common in social media data\nin this domain, the class labels of the corpus are very imbalanced. This and a\nhigh topic imbalance make it a very challenging dataset, since often, the same\nsymptom can have several causes and is not always related to a medication\nintake. We aim to encourage further multi-lingual efforts in the domain of ADR\ndetection and provide preliminary experiments for binary classification using\ndifferent methods of zero- and few-shot learning based on a multi-lingual\nmodel. When fine-tuning XLM-RoBERTa first on English patient forum data and\nthen on the new German data, we achieve an F1-score of 37.52 for the positive\nclass. We make the dataset and models publicly available for the community.\n", "date": "2022-08-03", "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": "2207.13757", "title": "The Leaf Clinical Trials Corpus: a new resource for query generation\n  from clinical trial eligibility criteria", "abstract": "  Identifying cohorts of patients based on eligibility criteria such as medical\nconditions, procedures, and medication use is critical to recruitment for\nclinical trials. Such criteria are often most naturally described in free-text,\nusing language familiar to clinicians and researchers. In order to identify\npotential participants at scale, these criteria must first be translated into\nqueries on clinical databases, which can be labor-intensive and error-prone.\nNatural language processing (NLP) methods offer a potential means of such\nconversion into database queries automatically. However they must first be\ntrained and evaluated using corpora which capture clinical trials criteria in\nsufficient detail. In this paper, we introduce the Leaf Clinical Trials (LCT)\ncorpus, a human-annotated corpus of over 1,000 clinical trial eligibility\ncriteria descriptions using highly granular structured labels capturing a range\nof biomedical phenomena. We provide details of our schema, annotation process,\ncorpus quality, and statistics. Additionally, we present baseline information\nextraction results on this corpus as benchmarks for future work.\n", "date": "2022-07-27", "categories": []}
{"id": "2207.12940", "title": "Learning structures of the French clinical language:development and\n  validation of word embedding models using 21 million clinical reports from\n  electronic health records", "abstract": "  Background\n  Clinical studies using real-world data may benefit from exploiting clinical\nreports, a particularly rich albeit unstructured medium. To that end, natural\nlanguage processing can extract relevant information. Methods based on transfer\nlearning using pre-trained language models have achieved state-of-the-art\nresults in most NLP applications; however, publicly available models lack\nexposure to speciality-languages, especially in the medical field.\n  Objective\n  We aimed to evaluate the impact of adapting a language model to French\nclinical reports on downstream medical NLP tasks.\n  Methods\n  We leveraged a corpus of 21M clinical reports collected from August 2017 to\nJuly 2021 at the Greater Paris University Hospitals (APHP) to produce two\nCamemBERT architectures on speciality language: one retrained from scratch and\nthe other using CamemBERT as its initialisation. We used two French annotated\nmedical datasets to compare our language models to the original CamemBERT\nnetwork, evaluating the statistical significance of improvement with the\nWilcoxon test.\n  Results\n  Our models pretrained on clinical reports increased the average F1-score on\nAPMed (an APHP-specific task) by 3 percentage points to 91%, a statistically\nsignificant improvement. They also achieved performance comparable to the\noriginal CamemBERT on QUAERO. These results hold true for the fine-tuned and\nfrom-scratch versions alike, starting from very few pre-training samples.\n  Conclusions\n  We confirm previous literature showing that adapting generalist pre-train\nlanguage models such as CamenBERT on speciality corpora improves their\nperformance for downstream clinical NLP tasks. Our results suggest that\nretraining from scratch does not induce a statistically significant performance\ngain compared to fine-tuning.\n", "date": "2022-07-26", "categories": []}
{"id": "2207.12376", "title": "Fine-Tuning BERT for Automatic ADME Semantic Labeling in FDA Drug\n  Labeling to Enhance Product-Specific Guidance Assessment", "abstract": "  Product-specific guidances (PSGs) recommended by the United States Food and\nDrug Administration (FDA) are instrumental to promote and guide generic drug\nproduct development. To assess a PSG, the FDA assessor needs to take extensive\ntime and effort to manually retrieve supportive drug information of absorption,\ndistribution, metabolism, and excretion (ADME) from the reference listed drug\nlabeling. In this work, we leveraged the state-of-the-art pre-trained language\nmodels to automatically label the ADME paragraphs in the pharmacokinetics\nsection from the FDA-approved drug labeling to facilitate PSG assessment. We\napplied a transfer learning approach by fine-tuning the pre-trained\nBidirectional Encoder Representations from Transformers (BERT) model to develop\na novel application of ADME semantic labeling, which can automatically retrieve\nADME paragraphs from drug labeling instead of manual work. We demonstrated that\nfine-tuning the pre-trained BERT model can outperform the conventional machine\nlearning techniques, achieving up to 11.6% absolute F1 improvement. To our\nknowledge, we were the first to successfully apply BERT to solve the ADME\nsemantic labeling task. We further assessed the relative contribution of\npre-training and fine-tuning to the overall performance of the BERT model in\nthe ADME semantic labeling task using a series of analysis methods such as\nattention similarity and layer-based ablations. Our analysis revealed that the\ninformation learned via fine-tuning is focused on task-specific knowledge in\nthe top layers of the BERT, whereas the benefit from the pre-trained BERT model\nis from the bottom layers.\n", "date": "2022-07-25", "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": "2207.06254", "title": "Towards Knowledge-based Mining of Mental Disorder Patterns from Textual\n  Data", "abstract": "  Mental health disorders may cause severe consequences on all the countries'\neconomies and health. For example, the impacts of the COVID-19 pandemic, such\nas isolation and travel ban, can make us feel depressed. Identifying early\nsigns of mental health disorders is vital. For example, depression may increase\nan individual's risk of suicide. The state-of-the-art research in identifying\nmental disorder patterns from textual data, uses hand-labelled training sets,\nespecially when a domain expert's knowledge is required to analyse various\nsymptoms. This task could be time-consuming and expensive. To address this\nchallenge, in this paper, we study and analyse the various clinical and\nnon-clinical approaches to identifying mental health disorders. We leverage the\ndomain knowledge and expertise in cognitive science to build a domain-specific\nKnowledge Base (KB) for the mental health disorder concepts and patterns. We\npresent a weaker form of supervision by facilitating the generating of training\ndata from a domain-specific Knowledge Base (KB). We adopt a typical scenario\nfor analysing social media to identify major depressive disorder symptoms from\nthe textual content generated by social users. We use this scenario to evaluate\nhow our knowledge-based approach significantly improves the quality of results.\n", "date": "2022-07-07", "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": "2206.15076", "title": "BigBIO: A Framework for Data-Centric Biomedical Natural Language\n  Processing", "abstract": "  Training and evaluating language models increasingly requires the\nconstruction of meta-datasets --diverse collections of curated data with clear\nprovenance. Natural language prompting has recently lead to improved zero-shot\ngeneralization by transforming existing, supervised datasets into a diversity\nof novel pretraining tasks, highlighting the benefits of meta-dataset curation.\nWhile successful in general-domain text, translating these data-centric\napproaches to biomedical language modeling remains challenging, as labeled\nbiomedical datasets are significantly underrepresented in popular data hubs. To\naddress this challenge, we introduce BigBIO a community library of 126+\nbiomedical NLP datasets, currently covering 12 task categories and 10+\nlanguages. BigBIO facilitates reproducible meta-dataset curation via\nprogrammatic access to datasets and their metadata, and is compatible with\ncurrent platforms for prompt engineering and end-to-end few/zero shot language\nmodel evaluation. We discuss our process for task schema harmonization, data\nauditing, contribution guidelines, and outline two illustrative use cases:\nzero-shot evaluation of biomedical prompts and large-scale, multi-task\nlearning. BigBIO is an ongoing community effort and is available at\nhttps://github.com/bigscience-workshop/biomedical\n", "date": "2022-06-30", "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": "2206.14181", "title": "The NLP Sandbox: an efficient model-to-data system to enable federated\n  and unbiased evaluation of clinical NLP models", "abstract": "  Objective The evaluation of natural language processing (NLP) models for\nclinical text de-identification relies on the availability of clinical notes,\nwhich is often restricted due to privacy concerns. The NLP Sandbox is an\napproach for alleviating the lack of data and evaluation frameworks for NLP\nmodels by adopting a federated, model-to-data approach. This enables unbiased\nfederated model evaluation without the need for sharing sensitive data from\nmultiple institutions. Materials and Methods We leveraged the Synapse\ncollaborative framework, containerization software, and OpenAPI generator to\nbuild the NLP Sandbox (nlpsandbox.io). We evaluated two state-of-the-art NLP\nde-identification focused annotation models, Philter and NeuroNER, using data\nfrom three institutions. We further validated model performance using data from\nan external validation site. Results We demonstrated the usefulness of the NLP\nSandbox through de-identification clinical model evaluation. The external\ndeveloper was able to incorporate their model into the NLP Sandbox template and\nprovide user experience feedback. Discussion We demonstrated the feasibility of\nusing the NLP Sandbox to conduct a multi-site evaluation of clinical text\nde-identification models without the sharing of data. Standardized model and\ndata schemas enable smooth model transfer and implementation. To generalize the\nNLP Sandbox, work is required on the part of data owners and model developers\nto develop suitable and standardized schemas and to adapt their data or model\nto fit the schemas. Conclusions The NLP Sandbox lowers the barrier to utilizing\nclinical data for NLP model evaluation and facilitates federated, multi-site,\nunbiased evaluation of NLP models.\n", "date": "2022-06-28", "categories": []}
{"id": "2206.11502", "title": "A Review of Published Machine Learning Natural Language Processing\n  Applications for Protocolling Radiology Imaging", "abstract": "  Machine learning (ML) is a subfield of Artificial intelligence (AI), and its\napplications in radiology are growing at an ever-accelerating rate. The most\nstudied ML application is the automated interpretation of images. However,\nnatural language processing (NLP), which can be combined with ML for text\ninterpretation tasks, also has many potential applications in radiology. One\nsuch application is automation of radiology protocolling, which involves\ninterpreting a clinical radiology referral and selecting the appropriate\nimaging technique. It is an essential task which ensures that the correct\nimaging is performed. However, the time that a radiologist must dedicate to\nprotocolling could otherwise be spent reporting, communicating with referrers,\nor teaching. To date, there have been few publications in which ML models were\ndeveloped that use clinical text to automate protocol selection. This article\nreviews the existing literature in this field. A systematic assessment of the\npublished models is performed with reference to best practices suggested by\nmachine learning convention. Progress towards implementing automated\nprotocolling in a clinical setting is discussed.\n", "date": "2022-06-23", "categories": []}
{"id": "2206.08611", "title": "Medical Dialogue Response Generation with Pivotal Information Recalling", "abstract": "  Medical dialogue generation is an important yet challenging task. Most\nprevious works rely on the attention mechanism and large-scale pretrained\nlanguage models. However, these methods often fail to acquire pivotal\ninformation from the long dialogue history to yield an accurate and informative\nresponse, due to the fact that the medical entities usually scatters throughout\nmultiple utterances along with the complex relationships between them. To\nmitigate this problem, we propose a medical response generation model with\nPivotal Information Recalling (MedPIR), which is built on two components, i.e.,\nknowledge-aware dialogue graph encoder and recall-enhanced generator. The\nknowledge-aware dialogue graph encoder constructs a dialogue graph by\nexploiting the knowledge relationships between entities in the utterances, and\nencodes it with a graph attention network. Then, the recall-enhanced generator\nstrengthens the usage of these pivotal information by generating a summary of\nthe dialogue before producing the actual response. Experimental results on two\nlarge-scale medical dialogue datasets show that MedPIR outperforms the strong\nbaselines in BLEU scores and medical entities F1 measure.\n", "date": "2022-06-17", "categories": []}
{"id": "2206.06308", "title": "Knowledge Graph Construction and Its Application in Automatic Radiology\n  Report Generation from Radiologist's Dictation", "abstract": "  Conventionally, the radiologist prepares the diagnosis notes and shares them\nwith the transcriptionist. Then the transcriptionist prepares a preliminary\nformatted report referring to the notes, and finally, the radiologist reviews\nthe report, corrects the errors, and signs off. This workflow causes\nsignificant delays and errors in the report. In current research work, we focus\non applications of NLP techniques like Information Extraction (IE) and\ndomain-specific Knowledge Graph (KG) to automatically generate radiology\nreports from radiologist's dictation. This paper focuses on KG construction for\neach organ by extracting information from an existing large corpus of free-text\nradiology reports. We develop an information extraction pipeline that combines\nrule-based, pattern-based, and dictionary-based techniques with\nlexical-semantic features to extract entities and relations. Missing\ninformation in short dictation can be accessed from the KGs to generate\npathological descriptions and hence the radiology report. Generated\npathological descriptions evaluated using semantic similarity metrics, which\nshows 97% similarity with gold standard pathological descriptions. Also, our\nanalysis shows that our IE module is performing better than the OpenIE tool for\nthe radiology domain. Furthermore, we include a manual qualitative analysis\nfrom radiologists, which shows that 80-85% of the generated reports are\ncorrectly written, and the remaining are partially correct.\n", "date": "2022-06-13", "categories": []}
{"id": "2206.03886", "title": "Counseling Summarization using Mental Health Knowledge Guided Utterance\n  Filtering", "abstract": "  The psychotherapy intervention technique is a multifaceted conversation\nbetween a therapist and a patient. Unlike general clinical discussions,\npsychotherapy's core components (viz. symptoms) are hard to distinguish, thus\nbecoming a complex problem to summarize later. A structured counseling\nconversation may contain discussions about symptoms, history of mental health\nissues, or the discovery of the patient's behavior. It may also contain\ndiscussion filler words irrelevant to a clinical summary. We refer to these\nelements of structured psychotherapy as counseling components. In this paper,\nthe aim is mental health counseling summarization to build upon domain\nknowledge and to help clinicians quickly glean meaning. We create a new dataset\nafter annotating 12.9K utterances of counseling components and reference\nsummaries for each dialogue. Further, we propose ConSum, a novel\ncounseling-component guided summarization model. ConSum undergoes three\nindependent modules. First, to assess the presence of depressive symptoms, it\nfilters utterances utilizing the Patient Health Questionnaire (PHQ-9), while\nthe second and third modules aim to classify counseling components. At last, we\npropose a problem-specific Mental Health Information Capture (MHIC) evaluation\nmetric for counseling summaries. Our comparative study shows that we improve on\nperformance and generate cohesive, semantic, and coherent summaries. We\ncomprehensively analyze the generated summaries to investigate the capturing of\npsychotherapy elements. Human and clinical evaluations on the summary show that\nConSum generates quality summary. Further, mental health experts validate the\nclinical acceptability of the ConSum. Lastly, we discuss the uniqueness in\nmental health counseling summarization in the real world and show evidences of\nits deployment on an online application with the support of mpathic.ai\n", "date": "2022-06-08", "categories": []}
{"id": "2206.02428", "title": "Domain-specific Language Pre-training for Dialogue Comprehension on\n  Clinical Inquiry-Answering Conversations", "abstract": "  There is growing interest in the automated extraction of relevant information\nfrom clinical dialogues. However, it is difficult to collect and construct\nlarge annotated resources for clinical dialogue tasks. Recent developments in\nnatural language processing suggest that large-scale pre-trained language\nbackbones could be leveraged for such machine comprehension and information\nextraction tasks. Yet, due to the gap between pre-training and downstream\nclinical domains, it remains challenging to exploit the generic backbones for\ndomain-specific applications. Therefore, in this work, we propose a\ndomain-specific language pre-training, to improve performance on downstream\ntasks like dialogue comprehension. Aside from the common token-level masking\npre-training method, according to the nature of human conversations and\ninteractive flow of multi-topic inquiry-answering dialogues, we further propose\nsample generation strategies with speaker and utterance manipulation. The\nconversational pre-training guides the language backbone to reconstruct the\nutterances coherently based on the remaining context, thus bridging the gap\nbetween general and specific domains. Experiments are conducted on a clinical\nconversation dataset for symptom checking, where nurses inquire and discuss\nsymptom information with patients. We empirically show that the neural model\nwith our proposed approach brings improvement in the dialogue comprehension\ntask, and can achieve favorable results in the low resource training scenario.\n", "date": "2022-06-06", "categories": []}
{"id": "2206.02696", "title": "Learning to Ask Like a Physician", "abstract": "  Existing question answering (QA) datasets derived from electronic health\nrecords (EHR) are artificially generated and consequently fail to capture\nrealistic physician information needs. We present Discharge Summary Clinical\nQuestions (DiSCQ), a newly curated question dataset composed of 2,000+\nquestions paired with the snippets of text (triggers) that prompted each\nquestion. The questions are generated by medical experts from 100+ MIMIC-III\ndischarge summaries. We analyze this dataset to characterize the types of\ninformation sought by medical experts. We also train baseline models for\ntrigger detection and question generation (QG), paired with unsupervised answer\nretrieval over EHRs. Our baseline model is able to generate high quality\nquestions in over 62% of cases when prompted with human selected triggers. We\nrelease this dataset (and all code to reproduce baseline model results) to\nfacilitate further research into realistic clinical QA and QG:\nhttps://github.com/elehman16/discq.\n", "date": "2022-06-06", "categories": []}
{"id": "2606.21447", "title": "Precision Recall Controllable Radiology Report Generation via Hybrid Natural Language and Clinical Reward Learning", "abstract": "Automated radiology report generation (RRG) has gained increasing attention because it can reduce the heavy workload of clinical report writing. However, most existing methods mainly optimize for natural language generation (NLG) metrics that focus on language fluency, while providing little control over clinically important factors such as precision and recall. As consequence, generated reports may be fluent but not well aligned with different clinical needs. To address this challenge, we propose a reinforcement learning framework for precision recall controllable RRG, where a control parameter explicitly adjusts the trade-off between clinical precision and recall during inference. This design allows the model to flexibly generate reports according to different clinical requirements. To ensure clinical correctness, we introduce a clinical reward into the training objective, which helps improve clinical efficacy (CE) beyond standard language-based optimization. In addition, we apply a group-relative training strategy that normalizes rewards within each training group, reducing reward variance and improving training stability. Extensive experiments on the MIMIC-CXR dataset show that our method consistently outperforms state-of-the-art approaches in both NLG and CE evaluation metrics, while providing reliable control over the CE precision recall trade-off.", "date": "2026-06-19", "categories": []}
{"id": "2606.21517", "title": "MedHal-Loc: Are \"Explainable-by-Architecture\" Medical Hallucination Detectors Faithful Localizers? A Localization Benchmark", "abstract": "Detecting hallucinations in clinical text is increasingly framed as an explainability problem: systems should not merely flag an unreliable response but point to the offending span. Architectures built around knowledge-graph (KG) triple decomposition are marketed for exactly this auditability, yet their localization ability is typically assumed rather than measured. We introduce MedHal-Loc, a benchmark and metric for localization faithfulness -- whether a detector's top-ranked error unit actually overlaps the erroneous span. The controlled subset comprises 300 PubMedQA-derived statements with single, span-level errors injected across four localizable types (entity substitution, relation error, mechanism misattribution, invention), yielding gold spans by construction; a complementary natural subset documents that real hallucinations are dominated by diffuse conclusion-flips that resist span localization (a human expert accepted 1/18 candidate spans). Evaluating four fine-grained paradigms, we find that NLI-per-clause, consistency-per-sentence, and the dedicated span detector FAVA all localize well above chance, whereas an elaborate KG-triple pipeline localizes no better than chance (+3.3pp, n.s.), bottlenecked by ~59% entity-extraction coverage -- despite competitive detection F1 (0.609). Detection competence does not imply faithful localization; architectural explainability must be validated, not presumed.", "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": "2606.22079", "title": "Where Does the Signal Live? A Web Data Recipe for Medical Encoder Pretraining", "abstract": "Web data curation has been widely studied for decoder Large Language Model (LLM) pretraining. Encoders for dense-terminology domains such as medicine, by contrast, are pretrained on small, manually-curated corpora that limit scalability and writing style diversity, a bottleneck even more severe in non-English clinical settings. Whether web-scale data curation also benefits encoder Masked Language Modeling (MLM) in a dense-terminology domain remains an open question. To address this, we introduce two complementary levers. Medical-term density filtering selects documents rich in medical terms. Signal-amplifying rephrasing uses an LLM to rewrite documents into denser variants with broader entity contexts. We instantiate the recipe on French medical NLP. The medical-term density filter outperforms the widely-used educational quality filter on downstream medical tasks, and the two complement each other. Signal-amplifying rephrasing alone improves on raw web data, and mixing it with filtered web data produces the largest gain. The recipe yields FineMed, a French medical pretraining corpus, and DoctoBERT, a state-of-the-art French medical encoder family evaluated on both the public benchmark DrBenchmark and a proprietary clinical Named Entity Recognition (NER) task.", "date": "2026-06-20", "categories": []}
{"id": "2606.22692", "title": "VISTA Architect: A graph database-oriented health AI system demonstrated in multidisciplinary tumor boards", "abstract": "We introduce VISTA Architect, a database-oriented AI architecture for integrating large language models (LLMs) with longitudinal electronic health records (EHRs). At ingestion, it transforms complex clinical documentation into a persistent, provenance-linked knowledge graph, eliminating repeated reprocessing of raw records at query time. The architecture has two layers: a source-faithful MEDS Graph preserving granular EHR structure with full provenance, and a clinically abstracted Timeline Object Architecture (TOA) that uses graph-guided LLM extraction to synthesize a concise timeline of deduplicated, temporally coherent clinical events. This addresses key limitations of direct long-context prompting and retrieval-augmented generation (RAG), which often miss temporal relationships and incur high cost and latency from repeated raw-text processing. By precomputing clinical synthesis once, downstream queries access an organized patient state and traverse to source documentation only when detailed verification is needed.\n  We demonstrate the system in multidisciplinary thoracic oncology tumor boards at Stanford Medicine, where precise reconstruction of patient histories is critical. Across 1,180 patients, VISTA Architect achieved 96.4% accuracy (mean 9.75/10) on 15 tumor board-salient variables (17,700 evaluations; 95% CI 96.1-96.7%), surpassing a matched BM25 RAG baseline and recent benchmarks for LLM-based clinical extraction. An agentic interface reduced preparation for a 30-patient held-out cohort to about 2.2 minutes without sacrificing accuracy. While configured here for thoracic oncology, the modular design adapts to other specialties through customizable event definitions, episode structures, and agentic tools; validation beyond thoracic oncology remains future work.", "date": "2026-06-21", "categories": []}
{"id": "2606.23992", "title": "RASC+: Retrieval-Constrained LLM Adjudication for Clinical Value Set Authoring", "abstract": "Clinical value sets define the standardized terminology codes used in quality measurement, phenotyping, cohort construction, and clinical decision support. The recently introduced Retrieval-Augmented Set Completion (RASC) benchmark showed that direct zero-shot large language model (LLM) generation is poorly suited to this task: clinical code systems are large, version-controlled, and not reliably memorized by language models. We study a stage-wise alternative in which candidate-pool construction is optimized for recall and a constrained LLM adjudicator is optimized for candidate selection. On the full 3,744-value-set RASC test split, Qwen3-based retrieval with vocabulary-aware expansion and code-display rescue retrieval increases candidate-pool recall from the original RASC retrieval baseline of 0.553 to 0.730; on the held-out-publisher stratum, pool recall is 0.655. The higher-recall pool alone is not sufficient: applying the original SAPBert cross-encoder to this expanded pool gives full-test macro F1 of 0.287 and held-out-publisher macro F1 of 0.233. Replacing the stage-2 selector with blinded GPT-5 adjudication over the same pool increases full-test macro F1 to 0.549 and held-out-publisher macro F1 to 0.533. These results show that retrieval-constrained LLM adjudication can substantially improve value set completion while preserving the safety constraint that all returned codes must come from an auditable candidate pool.", "date": "2026-06-22", "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": "2606.24145", "title": "T2D-Bench: Evidence-Gated Evaluation of LLM Outputs for Type 2 Diabetes Using a Multi-Layer Clinical-Lifestyle Knowledge Graph", "abstract": "Large language models (LLMs) can produce clinically fluent recommendations for type 2 diabetes while failing to satisfy guideline constraints or explicitly justify lifestyle-related glycemic claims. We present T2D-Bench, a reproducible benchmark and evidence-gated evaluation framework for testing whether LLM outputs satisfy explicit, graph-checkable evidence requirements. T2D-Bench is built on a multi-layer clinical-lifestyle knowledge graph that combines a biomedical spine (UMLS, DrugBank, SIDER), computable ADA Standards of Care rules, and lifestyle knowledge connected through a mechanistic bridge to glycemic laboratory effects. Across 100 structured vignettes spanning diagnosis, medication safety, and adversarial lifestyle conflicts, baseline outputs failed benchmark-defined evidence-path checks in 35% of cases for GPT-4o-mini and 33% for GPT-4o. The evidence gate detects unsupported omissions and uses constrained revision to bring outputs into verifier-level compliance with benchmark-defined evidence requirements. These results show that computable evidence constraints can make unsupported clinical omissions explicit, measurable, and correctable in diabetes-focused LLM outputs.", "date": "2026-06-23", "categories": []}
{"id": "2606.25651", "title": "MedGuards: Multi-Agent System for Reliable Medical Error Detection and Correction", "abstract": "As Large Language Models (LLMs) are increasingly deployed in healthcare settings, accurate error detection and correction in generated or existing text becomes critical, as even minor mistakes can pose risks to patient safety. Existing methods for error detection and correction, including automated checks and heuristic-based approaches, do not generalize well across unseen datasets. In this paper, we propose MedGuards as a medical safety guardrail, which is a new framework that treats medical error detection and correction as a multi-agent in-context learning task. Specialized agents separately detect, localize, and correct errors, while a confidence-guided arbitration mechanism resolves disagreements using reasoning traces and confidence scores. This design enhances interpretability, robustness, and adaptability, without requiring additional training of the base LLMs. Additionally, we introduce the Keyword-Prioritized Correction Score (KPCS), a new evaluation metric that considers whether critical keywords within the reference text are generated correctly, providing a more comprehensive assessment than conventional metrics. Experiments across four multilingual medical datasets consisting of clinical notes demonstrate significant improvements by the proposed framework across several metrics and models. Our aim is to enable safer deployment of LLMs in real-world healthcare applications. For reproducibility, we make our code publicly available at https://github.com/congboma/MedGuards.", "date": "2026-06-24", "categories": []}
{"id": "2606.26205", "title": "Knowledge-augmented Agentic AI for Mental Health Medication Information Seeking", "abstract": "Patients increasingly seek medication information online, yet safety knowledge for psychiatric drugs is split between regulatory adverse-event records, which are authoritative but abstract, and patient narratives, which are experience-near but unvalidated. Integrating them without conflating evidence and anecdote is especially consequential in psychiatry, where poorly contextualised information can amplify fear, nocebo responses, and non-adherence. Here we develop a provenance-aware, knowledge-graph-based multi-agent framework unifying 466,525 Reddit posts, 60,782 WebMD reviews, and twenty years of U.S. FDA Adverse Event Reporting System records for nine antidepressants. A large-language-model entity-recognition pipeline benchmarked against physician annotations reached highest F1 scores of 0.969 for medications and 0.973 for conditions. The two community platforms were far more concordant with each other (overlap up to a Jaccard similarity of 0.905) than with regulatory reports, indicating that patient-generated data form a partly independent safety signal. For sertraline, many adverse events appeared in community sources hundreds of days before the corresponding FDA date. A Neo4j knowledge graph grounded in ATC-N, ICD-10, and MedDRA vocabularies preserves provenance, keeping every claim traceable and regulatory facts distinct from patient experience. These results establish source-aware integration as a route to more auditable psychiatric medication information, with usefulness and patient benefit to be tested prospectively.", "date": "2026-06-24", "categories": []}
{"id": "2606.27174", "title": "RecallRisk-BERT: A Multi-Task Framework for Post-Report Medical Device Recall Triage", "abstract": "Medical device recalls are a critical regulatory mechanism for protecting patient safety. The growing volume of FDA recall records presents challenges in post-report recall triage, severity assessment, and root-cause interpretation. Existing studies mostly address recall occurrence prediction or root-cause analysis separately, while joint modeling of recall severity and root-cause categories has received limited attention. We develop an automated recall triage framework using 54,165 FDA medical device recall records from openFDA, covering the period from 2002 to October 2025. We first evaluate classical machine learning and boosting-based models for recall severity and root-cause category prediction. We then develop RecallRisk-BERT, a multi-task model that combines PubMedBERT-based textual representations of recall narratives with embedding-based representations of structured categorical features, including product code, regulation number, and medical specialty. The model simultaneously predicts recall severity (Class I/II/III) and a consolidated root-cause category (9 classes). Performance was evaluated using accuracy, macro-averaged precision, recall, F1-score, and ROC-AUC. In single-task severity prediction, our LightGBM-based text--tabular configuration achieved the strongest performance, with an accuracy of 0.963, macro-F1 of 0.856, and ROC-AUC of 0.974. In the multi-task setting, RecallRisk-BERT substantially outperformed the single-task PubMedBERT baseline. Model-derived risk rankings were strongly consistent with observed root-cause severity patterns (rho = 0.983, p = 1.936e-6). These findings indicate that text--tabular learning can support scalable post-report recall triage, regulatory decision support, and model-based root-cause risk analysis.", "date": "2026-06-25", "categories": []}
{"id": "2606.28445", "title": "LoRA-Tuned Large Language Models for Dementia Detection via Multi-View Speech-Derived Features", "abstract": "Early detection of dementia enables timely intervention, and reflecting cognitive impairment, spontaneous speech offers a non-invasive screening modality. Conventional approaches often focus on a single representational dimension -- such as acoustic descriptors, pause modeling, automatic speech recognition (ASR) transcripts, or multimodal fusion -- limiting integrative reasoning across heterogeneous cognitive symptoms. We propose a low-rank adaptation (LoRA)-tuned large language model (LLM) that performs structured multi-view reasoning over four complementary speech-derived signals: ASR transcripts with pause markers, discourse-level topic cues, temporal fluency statistics, and phonological sequences. These cues are encoded within a unified prompt, enabling a single LLM to learn a coherent decision function without modality-specific encoders or late-stage fusion. On ADReSSo, our best model achieves an F1-score of 90.14%, and ablation confirms the complementary contribution of each view.", "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": "2606.29503", "title": "The Verbose Context Problem in Medical Records", "abstract": "The verbose context problem occurs when structured concepts have token-inefficient textual representations. This bottleneck is acute in population health: cohort-level analysis of longitudinal patient records requires reasoning over thousands of medically-coded events, often exceeding 400K tokens in total. We present PopMedQA, a benchmark isolating this problem through computational tasks on groups of longitudinal patient records. We construct the benchmark using neopatient, a new library for language-controlled generation of artificial patient records. Through extensive ablations -- including prompting strategies, prompt compression, and agentic decomposition -- we find that domain-independent methods fail to alleviate the verbose context problem. There remains significant opportunity to exploit domain-specific structure in language model inputs for population-scale reasoning.", "date": "2026-06-28", "categories": []}
{"id": "2606.29605", "title": "How much of an LLM-generated clinical corpus is actually new? A production-scale measurement of content redundancy for provenance classification", "abstract": "Clinical machine learning increasingly relies on training corpora generated by large language models (LLMs) rather than annotated by clinicians, and such corpora are described and reused largely on the basis of their reported scale. We test whether volume reflects information content. Analysing the complete output of a multi-agent clinical extraction pipeline applied to 167,034 patient narratives, 2.51 billion generated tokens across the ten text-bearing channels of an eleven-channel pipeline, we introduce Provenance-based Redundancy Decomposition, a token-level classification of the entire output by source. Only 10.9% of the output is trainable-unique content while 79.4% is redundant; raw token count overstates information content by roughly ninefold. The redundancy arises through two distinct mechanisms, verbatim copying of source context into per-item fields, and duplication of generated text across records, of which only the former is losslessly removable. An independent, model-free analysis based on lossless compression confirms the redundancy, recovering the two mechanisms without reference to the provenance labels. One pipeline channel carries almost no redundancy, showing that the level of redundancy depends on how each channel is structured rather than being a fixed property of LLM extraction. Because uncorrected redundancy up-weights the longer, more complex presentations that generate the most items, it skews the token-level training distribution of the corpus, a property we measure directly. In a controlled downstream test, de-duplicating the corpus before adaptation improved a clinical encoder on external disease-recognition benchmarks at equal token budget, robustly across adaptation depths and replicated on a second benchmark, confirming that the redundancy carries a measurable cost beyond storage. The classification tool is released openly.", "date": "2026-06-28", "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": "2607.00147", "title": "RareDxR1: Autonomous Medical Reasoning for Rare Disease Diagnosis Beyond Human Annotation", "abstract": "Rare disease differential diagnosis is a critical yet arduous clinical task, requiring physicians to identify precise phenotypes from complex, unstructured patient symptoms and execute intricate reasoning within a vast search space. However, existing AI approaches typically rely on pipeline-based phenotype extraction or retrieval-augmented generation, which suffer from critical information loss due to predefined ontologies, retrieval bottlenecks, and a lack of diagnostic logic. To address these challenges, we introduce RareDxR1, an end-to-end reasoning-centric large language model designed for open-domain rare disease diagnosis directly from unstructured clinical notes. We design a progressive end-to-end training framework by synergizing knowledge internalization with autonomous evolutionary learning, thereby bypassing reliance on structured phenotypes and closed-set decision-making. To overcome the limitations of RAG and phenotype restriction, we enabled the deep internalization of fragmented rare-disease knowledge directly into the model's parameters. Moreover, to bridge the gap between model generation and expert reasoning, we propose Reflection-Enhanced Reasoning Sampling (RERS), a strategy that synthesizes expert-level diagnostic trajectories by learning from failures without human annotation. Additionally, we propose a dual-level curriculum reinforcement learning approach for gradually mastering rare disease diagnosis. Experimental results demonstrate that RareDxR1 achieves state-of-the-art accuracy across different benchmarks, marking a significant breakthrough in open-domain rare disease diagnosis. Our code and dataset will be publicly available.", "date": "2026-06-30", "categories": []}