{"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": "2604.08552", "title": "Automated Standardization of Legacy Biomedical Metadata Using an Ontology-Constrained LLM Agent", "abstract": "Scientific metadata are often incomplete and noncompliant with community standards, limiting dataset findability, interoperability, and reuse. Even when standard metadata reporting guidelines exist, they typically lack machine-actionable representations. Producing FAIR datasets requires encoding metadata standards as machine-actionable templates with rich field specifications and precise value constraints. Recent work has shown that LLMs guided by field names and ontology constraints can improve metadata standardization, but these approaches treat constraints as static text prompts, relying on the model's training knowledge alone. We present an LLM-based metadata standardization system that queries standard reporting guidelines and authoritative biomedical terminology services in real time to retrieve canonically correct standards on demand. We evaluate this approach on 839 legacy metadata records from the Human BioMolecular Atlas Program (HuBMAP) using an expert-curated gold standard for exact-match assessment. Our evaluation shows that augmenting the LLM with real-time tool access consistently improves prediction accuracy over the LLM alone across both ontology-constrained and non-ontology-constrained fields, demonstrating a practical approach to automated standardization of biomedical metadata.", "date": "2026-03-10", "categories": []}
{"id": "2604.23938", "title": "TSAssistant: A Human-in-the-Loop Agentic Framework for Automated Target Safety Assessment", "abstract": "Target Safety Assessment (TSA) requires systematic integration of genetic, transcriptomic, target homology, pharmacological, and clinical data to evaluate potential safety liabilities of therapeutic targets. This process is labor-intensive and expert-dependent, posing challenges in scalability and reproducibility. We present TSAssistant, a human-in-the-loop multi-agent framework that decomposes TSA report generation into a workflow of specialized subagents: Research Subagents that each ground and cite a single TSA domain, and Synthesis Subagents that integrate findings across domains. Subagents retrieve and synthesize evidence from curated biomedical sources through standardized tool interfaces and produce individually citable, evidence-grounded sections, with behavior shaped by a hierarchical instruction architecture that separates coordination logic from domain expertise and user intent. To complement these soft constraints, programmatic execution hooks and persistent memory stores enforce hard constraints across the workflow, while an interactive refinement loop allows experts to review and revise individual sections with full conversational context preserved across iterations. Rather than a single holistic comparison, we decompose report quality into reproducibility, evidential grounding, task-level accuracy, and controllability under expert oversight, finding high reproducibility and grounding, substantial agreement with the human reference, and net-positive expert-driven refinement.", "date": "2026-04-27", "categories": []}
{"id": "2606.14031", "title": "Applicability Condition Extraction for Therapeutic Drug-Disease Relations", "abstract": "Identifying conditions that a certain drug takes therapeutic effect on a target disease is crucial for clinical decision-making support. However, most existing biomedical information extraction methods have focused on identifying only relations between drugs and diseases, while largely overlooking the context-specific conditions where such relations can apply. To address this problem, we introduce the task of applicability condition extraction for therapeutic drug-disease relations from biomedical research literature. We create the first dataset that has manually annotated triples of drugs, diseases, and applicability conditions on biomedical paper abstracts with 1,119 drug-disease pairs. Using this dataset, we systematically evaluate the performance of a range of existing methods. In addition, we propose a new method that enhances LoRA to consider relations between drugs and diseases. Our method consistently outperforms strong baselines across different evaluation settings.", "date": "2026-06-12", "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": "2601.12805", "title": "SciHorizon-GENE: Benchmarking LLM for Life Sciences Inference from Gene Knowledge to Functional Understanding", "abstract": "Large language models (LLMs) have shown growing promise in biomedical research, particularly for knowledge-driven interpretation tasks. However, their ability to reliably reason from gene-level knowledge to functional understanding, a core requirement for knowledge-enhanced cell atlas interpretation, remains largely underexplored. To address this gap, we introduce SciHorizon-GENE, a large-scale gene-centric benchmark constructed from authoritative biological databases. The benchmark integrates curated knowledge for over 190K human genes and comprises more than 540K questions covering diverse gene-to-function reasoning scenarios relevant to cell type annotation, functional interpretation, and mechanism-oriented analysis. Motivated by behavioral patterns observed in preliminary examinations, SciHorizon-GENE evaluates LLMs along four biologically critical perspectives: research attention sensitivity, hallucination tendency, answer completeness, and literature influence, explicitly targeting failure modes that limit the safe adoption of LLMs in biological interpretation pipelines. We systematically evaluate a wide range of state-of-the-art general-purpose and biomedical LLMs, revealing substantial heterogeneity in gene-level reasoning capabilities and persistent challenges in generating faithful, complete, and literature-grounded functional interpretations. Our benchmark establishes a systematic foundation for analyzing LLM behavior at the gene scale and offers insights for model selection and development, with direct relevance to knowledge-enhanced biological interpretation.", "date": "2026-01-19", "categories": []}
{"id": "2605.07022", "title": "Self-Driving Datasets: From 20 Million Papers to Nuanced Biomedical Knowledge at Scale", "abstract": "Manually curated biomedical repositories -- spanning bioactivity, genomics, and chemistry -- are expensive to maintain, lag behind primary literature, and discard experimental context, obscuring nuances needed to assess data correctness and coverage. We show that PubMed itself can be autonomously and cost-effectively turned into structured datasets that are larger, more nuanced, and more accurate than the curated databases they replace. We present three coupled contributions: (1) an LLM-based entity-tagging pipeline, grounded in nine biomedical ontologies, that tags 4.5B entities across 19 categories in a 22.5M-paper, 2.5T-token PubMed corpus; (2) hybrid sparse-dense retrieval supporting entity-filtered semantic queries over the tagged corpus; and (3) Starling, a multi-agent deep research system that, given only a natural-language task description, designs precision- and recall-targeted retrieval filters, induces an extraction schema, and emits structured records with nuance-rich fields and supporting passages. Across six tasks -- blood-brain barrier permeability, oral bioavailability, acute toxicity (LD50), gene-disease associations, protein subcellular localization, and chemical reactions -- Starling produces ~6.3M records (91K-3M per task); several are, to our knowledge, the largest public datasets for their property. Frontier-model rejection of our extractions is 0.6-7.7% across tasks, far below error rates we measure on widely used curated counterparts (e.g., 16.5% on BBB_Martins, 7.3% on Bioavailability_Ma). Beyond scale and accuracy, the supporting passages carry nuance tabular databases discard -- e.g., oral bioavailability may depend on fed vs. fasted state. Together, the corpus, retrieval, and agent establish a foundation for AI-driven therapeutic design. Code and datasets: https://github.com/starling-labs/starling.", "date": "2026-05-07", "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": "2606.15735", "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": "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": "2606.15412", "title": "Few-Shot Biomedical Relation Extraction with Large Language Models: A Viable Alternative to Supervised Learning?", "abstract": "Biomedical relation extraction (BioRE) is a key step in transforming biomedical literature into structured knowledge. However, most existing approaches rely on supervised models trained on costly annotated datasets, limiting their scalability and adaptability across relation types and domains. We investigate few-shot BioRE using prompt-based learning with large language models (LLMs) and compare two task formulations: pairwise classification, which predicts relations for individual entity pairs, and joint generation, which extracts multiple relations in a single model call. Experiments on the BioREDirect dataset reveal a clear precision-recall trade-off. Pairwise classification achieves higher recall, whereas joint generation is more precise and computationally efficient. The best-performing model achieves a micro-F1 score of 0.44, substantially outperforming previous few-shot results (0.34) while remaining below the supervised baseline (0.56). Much of this gap is attributable to a single ambiguously defined relation type. When evaluated using macro-F1, which better captures performance across relation types in an imbalanced setting, prompt-based approaches outperform the supervised baseline (0.45 vs. 0.38), particularly on rare relation types. These findings highlight the potential of LLMs for BioRE in low-resource settings and underscore the importance of well-defined relation schemas.", "date": "2026-06-13", "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": "2606.16211", "title": "Weaving Multi-Source Evidence for Biomedical Reasoning: The BioMedHop Benchmark and BioWeave Framework", "abstract": "Biomedical question answering (QA) increasingly requires reasoning over interacting entities, where supporting evidence is scattered across biomedical knowledge graphs, literature documents, and web-accessible resources. However, existing biomedical QA benchmarks mainly focus on exam-style knowledge, literature comprehension, or short-range multi-hop inference, leaving source-conditioned graph reasoning and evidence topology construction underexplored. To fill this gap, we introduce BioMedHop, a multi-source graph-grounded benchmark for evaluating biomedical reasoning over structured evidence topologies. BioMedHop contains 10,045 instances across KG, document, web, and hybrid evidence settings, covering shared-neighbor matching, intersection reasoning, path-based reasoning, and counting, with option-based, open-ended, and numeric count renderings. To support this benchmark, we further propose BioWeave, a source-aware reasoning framework that retrieves biomedical KG paths, gathers supporting clues from documents and web sources, assembles them into a unified evidence graph, and verifies answers through entity-level evidence support. Comprehensive experiments show that BioWeave achieves the best overall performance among compared methods on BioMedHop, outperforming the strong hybrid baseline ToG-2 by 10.5% in the overall average. Moreover, BioWeave consistently improves different LLM backbones and enables smaller models, such as Qwen3-4B, to achieve reasoning performance comparable to GPT-4-Turbo.", "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": "2505.23823", "title": "RAGPPI: RAG Benchmark for Protein-Protein Interactions in Drug Discovery", "abstract": "Retrieving the biological impacts of protein-protein interactions (PPIs) is essential for target identification (Target ID) in drug development. Given the vast number of proteins involved, this process remains time-consuming and challenging. Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) frameworks have supported Target ID; however, no benchmark currently exists for identifying the biological impacts of PPIs. To bridge this gap, we introduce the RAG Benchmark for PPIs (RAGPPI), a factual question-answer benchmark of 4,420 question-answer pairs that focus on the potential biological impacts of PPIs. Through interviews with experts, we identified criteria for a benchmark dataset, such as a type of QA and source. We built a gold-standard dataset (500 QA pairs) through expert-driven data annotation. We developed an ensemble auto-evaluation LLM that incorporates expert labeling characteristics, average fact-abstract similarity (F1), and low-similarity fact counts (F2), enabling the construction of a silver-standard dataset (3,720 QA pairs). We are committed to maintaining RAGPPI as a resource to support the research community in advancing RAG systems for drug discovery QA solutions.", "date": "2025-05-28", "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": "2606.12854", "title": "Small LLMs for Biomedical Claim Verification: Cost-Effective Fine-Tuning, Structural Dataset Shortcuts, and Cross-Domain Generalization", "abstract": "Large Language Models such as GPT-4o and GPT-5 achieve strong zero-shot performance on biomedical claim verification, but cost and opacity limit scalable use. We fine-tune three small LLMs: Phi-3-mini (3.8B), Qwen2.5-3B, and Mistral-7B, via QLoRA on SciFact and HealthVer, providing the first study of QLoRA models against GPT-4o and fine-tuned BioLinkBERT encoders. Mistral-7B QLoRA surpasses both GPT-4o and GPT-5 (up to 12% F1 gain) at a fractional cost using just 1,008 training examples. We conduct extensive in-domain and cross-domain evaluation: models trained on SciFact tested on HealthVer and vice versa, at matched sizes to isolate dataset structure from data quantity. We identify a previously unreported structural artifact in SciFact that inflates in-domain scores, and show through bidirectional out-of-domain evaluation that training on structurally sound data enables robust cross-domain transfer. We plan to release all code and adapter checkpoints.", "date": "2026-06-11", "categories": []}
{"id": "2606.13051", "title": "AAbAAC: An Annotated Corpus for Autoimmunity Information Extraction", "abstract": "Despite advances in information extraction driven by deep learning and large language models, performance gaps remain in highly specialized biomedical fields, where domainspecific complexity poses challenges for generalist models. In this work, we focus on the domain of autoimmunity, where the main entities of interest are autoimmune diseases, autoantibodies (i.e., molecules that may mark or cause these diseases), their molecular targets, their location in the body, and their associated clinical signs. Herein, we present AAbAAC (AutoAntibodies and Autoimmunity Annotated Corpus), a corpus of 115 abstracts selected from PubMed, where we manually annotated entities and their relationships. First, AAbAAC was used to evaluate several methods on the task of named entity recognition (NER), and secondly, to fine-tune NER models. Our study demonstrates the utility of AAbAAC for information extraction in the domain of autoimmunity, showing expected improvement in NER performance after finetuning. This illustrates the value of small-scale annotation efforts for specialized domains and contributes to the computational study of autoimmunity. The AAbAAC corpus is available at https://github.com/f-maury/AAbAAC.", "date": "2026-06-11", "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": "2408.02600", "title": "BioMamba: Domain-Adaptive Biomedical Language Models", "abstract": "Background. Biomedical language models should improve performance on biomedical text while retaining general-language-modeling fluency. For Mamba-based models, this trade-off has not been systematically studied across biomedical literature and clinical text. Methods. We developed BioMamba, a family of biomedical Mamba2 models at five scales obtained by continued pretraining of released public Mamba2 checkpoints on a balanced 80%/10%/10% mixture of PubMed abstracts, the Colossal Clean Crawled Corpus (C4), and Wikipedia. The contribution is the adaptation recipe and the accompanying open-weight checkpoints.\n  Results. Across five scales, BioMamba consistently lowered PubMed perplexity, improved Wikipedia-style held-out perplexity by 1.46-4.72 PPL, and left C4 perplexity essentially unchanged. On six out-of-domain multiple-choice benchmarks, BioMamba stayed within +/-3 percentage points of Mamba2 with no systematic regression. After supervised fine-tuning, BioMamba+SFT matched or exceeded Mamba2+SFT on MIMIC-IV note completion and discharge summary generation at every evaluated scale, and improved PubMedQA at every scale. The strongest model (BioMamba-2.7B) reached a PubMed perplexity of 5.28 and accuracies of 90.24% and 73.00% on BioASQ and PubMedQA, respectively.\n  Conclusions. A balanced domain-adaptive continued pretraining recipe strengthens Mamba2 language models on biomedical literature and clinical text while preserving general-language-modeling fluency.", "date": "2024-08-05", "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": "2606.11208", "title": "BioDivergence: A Benchmark and Evaluation Framework for Hidden Contextual Contradictions in Biomedical Abstracts", "abstract": "Biomedical findings often seem to conflict across studies, but many of these differences are context-dependent rather than true contradictions. Variations in cohort, geography, assay protocol, disease subtype, and clinical setting can make both claims locally valid. Existing NLI and scientific claim-verification benchmarks reduce such cases to entailment, contradiction, or neutral, failing to capture the contextual structure behind divergence. To address this, we introduce BioDivergence, an evaluation framework with a six-class conflict taxonomy, a 13-axis divergence ontology, and four structured outputs per claim pair: conflict type, divergence axes, dominant confounder, and reconciliation explanation. We release BioDivergence-Silver-v1.0, an article-disjoint silver benchmark of 11,865 claim pairs across five biomedical domains, alongside a legacy deduplicated variant for comparison. Results show notable ranking differences between the two variants, with the fine-tuned reference model dropping about 12 points under the article-disjoint setting, while Mistral-7B-Instruct-v0.3 achieves 0.5523 accuracy and 0.3894 contextual-F1 on the 842-example primary test set. BioDivergence offers a more faithful way to distinguish contextual divergence from direct contradiction and to separate article-level memorization from genuine task learning.", "date": "2026-04-23", "categories": []}
{"id": "2606.11361", "title": "A PubMed-Scale Dataset of Structured Biomedical Abstracts", "abstract": "Structured abstracts are important for biomedical literature processing, by facilitating information retrieval, text mining, and knowledge synthesis. However, a vast portion of abstracts indexed in PubMed remain unstructured, presenting a significant bottleneck for downstream text-processing workflows and applications. To resolve this limitation, we introduce Structured PubMed, a comprehensive corpus of section-labeled biomedical abstracts compiled from the complete PubMed database, encompassing over 23.2 million research-article records. The corpus is divided into two distinct subsets: a collection of 5.9 million author-structured abstracts parsed from official XML files, and an automatically labeled collection of 17.2 million originally unstructured abstracts structured via a verbatim-extraction Large Language Model pipeline. Every record is harmonized under a unified five-section schema and mapped to its original PubMed identifier, publication type, and publication date. This dataset can be utilized to train sentence-classification models, benchmark text-segmentation architectures, and perform large-scale, section-specific information extraction at an unprecedented PubMed-wide scale.", "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": "2509.11517", "title": "PeruMedQA: Benchmarking Large Language Models (LLMs) on Peruvian Medical Exams -- Dataset Construction and Evaluation", "abstract": "BACKGROUND: Medical large language models (LLMs) have demonstrated remarkable performance in answering medical examinations. However, the extent to which this high performance is transferable to medical questions in Spanish and from a Latin American country remains unexplored. This knowledge is crucial as LLM-based medical applications gain traction in Latin America. AIMS: To build a dataset of questions medical examinations taken by Peruvian physicians pursuing specialty training; to fine-tune a LLM on this dataset; to evaluate and compare the performance in terms of accuracy between vanilla LLMs and the fine-tuned LLM. METHODS: We curated PeruMedQA, a multiple-choice question-answering (MCQA) dataset containing 8,380 questions spanning 12 specialties (2018-2025). We selected ten medical LLMs, including medgemma-4b-it and medgemma-27b-text-it, and developed zero-shot task specific prompts to answer the questions. We employed parameter-efficient fine tuning (PEFT) and low-rand adaptation (LoRA) to fine-tune medgemma-4b-it utilizing all questions except those from 2025 (test set). RESULTS: Medgemma-27b showed the highest accuracy across all specialities, achieving the highest score of 89.29% in Psychiatry; yet, in two specialties, OctoMed-7B exhibited slight superiority: Neurosurgery with 77.27% and 77.38, respectively; and Radiology with 76.13% and 77.39%, respectively. Across specialties, most LLMs with <10 billion parameters exhibited <50% of correct answers. The fine-tuned version of medgemma-4b-it emerged victorious against all LLMs with <10 billion parameters and rivaled a LLM with 70 billion parameters across various examinations. CONCLUSIONS: For medical AI applications and research that require knowledge bases from Spanish-speaking countries and those exhibiting similar epidemiological profile to Peru's, interested parties should utilize medgemma-27b-text-it.", "date": "2025-09-15", "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": "2606.10471", "title": "Detecting Speculative Language in Biomedical Texts using Recurrent Neural Tensor Networks", "abstract": "In this investigation, we delve into the automated detection of speculative language within biomedical articles by utilizing distributed sentence representations and advanced deep learning techniques. The implications of such identification extend to information retrieval, multi-document summarization, and the exploration of new knowledge. Our exploration encompasses two distinct approaches for acquiring distributed sentence representations: the Paragraph Vector model and the Recursive Neural Tensor Network. These methodologies are then rigorously compared against three foundational baseline algorithms: Support Vector Machines, Naive Bayes, and pattern matching. Our findings reveal that the Recursive Neural Tensor Network (RNTN) demonstrates a slight performance edge (F1 = 0.885) over the top-performing baseline, the linear bigram SVM (F1 = 0.881). Meanwhile, the Paragraph Vector model proves less effective (F1 = 0.368), even after extensive training using an expansive, unlabeled dataset. We engage in a comprehensive discourse on the factors influencing these performance disparities and provide insightful recommendations for future research directions.", "date": "2026-06-09", "categories": []}
{"id": "2507.15152", "title": "What Level of Automation is \"Good Enough\"? A Benchmark of Large Language Models for Meta-Analysis Data Extraction", "abstract": "Automating data extraction from full-text randomised controlled trials (RCTs) for meta-analysis remains a significant challenge. This study evaluates the practical performance of three LLMs (Gemini-2.0-flash, Grok-3, GPT-4o-mini) across tasks involving statistical results, risk-of-bias assessments, and study-level characteristics in three medical domains: hypertension, diabetes, and orthopaedics. We tested four distinct prompting strategies (basic prompting, self-reflective prompting, model ensemble, and customised prompts) to determine how to improve extraction quality. All models demonstrate high precision but consistently suffer from poor recall by omitting key information. We found that customised prompts were the most effective, boosting recall by up to 15\\%. Based on this analysis, we propose a three-tiered set of guidelines for using LLMs in data extraction, matching data types to appropriate levels of automation based on task complexity and risk. Our study offers practical advice for automating data extraction in real-world meta-analyses, balancing LLM efficiency with expert oversight through targeted, task-specific automation.", "date": "2025-07-20", "categories": []}
{"id": "2602.17911", "title": "Condition-Gated Reasoning for Context-Dependent Biomedical Question Answering", "abstract": "Current biomedical question answering (QA) systems often assume that medical knowledge applies uniformly, yet real-world clinical reasoning is inherently conditional: nearly every decision depends on patient-specific factors such as comorbidities and contraindications. Existing benchmarks do not evaluate such conditional reasoning, and retrieval-augmented or graph-based methods lack explicit mechanisms to ensure that retrieved knowledge is applicable to given context. To address this gap, we propose CondMedQA, the first benchmark for conditional biomedical QA, consisting of multi-hop questions whose answers vary with patient conditions. Furthermore, we propose Condition-Gated Reasoning (CGR), a novel framework that constructs condition-aware knowledge graphs and selectively activates or prunes reasoning paths based on query conditions. Our findings show that CGR more reliably selects condition-appropriate answers while matching or exceeding state-of-the-art performance on biomedical QA benchmarks, highlighting the importance of explicitly modeling conditionality for robust medical reasoning.", "date": "2026-02-20", "categories": []}
{"id": "2605.11502", "title": "Robust Biomedical Publication Type and Study Design Classification with Knowledge-Guided Perturbations", "abstract": "Accurately and consistently indexing biomedical literature by publication type and study design is essential for supporting evidence synthesis and knowledge discovery. Prior work on automated publication type and study design indexing has primarily focused on expanding label coverage, enriching feature representations, and improving in-domain accuracy, with evaluation typically conducted on data drawn from the same distribution as training. Although pretrained biomedical language models achieve strong performance under these settings, models optimized for in-domain accuracy may rely on superficial lexical or dataset-specific cues, resulting in reduced robustness under distributional shift. In this study, we introduce an evaluation framework based on controlled semantic perturbations to assess the robustness of a publication type classifier and investigate robustness-oriented training strategies that combine entity masking and domain-adversarial training to mitigate reliance on spurious topical correlations. Our results show that the commonly observed trade-off between robustness and in-domain accuracy can be mitigated when robustness objectives are designed to selectively suppress non-task-defining features while preserving salient methodological signals. We find that these improvements arise from two complementary mechanisms: (1) increased reliance on explicit methodological cues when such cues are present in the input, and (2) reduced reliance on spurious domain-specific topical features. These findings highlight the importance of feature-level robustness analysis for publication type and study design classification and suggest that refining masking and adversarial objectives to more selectively suppress topical information may further improve robustness. Data, code, and models are available at: https://github.com/ScienceNLP-Lab/MultiTagger-v2/tree/main/ICHI", "date": "2026-05-12", "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": "2606.07548", "title": "Evaluating Advanced Prompting on Gemini Flash for Multi-Hop Biomedical QA", "abstract": "The MedHopQA challenge presents a critical test for Large Language Models (LLMs): complex, multi-hop reasoning in the high-stakes biomedical domain. This paper details our direct API-based evaluation of Google's Gemini Flash models, focusing on the impact of advanced prompt engineering. We designed a sophisticated, multi-component prompt for Gemini 2.0 Flash that combined role-playing, explicit multi-shot Chain-of-Thought (CoT) examples, and detailed formatting rules. Our best run, using this complex prompt, achieved a Concept Level Score of 0.720. This result dramatically outperformed a baseline prompt which scored only 0.565. Remarkably, this performance on the efficient Gemini 2.0 Flash was almost identical to the result from the next-generation Gemini 2.5 Flash. Our findings demonstrate that sophisticated prompt design is a critical factor for unlocking the full reasoning capabilities of modern LLMs.", "date": "2026-05-05", "categories": []}
{"id": "2606.07721", "title": "Automatic Extraction of Structured Information from Brain MRI Reports Using an Open-Weight Large Language Model", "abstract": "Objectives: Automatic data extraction from free-text radiology reports enables large-scale research, but few studies assessed the performance of large language models (LLMs) on Dutch neuroradiology reports. Methods: We analyzed 947 brain MRI reports from a tertiary memory clinic (2016-2021), authored by consultant neuroradiologists. Trained medical students annotated thirty variables; 100 reports were double-annotated to assess inter-rater reliability. We evaluated the performance of the open-weight LLM LLaMA 3.1 using different languages (Dutch vs. English translation) and few-shot prompting with different example selection strategies. Performance was evaluated using balanced accuracy for categorical variables, accuracy and mean absolute error for counts, and text similarity for free-text. Metrics were computed across 10 random splits of the 947 reports. Results: LLaMA 3.1 demonstrated high zero-shot performance for visual rating scores (mean [95%-CI]): Medial Temporal Atrophy: 90% [77-100%] on the left and 96% [94-99%] on the right, Global Cortical Atrophy: 87% [83-91%], and Fazekas: 94% [93-96%]. Microbleed mentions were detected with 93% accuracy [92-95%] and infarct mentions with 82% [80-84%]. Text similarity for lesion location reached 0.95 [0.95-0.96]. Performance was lower for numerical variables: 80% [78-82%] for the number of microbleeds and 66% [63-68%] for infarcts. English translation yielded comparable results. Few-shot prompting improved performance for numerical variables, achieving 92% [90-93%] for microbleeds and 81% [77-85%] for infarcts using structural similarity-based selection. Conclusion: LLaMA 3.1 shows strong potential for extracting data from Dutch neuroradiology reports. Few-shot prompting enhances performance for numerical variables, whereas challenges remain for location-specific variables.", "date": "2026-06-05", "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": "2606.08673", "title": "ClinicalAligner26AM: A Cross-Lingual Aligner for Dataset Translation; Evidences from the MultiClinCorpus Shared Task", "abstract": "Word-level cross-lingual alignment is central to annotation projection, translation auditing, and cross-lingual faithfulness estimation, yet existing neural aligners are rarely adapted to specialized domains. In this paper, we introduce ClinicalAligner26AM, a large-context multilingual aligner model for biomedical and clinical text initialized from ClinicalEncoder26AM. Our training recipe is inspired by AWESoME Align. We build our soft alignment target by sharpening with Sinkhorn-Knop optimal transport a cost matrix established for parallel clinical texts and conversations through the fusion of sentence-level, phrase-level, and token-level signals. We distill this sharpened alignment matrix directly into our student aligner, by encouraging its naive cosine-based token similarity scores to match this target. At inference time, we project source-span scores through the learned token alignment matrix and decode the longest valid high-scoring span in the target text, optionally supported by MultiClinNER predictions summarized in Appendix B. We evaluate CA26AM on the MultiClinCorpus shared task, which projects Spanish clinical entity annotations into six target languages. Our two submitted systems ranked respectively first and second across all languages and entity types, with character-weighted F1 scores above 0.95 in nearly all settings.", "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": "2605.20628", "title": "Divide-Prompt-Refine: a Training-Free, Structure-Aware Framework for Biomedical Abstract Generation", "abstract": "Biomedical abstracts play a critical role in downstream NLP applications, such as information retrieval, biocuration, and biomedical knowledge discovery. However, a non-trivial number of biomedical articles do not have abstracts, diminishing the utility of these articles for downstream tasks. We propose DPR-BAG (Divide, Prompt, and Refine for Biomedical Abstract Generation), a training-free, zero-shot framework that generates coherent and factually grounded abstracts for biomedical articles with full text but no abstract. DPR-BAG decomposes full-text documents into structured rhetorical facets following the Background-Objective-Methods-Results-Conclusions (BOMRC) schema, performs parallel LLM-based summarization for each facet, and applies a final refinement stage to restore global discourse coherence. On PMC-MAD, a distribution-aligned dataset of 46,309 biomedical articles, DPR-BAG improves abstractive novelty over strong extractive and fine-tuned baselines, while maintaining factual consistency. Our ablation study reveals a counterintuitive finding: increasing prompt complexity or explicitly injecting entity-level guidance can degrade factual alignment, highlighting the importance of controlled prompting strategies. These findings underscore the potential of training-free, structure-aware frameworks for scalable biomedical abstract generation in low-resource settings. Our data and code are available at https://huggingface.co/datasets/pmc-mad/PMC-MAD and https://github.com/ScienceNLP-Lab/MultiTagger-v2/tree/main/DPR-BAG.", "date": "2026-05-20", "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": "2606.02403", "title": "AutoForest: Automatically Generating Forest Plots from Biomedical Studies with End-to-End Evidence Extraction and Synthesis", "abstract": "Systematic reviews rely on forest plots to synthesise quantitative evidence across biomedical studies, but generating them remains a fragmented and labour-intensive process. Researchers must interpret complex clinical texts, manually extract outcome data from trials, define appropriate interventions and comparators, harmonise inconsistent study designs, and carry out meta-analytic computations-typically using specialised software that demands structured inputs and domain expertise. While recent work has demonstrated that large language models can extract study-level data from unstructured text, no existing system automates the complete pipeline from raw documents to synthesised forest plots. To address this gap, we introduce AutoForest, the first end-to-end system that generates publication-ready forest plots directly from biomedical papers. Given one or more study papers, AutoForest automatically suggests ICO (Intervention, Comparator, Outcome) elements, extracts outcome data, performs statistical synthesis, and renders the final forest plot. We describe the system architecture, user interface and demonstrate its effectiveness on real-world examples through a user study involving clinicians, showing how AutoForest can accelerate evidence synthesis and substantially lower the barrier to conducting meta-analyses.", "date": "2026-06-01", "categories": []}
{"id": "2606.04127", "title": "When Retrieval Doesn't Help: A Large-Scale Study of Biomedical RAG", "abstract": "Medical question answering is a high-stakes setting where factual errors can have serious consequences. Retrieval-augmented generation (RAG) is widely viewed as a promising solution, and prior work has reported substantial gains for large medical QA models. We revisit this assumption across a broad range of open-weight instruction-tuned models spanning 7B to 72B parameters. Across five models, ten biomedical QA datasets, four retrieval methods, and four retrieval corpora, we find that retrieval yields only small and inconsistent improvements over a no-retrieval baseline, typically within 1-2 points. In contrast, the choice of backbone model has a much larger effect than the choice of retriever or corpus, and expert and layman retrieval sources perform similarly in most settings. These results suggest that the main bottleneck is not retrieval quality alone, but the model's limited ability to use retrieved evidence effectively.", "date": "2026-06-02", "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": "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": "2603.05308", "title": "Med-V1: Small Language Models for Zero-shot and Scalable Biomedical Evidence Attribution", "abstract": "Assessing whether an article supports an assertion is essential for hallucination detection and claim verification. While large language models (LLMs) have the potential to automate this task, achieving strong performance requires frontier models such as GPT-5 that are prohibitively expensive to deploy at scale. To efficiently perform biomedical evidence attribution, we present Med-V1, a family of small language models with only three billion parameters. Trained on high-quality synthetic data newly developed in this study, Med-V1 substantially outperforms (+27.0% to +71.3%) its base models on five biomedical benchmarks unified into a verification format. Despite its smaller size, Med-V1 performs comparably to frontier LLMs such as GPT-5, along with high-quality explanations for its predictions. We use Med-V1 to conduct a first-of-its-kind use case study that quantifies hallucinations in LLM-generated answers under different citation instructions. Results show that the format instruction strongly affects citation validity and hallucination, with GPT-5 generating more claims but exhibiting hallucination rates similar to GPT-4o. Additionally, we present a second use case showing that Med-V1 can automatically identify high-stakes evidence misattributions in clinical practice guidelines, revealing potentially negative public health impacts that are otherwise challenging to identify at scale. Overall, Med-V1 provides an efficient and accurate lightweight alternative to frontier LLMs for practical and real-world applications in biomedical evidence attribution and verification tasks. Med-V1 is available at https://github.com/ncbi-nlp/Med-V1.", "date": "2026-03-05", "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": "2606.00971", "title": "HypothesisMed: Inference-Time Answer Fusion and Structured Hypothesis-Space Reporting for Biomedical Question Answering", "abstract": "Biomedical question answering with large language models is commonly evaluated using answer accuracy, but answer accuracy alone does not indicate whether a model can produce parseable outputs, follow structured reliability instructions, recognize weak answer spaces, or avoid confident incorrect commitments. This paper presents HypothesisMed, an inference-time reliability pipeline for biomedical multiple-choice question answering. It combines direct, chain-of-thought, HypothesisMed-v3 prompting, and answer fusion. The final answer is selected by fusion, while HypothesisMed-v3 supplies SPACE labels and confidence information. SPACE labels mark the answer space as VALID, INCOMPLETE, or CONTRADICTED. We evaluate Qwen2.5-7B, Phi-4-mini, DeepSeek-R1-32B, and BioMistral-7B on MedQA, MedMCQA, and PubMedQA using 1,000 examples per dataset. The pipeline improves weighted accuracy over each model's best direct or chain-of-thought baseline while increasing parse and SPACE coverage. We also scale evaluation to Qwen2.5-7B and Phi-4-mini using 10,183 examples per model. Fusion improves Phi-4-mini accuracy from 0.4296 to 0.5192, while Qwen2.5-7B chain-of-thought remains slightly higher in answer accuracy. However, Qwen2.5-7B fusion achieves complete parse and SPACE coverage with much lower false commitment. A 12,000-example SPACE stress test shows answer-space diagnosis remains difficult, with SPACE accuracy of 0.3074 for Qwen2.5-7B and 0.4168 for Phi-4-mini. These results show that answer accuracy, parseability, structured reliability reporting, calibration behavior, and false-commitment behavior are separable capabilities. The main contribution is not a universal state-of-the-art claim, but a reproducible inference-time framework for evaluating biomedical question answering models as auditable workflow components under structured reliability constraints.", "date": "2026-05-31", "categories": []}
{"id": "2606.01394", "title": "UniD$^3$: A Knowledge Graph-Enhanced RAG Framework for Drug-Disease Discovery and Reasoning", "abstract": "Systematic characterization of drug-disease relationships is essential for drug discovery and repurposing, yet is hindered by the heterogeneity and rapid growth of biomedical literature. Existing datasets rely on labor-intensive curation and are often incomplete, while LLM-only approaches suffer from hallucination and weak evidence grounding. We introduce UniD$^3$, a unified framework that integrates Large Language Models with Knowledge Graph-enhanced Retrieval-Augmented Generation (KG-RAG) to extract, organize, and validate drug-disease knowledge across Drug-Disease Matching (DDM), Drug Effectiveness Assessment (DEA), and Drug-Target Analysis (DTA). UniD$^3$ processes 157,849 PubMed articles with Llama 3.3-70B and constructs knowledge graphs via a dual-stage strategy combining paper-level extraction with KG-level consolidation centered on drug and disease entities. These graphs support KG-RAG-based generation of structured datasets, evaluated through external benchmarks, fuzzy matching with curated resources, and clinician review. UniD$^3$ produces six knowledge graphs and large-scale datasets, including 28,915 DDM, 15,042 DEA, and over 4,000 DTA QA pairs. External validation shows strong performance (F1: 0.85-0.87 for DDM/DEA; 0.82 for DTA), with clinician review confirming high reliability (AUROC = 0.90). KG-RAG-augmented models outperform standalone LLMs, and the UniD$^3$ chatbot enables interpretable, citation-supported exploration of drug-disease relationships. UniD$^3$ provides a scalable, extensible framework for transforming unstructured biomedical literature into high-quality, structured drug-disease knowledge, supporting AI-driven discovery, repurposing, and precision medicine.", "date": "2026-05-31", "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": "2606.02545", "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": "2605.30400", "title": "Protocol for evaluating ChatGPT in biomedical association generation and verification using a RAG-enabled, cross-model majority voting workflow", "abstract": "We present a protocol to evaluate ChatGPT's ability to generate disease-centric biomedical associations. It outlines how we generate the associations, validate the biological entities using biomedical ontologies, and verify associations using literature. The protocol includes a self-consistency strategy to assess generative reliability across ChatGPT models. To address ontology exact-match limitations, we provide a use case performing semantic verification through a workflow enabled by Retrieval-Augmented Generation (RAG) powered by open-source large language models (LLMs). This enables LLMs to establish truth over content generated by other LLMs and expose hallucination.", "date": "2026-05-28", "categories": []}
{"id": "2605.30826", "title": "Beyond Agreement: Scoring Panel-Surfaced Biomedical Entity Candidates for Curator Triage", "abstract": "Biomedical NER is deceptively simple for modern LLMs: plausible biomedical mentions are easy to surface, but corpus-convention correctness depends on annotation conventions, span boundaries, entity granularity, and type schemas. Multi-LLM agreement is a salience signal, not corpus-convention correctness. We introduce a candidate-level panel-output benchmark for panel-surfaced candidate verification, where the unit is an aligned candidate surfaced by an explicitly defined multi-model panel rather than a standalone extractor output. The benchmark aligns eight LLMs' predictions over five public biomedical NER datasets into a candidate master table. BioConCal is an in-domain supervised scorer that instantiates this layer with inference-time gold-free agreement, mention, surface-availability, and document features for a fixed candidate stream. In domain, BioConCal improves AUROC from 0.753 for raw agreement to 0.910. At a validation-selected 0.95 precision target it selects 1,340 candidates at empirical test precision 0.939, compared with 293 for raw agreement. This corresponds to candidate-level recall 0.592 and corpus-level recall 0.523 against a within-panel row-label ceiling of 0.883. The main benefit is not recovering entities missed by every panel member, but reshaping a noisy panel stream into a higher-yield review queue. Under entity-type shift, thresholds require target-domain validation, and exact character localization remains a separate deterministic post-processing step.", "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": "2605.28838", "title": "Specialty-Specific Medical Language Model for Immune-Mediated Diseases", "abstract": "Extracting detailed clinical information from free-text medical narratives remains a practical challenge for researchers and healthcare systems. Terminology for immune-mediated and infectious diseases is especially inconsistent across sources, which often limits the ability of general-purpose Natural Language Processing (NLP) systems to capture the relevant biomedical concepts with sufficient granularity. We developed a domain-specific Named Entity Recognition (NER) model tailored to identify disease-related entities occurring in immunology and infectious disease contexts. We assembled and manually annotated a dataset of 371 case reports in collaboration with two clinical specialists, defining twelve entity classes covering immune-mediated and infectious conditions as well as related symptoms and clinical descriptors. We evaluated several modeling strategies, including the MedicalNER architecture with multiple healthcare-specific embeddings, a BERT-based token classification model, and zero-shot NER systems. The strongest performance was obtained with a transformer-based model trained on clinical-domain embeddings, which reached an F1 score of 0.89, consistently outperforming baseline and zero-shot approaches. The combination of specialized embeddings and expert annotation proved particularly valuable for capturing nuanced disease terminology and improving generalization across heterogeneous biomedical text. The prompted LLM baseline achieved substantially lower performance under the same evaluation protocol, reflecting difficulties in producing span-consistent outputs for fine-grained entity boundaries despite detailed prompting. The resulting model provides a structured way to analyze case reports and can support downstream tasks such as cohort identification, disease monitoring, and clinical decision support.", "date": "2026-04-12", "categories": []}
{"id": "2605.28965", "title": "Frontier LLM-based agents can overcome the ontology curation bottleneck for natural phenotypes", "abstract": "Linking free-text phenotype descriptions to ontology terms, typically referred to as phenotype annotation, is essential for the cross-study integration of comparative morphological data. This labor intensive process has heavily relied on highly trained human experts, which makes it challenging to scale and thus a key bottleneck. Dahdul et al. (2018) established a Gold Standard (GS) of Entity-Quality (EQ) annotations across seven phylogenetic studies and used it to evaluate three human curators and the Semantic CharaParser NLP tool with ontology-based semantic similarity metrics; they reported that machine-human consistency was significantly lower than inter-curator (human-human) consistency. Here we revisit that benchmark with five frontier hosted LLMs from Anthropic and OpenAI, each operating as an \"agentic curator\" within a self-contained workspace that supplies the source publication PDF, the same annotation guide used by the original human curators, the four project ontologies (UBERON, PATO, BSPO, GO), and a validation script. Evaluated against the same Gold Standard, every agent fell within the range of inter-curator variability of the three trained human biocurators of the original study; the best performing agents approached but did not reach the best performing human curator. Agents substantially outperformed Semantic CharaParser on all four metrics.", "date": "2026-05-27", "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": "2605.27380", "title": "BioELX: Cross-lingual Biomedical Entity Linking via Alias-based Retrieval and LLM Ranking", "abstract": "Cross-lingual biomedical entity linking (BEL) maps mentions in any language to unique identifiers in a biomedical knowledge base (KB), supporting clinical and biomedical NLP applications. However, expert-annotated training data for BEL are costly, especially for low-resource languages. Moreover, many cross-lingual BEL systems rely on SapBERT-based retrievers trained on predominantly English aliases in the KB, leading to poor generalization to unseen non-English mentions and limited context-aware disambiguation. We propose BioELX, a two-stage cross-lingual BEL framework that requires no task-specific annotated training corpora. In Stage~1, we enrich SapBERT training with Wikidata-derived multilingual aliases and use the resulting retriever to improve cross-lingual candidate retrieval. In Stage~2, we perform context-aware disambiguation with a pre-trained LLM ranker that jointly considers the mention context and candidate, eliminating the need for supervised training. Experiments on five benchmarks (XL-BEL, EMEA, Patent, WikiMed-DE, and MedMentions) show that BioELX achieves new state-of-the-art performance. It improves average Recall@1 on XL-BEL by +19.2, with especially large gains for low-resource languages, e.g., +21.6 on Turkish, +22.1 on Korean, +30.8 on Thai, and delivers consistent improvements on EMEA (+6.2), Patent (+5.4), and WikiMed-DE (+12.8). Code and resources will be released upon publication.", "date": "2026-04-09", "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": "2605.28363", "title": "PubMedCausal: A Span-Level Annotated Corpus for Causal Relation Extraction in Biomedical Text", "abstract": "Causal relation extraction (CRE) is central to biomedical text mining, but current resources often conflate causal relations with broader associations, restrict annotation to sentence-level examples, or focus mainly on explicit causal cues. This limits their usefulness for evaluating whether models can recover causal claims as they are actually expressed in biomedical text. We introduce PubMedCausal, a span-level annotated corpus for biomedical CRE built from PubMed abstracts. The corpus contains 30,000 paragraph-level rows, including 3,945 causal rows and 6,491 adjudicated cause--effect pairs. Each causal relation is annotated with full-text cause and effect spans, causality type, and sententiality, enabling evaluation of both causal detection and full-span causal extraction. We benchmark discriminative encoders and open-source generative models across detection and extraction settings. For causal detection, biomedical encoders are strongest, with PubMedBERT reaching an F$_1$ score of 0.7391. For span-level extraction, the best generative baseline is DeepSeek-R1-32B with few-shot prompting, reaching a Cosine Pair F$_1$ of 0.6765. We further test transfer learning by evaluating PubMedCausal-trained encoders on external causal relation datasets, showing that the resource supports cross-dataset evaluation. Our results show that biomedical CRE remains difficult under class imbalance, long causal spans, implicit causality, inter-sentential relations, and prompt sensitivity. Code and Data can be found here: https://github.com/josiahpaul07/PubMedCausal_Exp", "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": "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": "2605.26445", "title": "Curation and Extraction of Drug-Related Entities from Reddit Platform", "abstract": "Physicians learn primarily about illicit drugs from clinical overdose cases, limiting their understanding of real-world usage. Meanwhile, drug users share first-hand experiences online, offering insights into dosage and effects of drugs. To bridge this gap, we introduce ReDose (REddit Drug DOSe and Effect), a dataset of 6,435 Reddit posts on substance use. A board-certified toxicologist primarily annotated both the training and test sets, while two medical science students contributed to the test set, labeling DRUG, DOSE, and EFFECT entities. We benchmarked 6,267 annotations using BERT-based, large language model (LLM)-based, and Retrieval-Augmented Generation (RAG) models. BiomedBERT achieved an F1-score of 0.843 for DRUG, while Llama-3 70B outperformed GPT-4 (F1 = 0.79 vs. 0.72). EFFECT extraction remains challenging, with GPT-4 achieving a recall of 0.41. ReDose captures patient-curated narratives to advance medical data extraction from social media.", "date": "2026-04-08", "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": "2605.24635", "title": "HiMed: Incentivizing Hindi Reasoning in Medical LLMs", "abstract": "Medical large language models hold promise for reducing healthcare disparities, yet Hindi remains severely underrepresented. While medical LLMs excel in high-resource languages, their performance degrades sharply in Hindi, particularly on Indian systems of medicine. We argue that robust cross-lingual medical transfer requires Hindi reasoning. To this end, we introduce HiMed, a Hindi reasoning medical corpus and benchmark suite covering both Western and Indian medicine. We further propose HiMed-8B, a Hindi-form medical reasoning LLM, through the design of decaying scaffolding reward. Extensive experiments demonstrate improvement in Hindi medical reasoning performance and reduction in the English--Hindi accuracy gap. Ablation studies validate the contribution of each training stage and reward component. All data and code are available on GitHub: https://github.com/FreedomIntelligence/HiMed.", "date": "2026-05-23", "categories": []}
{"id": "2605.24755", "title": "Automated Detection and Classification of Delusion-related Content in Naturalistic Audio Diaries Using Multi-Agent Language Models", "abstract": "Speech monologues recorded in naturalistic settings provide opportunities to characterize mental illness phenomenology and detect symptom exacerbation. Large language models (LLMs) offer new possibilities for automating this process, as they require annotated data primarily for evaluation rather than training. In this paper, we present a novel automated, multi-agent LLM pipeline for the fine-grained, multi-label extraction of language suggestive of delusional beliefs, associated affective responses, and behavioral responses from transcripts of naturalistic audio diaries collected from people with moderate persecutory ideation. Evaluating an ensemble of three foundation models, we demonstrate that detailed diagnostic prompt instructions successfully reduce false positives for delusional theme classification, but also constrain the interpretation of affective or behavioral responses. Furthermore, comparing multi-agent adjudication frameworks shows that complex conversational debate between agents diminishes accuracy on clinically ambiguous text by inducing premature consensus. Instead, majority voting establishes robust performance (Micro F1 of 0.872 and 0.779 for delusion detection and classification respectively). This work provides a validated and scalable pipeline for the automated detection and characterization of content suggesting delusional beliefs in naturalistic speech.", "date": "2026-05-23", "categories": []}
{"id": "2605.25020", "title": "Privacy-Preserving Local Language Models for Longitudinal Data Retrieval in Chronic Dermatologic Disease: Implementation in Pemphigus Patients", "abstract": "Chronic dermatologic diseases such as pemphigus require long-term follow-up, generating extensive longitudinal clinical documentation that is difficult to review comprehensively during routine visits and increasing clinician workload as well as the risk of missing critical historical information. We evaluated whether a locally deployed, privacy-preserving small language model (SLM) could retrieve structured clinical features and generate longitudinal summaries from long-term dermatology follow-up records. In this retrospective case series, thirty pemphigus patients contributed 541 visit notes that were aggregated into full longitudinal records (89,336 words); 56 clinically relevant features were annotated by two expert dermatologists. The locally deployed SLM (Qwen3 4B Thinking 2507) was queried with each complete record to retrieve 56 features and generate one final report summaries. Across 1,680 feature retrieval tasks, mean accuracy was 82.25%. Dermatologists' ratings of AI-generated summaries were high for overall quality (8.23-8.47), clinical accuracy (7.93-8.20), and usefulness (8.47-8.50), with no significant inter-evaluator differences and an overall preference for AI summaries in 53.3% of evaluations. These findings suggest that privacy-preserving, locally deployed SLMs can outperform medical experts and reliably generate clinically meaningful longitudinal summaries. SLMs may support clinical decision-making when integrated with appropriate oversight.", "date": "2026-05-24", "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": "2605.22501", "title": "BeLink: Biomedical Entity Linking Meets Generative Re-Ranking", "abstract": "Despite recent progress, Biomedical Entity Linking (BEL) with large language models (LLMs) remains computationally inefficient and challenging to deploy in practical settings. In this work, we demonstrate that instruction-tuning of open-source generative models can offer an effective solution when applied at the re-ranking stage of the BEL pipeline. We propose a set-wise instruction-tuning formulation that enables fast and accurate candidate selection. Our method demonstrates strong performance on multiple BEL benchmarks, yielding significant improvements in linking accuracy (3%-24%) while reducing inference time compared to the state-of-the-art. We integrate our generative re-ranker into BeLink, a modular, end-to-end system designed for practical real-world BEL applications.", "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": "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": "2605.20537", "title": "What Do Biomedical NER and Entity Linking Benchmarks Measure? A Corpus-Centric Diagnostic Framework", "abstract": "Biomedical named entity recognition (NER) and entity linking (EL) strongly depend on annotated corpora, but the utility of these resources for benchmarking is often assumed rather than characterized. We present a corpus-centric framework for diagnosing benchmark-relevant properties directly from corpus annotations, concept links, train-test splits, document metadata, and terminology mappings. The framework organizes standardized statistics into five families: (1) scale, density and label distribution, (2) lexical and conceptual structure, (3) train-test overlap, (4) metadata composition, and (5) terminology coverage where applicable. Applying the framework to nine corpora spanning diseases, chemicals, and cell types, we find that corpus properties can differ substantially, even when they address the same apparent task. We find differences in the evaluation signal they provide, the generalization demands they impose, the degree of train-test reuse they permit, and the regions of biomedical literature and concept space they represent. These differences suggest that commonly reported corpus statistics can be insufficient to characterize what biomedical NER and EL benchmarks evaluate. We argue that corpus-centric diagnostics provide a practical framework for analyzing corpora beyond surface descriptors such as corpus size and entity type, for identifying potential transfer risks, and for interpreting the scope of benchmarking conclusions. We release the framework as open-source code with an interactive dashboard to support reproducing our analyses and characterizing additional corpora.", "date": "2026-05-19", "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": "2507.20917", "title": "MediQAl: A French Medical Question Answering Dataset for Knowledge and Reasoning Evaluation", "abstract": "This work introduces MediQAl, a French medical question answering dataset designed to evaluate the capabilities of language models in factual medical recall and reasoning over real-world clinical scenarios. MediQAl contains 32,603 questions sourced from French medical examinations across 41 medical subjects. The dataset includes three tasks: (i) Multiple-Choice Question with Unique answer, (ii) Multiple-Choice Question with Multiple answer, and (iii) Open-Ended Question with Short-Answer. Each question is labeled as Understanding or Reasoning, enabling a detailed analysis of models' cognitive capabilities. We validate the MediQAl dataset through extensive evaluation with 14 large language models, including recent reasoning-augmented models, and observe a significant performance gap between factual recall and reasoning tasks. Our evaluation provides a comprehensive benchmark for assessing language models' performance on French medical question answering, addressing a crucial gap in multilingual resources for the medical domain.", "date": "2025-07-28", "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": "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": "2605.17435", "title": "BELIEF: Structured Evidence Modeling and Uncertainty-Aware Fusion for Biomedical Question Answering", "abstract": "Biomedical question answering often requires decisions from retrieved literature whose relevance, quality, and support for candidate answers are uneven. Most retrieval-augmented large language model (LLM) methods feed this literature to the model as flat text, leaving evidence reliability and remaining uncertainty largely implicit. We propose BELIEF, a structured evidence modeling and uncertainty-aware fusion framework for closed-set biomedical question answering. Rather than treating retrieved documents as undifferentiated context, BELIEF converts them into evidence objects that record clinical attributes, source quality, question relevance, support strength, and the associated candidate hypothesis. These evidence objects provide a shared basis for two complementary reasoning paths. The symbolic path constructs reliability-weighted basic probability assignments based on Dempster--Shafer (D-S) theory over a finite answer space and performs uncertainty-aware symbolic evidence fusion to estimate belief and residual uncertainty. The neural path uses the same structured evidence for LLM-based semantic inference, while a reliability-aware arbitration module reconciles the symbolic and neural outputs according to belief strength, uncertainty, evidence reliability, and semantic consistency. Experiments on PubMedQA, MedQA, and MedMCQA with five general-purpose LLM backbones show that BELIEF obtains the best result in 25 of 30 backbone--dataset--metric settings. Comparisons with biomedical-domain models indicate that BELIEF is competitive on MedQA and MedMCQA, while specialized biomedical pretraining remains advantageous on PubMedQA. Ablation, complementarity, uncertainty-stratified, and cost analyses further show that BELIEF improves retrieved-evidence utilization by making evidence structure, path disagreement, and decision uncertainty explicit.", "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": "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": "2605.15168", "title": "Text Knows What, Tables Know When: Clinical Timeline Reconstruction via Retrieval-Augmented Multimodal Alignment", "abstract": "Reconstructing precise clinical timelines is essential for modeling patient trajectories and forecasting risk in complex, heterogeneous conditions like sepsis. While unstructured clinical narratives offer semantically rich and contextually complete descriptions of a patient's course, they often lack temporal precision and contain ambiguous event timing. Conversely, structured electronic health record (EHR) data provides precise temporal anchors but misses a substantial portion of clinically meaningful events. We introduce a retrieval-augmented multimodal alignment framework that bridges this gap to improve the temporal precision of absolute clinical timelines extracted from text. Our approach formulates timeline reconstruction as a graph-based multistep process: it first extracts central anchor events from narratives to build an initial temporal scaffold, places non-central events relative to this backbone, and then calibrates the timeline using retrieved structured EHR rows as external temporal evidence. Evaluated using instruction-tuned large language models on the i2m4 benchmark spanning MIMIC-III and MIMIC-IV, our multimodal pipeline consistently improves absolute timestamp accuracy (AULTC) and improves temporal concordance across nearly all evaluated models over unimodal text-only reconstruction, without compromising event match rates. Furthermore, our empirical gap analysis reveals that 34.8% of text-derived events are entirely absent from tabular records, demonstrating that aligning these modalities can produce a more temporally faithful and clinically informative reconstruction of patient trajectories than either source alone.", "date": "2026-05-14", "categories": []}
{"id": "2505.04152", "title": "SocialLM: Social Signal Processing of Patient-Provider Communication using LLMs and Contextual Aggregation", "abstract": "Effective patient-provider communication is difficult to assess at scale. We examine whether large language models (LLMs) can track 20 social behaviors from clinical transcripts without fine-tuning. Across three model families and multiple prompting strategies, LLMs reliably detect social signals, though performance varies by patient race and visit segment. To address this variability under query-only API constraints, we introduce an agreement-weighted ensemble using group-level agreement patterns. This approach improves both accuracy and stability over the best individual model, demonstrating a practical pathway for scalable social signal tracking in clinical conversations.", "date": "2025-05-07", "categories": []}
{"id": "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": "2605.13451", "title": "LongBEL: Long-Context and Document-Consistent Biomedical Entity Linking", "abstract": "Biomedical entity linking maps textual mentions to concepts in structured knowledge bases such as UMLS or SNOMED CT. Most existing systems link each mention independently, using only the mention or its surrounding sentence. This ignores dependencies between mentions in the same document and can lead to inconsistent predictions, especially when the same concept appears under different surface forms. We introduce LongBEL, a document-level generative framework that combines full-document context with a memory of previous predictions. To make this memory robust, LongBEL is trained with cross-validated predictions rather than gold labels, reducing the mismatch between training and inference and limiting cascading errors. Experiments on five biomedical benchmarks across English, French, and Spanish show that LongBEL improves over sentence-level generative baselines, with the largest gains on datasets where concepts frequently recur within documents. An ensemble of local, global, and memory-based variants achieves the best results across all benchmarks. Further analysis shows that the largest gains occur on recurring concepts, suggesting that LongBEL mainly improves document-level consistency rather than isolated mention disambiguation.", "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": "2605.10032", "title": "PlantMarkerBench: A Multi-Species Benchmark for Evidence-Grounded Plant Marker Reasoning", "abstract": "Cell-type-specific marker genes are fundamental to plant biology, yet existing resources primarily rely on curated databases or high-throughput studies without explicitly modeling the supporting evidence found in scientific literature. We introduce PlantMarkerBench, a multi-species benchmark for evaluating literature-grounded plant marker evidence interpretation from full-text biological papers. PlantMarkerBench is constructed using a modular curation pipeline integrating large-scale literature retrieval, hybrid search, species-aware biological grounding, structured evidence extraction, and targeted human review. The benchmark spans four plant species -- Arabidopsis, maize, rice, and tomato -- and contains 5,550 sentence-level evidence instances annotated for marker-evidence validity, evidence type, and support strength. We define two benchmark tasks: determining whether a candidate sentence provides valid marker evidence for a gene-cell-type pair, and classifying the evidence into expression, localization, function, indirect, or negative categories. We benchmark diverse open-weight and closed-source language models across species and prompting strategies. Although frontier models achieve relatively strong performance on direct expression evidence, performance drops substantially on functional, indirect, and weak-support evidence, with evidence-type confusion emerging as a dominant failure mode. Open-weight models additionally exhibit elevated false-positive rates under ambiguous biological contexts. PlantMarkerBench provides a challenging and reproducible evaluation framework for literature-grounded biological evidence attribution and supports future research on trustworthy scientific information extraction and AI-assisted plant biology.", "date": "2026-05-11", "categories": []}
{"id": "2605.11143", "title": "ClinicalBench: Stress-Testing Assertion-Aware Retrieval for Cross-Admission Clinical QA on MIMIC-IV", "abstract": "Reasoning benchmarks measure clinical performance on clean inputs. We evaluate the step before reasoning: retrieval over real EHR notes, where negation, temporality, and family-versus-patient attribution can flip a correct answer to a wrong one. EpiKG carries an assertion label and a temporality tag with every fact in a patient knowledge graph, then routes retrieval by question intent. ClinicalBench is a 400-question test over 43 MIMIC-IV patients across 9 assertion-sensitive categories. A 7-condition ablation tests each piece of EpiKG across six LLMs (Claude Opus 4.6, GPT-OSS 20B, MedGemma 27B, Gemma 4 31B, MedGemma 1.5 4B, Qwen 3.5 35B). Three physicians blindly adjudicated 100 paired items. The author-blind primary endpoint, leave-author-out paired exact McNemar on 50 unanimous-strict items rated by two external physicians, yields +22.0 percentage points (95 percent Newcombe CI [+5.1, +31.5], p=0.0192). The architectural novelty, intent-aware KG-RAG over a Contriever dense-RAG baseline (C2b to C4g_kw on the change-excluded n=362 endpoint), is +8.84 percentage points (paired McNemar p=1.79e-3); +12.43 percentage points under oracle intent. Sensitivities agree directionally: three-rater physician majority +24.0 percentage points (subject to single-author circularity); deterministic keyword reproducibility proxy +39.5 percentage points. Across the six models, the gain shrinks as the LLM-alone baseline rises (beta=-1.123, r=-0.921, p=0.009). With n=6 this looks more like regression to the mean than encoding substituting for model size. Physician adjudication identified 56 percent of auto-generated reference answers as defective, a methodological finding indicating that NLP-pipeline clinical-QA benchmarks require physician adjudication to be usable. ClinicalBench, the frozen evaluator, three-rater adjudication data, and the EpiKG output stack are publicly released.", "date": "2026-05-11", "categories": []}
{"id": "2605.11398", "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": "2605.12313", "title": "Overview of the MedHopQA track at BioCreative IX: track description, participation and evaluation of systems for multi-hop medical question answering", "abstract": "Multi-hop question answering (QA) remains a significant challenge in the biomedical domain, requiring systems to integrate information across multiple sources to answer complex questions. To address this problem, the BioCreative IX MedHopQA shared task was designed to benchmark in multi-hop reasoning for large language models (LLMs). We developed a novel dataset of 1,000 challenging QA pairs spanning diseases, genes, and chemicals, with particular emphasis on rare diseases. Each question was constructed to require two-hop reasoning through the integration of information from two distinct Wikipedia pages. The challenge attracted 48 submissions from 13 teams. Systems were evaluated using both surface string comparison and conceptual accuracy (MedCPT score). The results showed a substantial performance gap between baseline LLMs and enhanced systems. The top-ranked submission achieved an 89.30% F1 score on the MedCPT metric and an 87.30% exact match (EM) score, compared with 67.40% and 60.20%, respectively, for the zero-shot baseline. A central finding of the challenge was that retrieval-augmented generation (RAG) and related retrieval-based strategies were critical for strong performance. In addition, concept-level evaluation improved answer assessment when correct responses differed in surface form. The MedHopQA dataset is publicly available to support continued progress in this important area. Challenge materials: https://www.ncbi.nlm.nih.gov/research/bionlp/medhopqa and benchmark https://www.codabench.org/competitions/7609/", "date": "2026-05-12", "categories": []}
{"id": "2605.12361", "title": "MedHopQA: A Disease-Centered Multi-Hop Reasoning Benchmark and Evaluation Framework for LLM-Based Biomedical Question Answering", "abstract": "Evaluating large language models (LLMs) in the biomedical domain requires benchmarks that can distinguish reasoning from pattern matching and remain discriminative as model capabilities improve. Existing biomedical question answering (QA) benchmarks are limited in this respect. Multiple-choice formats can allow models to succeed through answer elimination rather than inference, while widely circulated exam-style datasets are increasingly vulnerable to performance saturation and training data contamination. Multi-hop reasoning, defined as the ability to integrate information across multiple sources to derive an answer, is central to clinically meaningful tasks such as diagnostic support, literature-based discovery, and hypothesis generation, yet remains underrepresented in current biomedical QA benchmarks. MedHopQA is a disease-centered multi-hop reasoning benchmark consisting of 1,000 expert-curated question-answer pairs introduced as a shared task at BioCreative IX. Each question requires synthesis of information across two distinct Wikipedia articles, and answers are provided in an open-ended free-text format. Gold annotations are augmented with ontology-grounded synonym sets from MONDO, NCBI Gene, and NCBI Taxonomy to support both lexical and concept-level evaluation. MedHopQA was constructed through a structured process combining human annotation, triage, iterative verification, and LLM-as-a-judge validation. To reduce leaderboard gaming and contamination risk, the 1,000 scored questions are embedded within a publicly downloadable set of 10,000 questions, with answers withheld, on a CodaBench leaderboard. MedHopQA provides both a benchmark and a reusable framework for constructing future biomedical QA datasets that prioritize compositional reasoning, saturation resistance, and contamination resistance as core design constraints.", "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": "2605.10025", "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": "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": "2605.05758", "title": "BioTool: A Comprehensive Tool-Calling Dataset for Enhancing Biomedical Capabilities of Large Language Models", "abstract": "Despite the success of large language models (LLMs) on general-purpose tasks, their performance in highly specialized domains such as biomedicine remains unsatisfactory. A key limitation is the inability of LLMs to effectively leverage biomedical tools, which clinical experts and biomedical researchers rely on extensively in daily workflows. While recent general-domain tool-calling datasets have substantially improved the capabilities of LLM agents, existing efforts in the biomedical domain largely rely on in-context learning and restrict models to a small set of tools. To address this gap, we introduce BioTool, a comprehensive biomedical tool-calling dataset designed for fine-tuning LLMs. BioTool comprises 34 frequently used tools collected from the NCBI, Ensembl, and UniProt databases, along with 7,040 high-quality, human-verified query-API call pairs spanning variation, genomics, proteomics, evolution, and general biology. Fine-tuning a 4-billion-parameter LLM on BioTool yields substantial improvements in biomedical tool-calling performance, outperforming cutting-edge commercial LLMs such as GPT-5.1. Furthermore, human expert evaluations demonstrate that integrating a BioTool-fine-tuned tool caller significantly improves downstream answer quality compared to the same LLM without tool usage, highlighting the effectiveness of BioTool in enhancing the biomedical capabilities of LLMs. The full dataset and evaluation code are available at https://github.com/gxx27/BioTool", "date": "2026-05-07", "categories": []}
{"id": "2605.06177", "title": "BioMedArena: An Open-source Toolkit for Building and Evaluating Biomedical Deep Research Agents", "abstract": "Building a deep research agent today is an exercise in glue code: the same backbone evaluated on the same benchmark can report different accuracies in different papers because harness and tool registry all differ, and integrating a new foundation model into a comparable evaluation surface costs weeks of model-specific engineering. We call this the per-paper engineering tax and release BioMedArena, an open-source toolkit that not only alleviates it but also provides an arena for fair comparison of different foundation models when evaluating them as deep-research agents. BioMedArena decouples six layers of biomedical agent evaluation -- benchmark loading, tool exposure, tool selection, execution mode, context management, and scoring -- and exposes 147 biomedical benchmarks and 75 biomedical tools across 9 functional families. Adding a new model, benchmark, or tool reduces to registering a few-line provider adapter. We further provide 6 agent harnesses with 6 context-management strategies, which provide 12 backbones with competitive research capabilities and significantly improved performance, achieving state-of-the-art (SOTA) results on 8 representative biomedical benchmarks, with an average lift of +15.03 percentage points over prior SOTA. The toolkit, configurations, and per-task traces are available at https://github.com/AI-in-Health/BioMedArena", "date": "2026-05-07", "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": "2511.05080", "title": "Making Knowledge Accessible: Divergent Readability-Accuracy Strategies of Mistral and QWen in Biomedical Text Simplification", "abstract": "The growing public demand for accessible biomedical information calls for scalable text simplification. While large language models (LLMs) offer solutions, they too struggle with balancing improved readability against preservation of meaning. This report empirically compares how two LLMs - instruction-tuned Mistral-Small 3 24B and the reasoning-augmented QWen2.5 32B- navigate this trade-off in biomedical text simplification, benchmarked against human performance. Our analysis highlights how each model applies distinct operational strategies when simplifying biomedical text. Mistral exhibits a tempered lexical simplification approach that consistently enhances readability across multiple metrics while preserving discourse fidelity (BERTScore: 0.91, statistically comparable to that of humans). In comparison, QWen also attains enhanced readability performance and a reasonable BERTScore of 0.89, but presents a disconnect in balancing between readability and accuracy. Additionally, a comprehensive correlation analysis of a suite of 21 metrics confirms strong functional redundancies in metrics and informs adaptation requirements.", "date": "2025-11-07", "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": "2605.00421", "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": "2605.01417", "title": "Medmarks: A Comprehensive Open-Source LLM Benchmark Suite for Medical Tasks", "abstract": "Evaluating large language models (LLMs) for medical applications remains challenging due to benchmark saturation, limited data accessibility, and insufficient coverage of relevant tasks. Existing suites have either saturated, heavily depend on restricted datasets, or lack comprehensive model coverage. We introduce Medmarks, a fully open-source evaluation suite with 30 benchmarks spanning question answering, information extraction, medical calculations, and open-ended clinical reasoning. We perform a systematic evaluation of 61 models across 71 configurations using verifiable metrics and LLM-as-a-Judge. Our results show that frontier reasoning models (Gemini 3 Pro Preview, GPT-5.1, & GPT-5.2) achieve the highest performance across both benchmarks, most frontier proprietary models are significantly more token efficient than open-weight alternatives, medically fine-tuned models outperform their generalist counterparts, and that models are susceptible to answer-order bias (particularly smaller models and Grok 4). A subset of our evals (Medmarks-T) can be directly used as reinforcement learning environments to post-train LLMs for medical reasoning. Code is available at https://github.com/MedARC-AI/Medmarks", "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": "2604.27470", "title": "HealthBench Professional: Evaluating Large Language Models on Real Clinician Chats", "abstract": "Millions of clinicians use ChatGPT to support clinical care, but evaluations of the most common use cases in model-clinician conversations are limited. We introduce HealthBench Professional, an open benchmark for evaluating large language models on real tasks that clinicians bring to ChatGPT in the course of their work. The benchmark is organized around three common use cases central to clinical practice: care consult, writing and documentation, and medical research. Each example includes a physician-authored conversation with ChatGPT for Clinicians and is scored via rubrics written and iteratively adjudicated by three or more physicians across three phases. HealthBench Professional examples were carefully selected for quality, representativeness, and difficulty for OpenAI's current frontier models, to enable continued measurement of progress. Difficult examples for recent OpenAI models were enriched by roughly 3.5 times relative to the candidate pool of 15,079 examples. Additionally, about one-third of examples involve physicians conducting deliberate adversarial testing of models. As a strong baseline, we also collected human physician responses for all tasks (unbounded time, specialist-matched, web access). The best scoring system, GPT-5.4 in ChatGPT for Clinicians, outperforms base GPT-5.4, all other models, and human physicians. We hope HealthBench Professional provides the healthcare AI community a measure to track frontier model progress in real-world clinical tasks and build systems that clinicians can trust to improve care.", "date": "2026-04-30", "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": "2604.25920", "title": "Analysing Lightweight Large Language Models for Biomedical Named Entity Recognition on Diverse Ouput Formats", "abstract": "Despite their strong linguistic capabilities, Large Language Models (LLMs) are computationally demanding and require substantial resources for fine-tuning, which is unadapted to privacy and budget constraints of many healthcare settings. To address this, we present an experimental analysis focused on Biomedical Named Entity Recognition using lightweight LLMs, we evaluate the impact of different output formats on model performance. The results reveal that lightweight LLMs can achieve competitive performance compared to the larger models, highlighting their potential as lightweight yet effective alternatives for biomedical information extraction. Our analysis shows that instruction tuning over many distinct formats does not improve performance, but identifies several format consistently associated with better performance.", "date": "2026-03-27", "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": "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": "2604.23054", "title": "DeepImagine: Learning Biomedical Reasoning via Successive Counterfactual Imagining", "abstract": "Predicting the outcomes of prospective clinical trials remains a major challenge for large language models. Prior work has shown that both traditional correlational predictors, such as random forests and logistic regression, and strong commercial LLMs achieve limited performance on this task. In this paper, we propose DeepImagine, a framework for teaching LLMs biomedical reasoning through successive counterfactual imagining. The central idea is to approximate hidden causal mechanisms of clinical trials by training models to infer how observed trial results would change under controlled perturbations of experimental conditions, such as dosage, outcome measures, study arms, geography, and other trial attributes. To support this objective, we construct both natural and approximate counterfactual pairs from real clinical trials with reported outcomes. For settings where strict counterfactual supervision is available, such as paired outcome measures or dose-ranging study arms within the same trial, we train models with supervised fine-tuning. For broader settings where only approximate counterfactual pairs can be retrieved, we optimize models with reinforcement learning using verifiable rewards based on downstream benchmark correctness. We further augment training with synthetic reasoning traces that provide causally plausible explanations for local counterfactual transitions. Using this pipeline, we train language models under 10B parameters, including Qwen3.5-9B, and evaluate them on clinical trial outcome prediction. We aim to show that DeepImagine consistently improves over untuned language models and traditional correlational baselines. Finally, we aim to show that the learned reasoning trajectories provide interpretable signals about how models represent trial-level mechanisms, suggesting a practical path toward more mechanistic and scientifically useful biomedical language models.", "date": "2026-04-24", "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": "2604.24376", "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": "2604.21076", "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": "2604.21508", "title": "BioMiner: A Multi-modal System for Automated Mining of Protein-Ligand Bioactivity Data from Literature", "abstract": "Protein-ligand bioactivity data published in the literature are essential for drug discovery, yet manual curation struggles to keep pace with rapidly growing literature. Automated bioactivity extraction remains challenging because it requires not only interpreting biochemical semantics distributed across text, tables, and figures, but also reconstructing chemically exact ligand structures (e.g., Markush structures). To address this bottleneck, we introduce BioMiner, a multi-modal extraction framework that explicitly separates bioactivity semantic interpretation from ligand structure construction. Within BioMiner, bioactivity semantics are inferred through direct reasoning, while chemical structures are resolved via a chemical-structure-grounded visual semantic reasoning paradigm, in which multi-modal large language models operate on chemically grounded visual representations to infer inter-structure relationships, and exact molecular construction is delegated to domain chemistry tools. For rigorous evaluation and method development, we further establish BioVista, a comprehensive benchmark comprising 16,457 bioactivity entries curated from 500 publications. BioMiner validates its extraction ability and provides a quantitative baseline, achieving an F1 score of 0.32 for bioactivity triplets. BioMiner's practical utility is demonstrated via three applications: (1) extracting 82,262 data from 11,683 papers to build a pre-training database that improves downstream models performance by 3.9%; (2) enabling a human-in-the-loop workflow that doubles the number of high-quality NLRP3 bioactivity data, helping 38.6% improvement over 28 QSAR models and identification of 16 hit candidates with novel scaffolds; and (3) accelerating protein-ligand complex bioactivity annotation, achieving a 5.59-fold speed increase and 5.75% accuracy improvement over manual workflows in PoseBusters dataset.", "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": "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": "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": "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": "2604.16364", "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": "2604.16422", "title": "Injecting Structured Biomedical Knowledge into Language Models: Continual Pretraining vs. GraphRAG", "abstract": "The injection of domain-specific knowledge is crucial for adapting language models (LMs) to specialized fields such as biomedicine. While most current approaches rely on unstructured text corpora, this study explores two complementary strategies for leveraging structured knowledge from the UMLS Metathesaurus: (i) Continual pretraining that embeds knowledge into model parameters, and (ii) Graph Retrieval-Augmented Generation (GraphRAG) that consults a knowledge graph at inference time. We first construct a large-scale biomedical knowledge graph from UMLS (3.4 million concepts and 34.2 million relations), stored in Neo4j for efficient querying. We then derive a ~100-million-token textual corpus from this graph to continually pretrain two models: BERTUMLS (from BERT) and BioBERTUMLS (from BioBERT). We evaluate these models on six BLURB (Biomedical Language Understanding and Reasoning Benchmark) datasets spanning five task types and evaluate GraphRAG on the two QA (Question Answering) datasets (PubMedQA, BioASQ). On BLURB tasks, BERTUMLS improves over BERT, with the largest gains on knowledge-intensive QA. Effects on BioBERT are more nuanced, suggesting diminishing returns when the base model already encodes substantial biomedical text knowledge. Finally, augmenting LLaMA 3-8B with our GraphRAG pipeline yields over than 3 points accuracy on PubMedQA and 5 points on BioASQ without any retraining, delivering transparent, multi-hop, and easily updated knowledge access. We release the processed UMLS Neo4j graph to support reproducibility.", "date": "2026-04-03", "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": "2604.17988", "title": "Employing General-Purpose and Biomedical Large Language Models with Advanced Prompt Engineering for Pharmacoepidemiologic Study Design", "abstract": "Background: The potential of large language models (LLMs) to automate and support pharmacoepidemiologic study design is an emerging area of interest, yet their reliability remains insufficiently characterized. General-purpose LLMs often display inaccuracies, while the comparative performance of specialized biomedical LLMs in this domain remains unknown. Methods: This study evaluated general-purpose LLMs (GPT-4o and DeepSeek-R1) versus biomedically fine-tuned LLMs (QuantFactory/Bio-Medical-Llama-3-8B-GGUF and Irathernotsay/qwen2-1.5B-medical_qa-Finetune) using 46 protocols (2018-2024) from the HMA-EMA Catalogue and Sentinel System. Performance was assessed across relevance, logic of justification, and ontology-code agreement across multiple coding systems using Least-to-Most (LTM) and Active Prompting strategies. Results: GPT-4o and DeepSeek-R1 paired with LTM prompting achieved the highest relevance and logic of justification scores, with GPT-4o-LTM reaching a median relevance score of 4 in 8 of 9 questions for HMA-EMA protocols. Biomedical LLMs showed lower relevance overall and frequently generated insufficient justification. All LLMs demonstrated limited proficiency in ontology-code mapping, although LTM provided the most consistent improvements in reasoning stability. Conclusion: Off-the-shelf general-purpose LLMs currently offer superior support for pharmacoepidemiologic design compared to biomedical LLMs. Prompt strategy strongly influenced LLM performance.", "date": "2026-04-20", "categories": []}
{"id": "2604.15591", "title": "BioHiCL: Hierarchical Multi-Label Contrastive Learning for Biomedical Retrieval with MeSH Labels", "abstract": "Effective biomedical information retrieval requires modeling domain semantics and hierarchical relationships among biomedical texts. Existing biomedical generative retrievers build on coarse binary relevance signals, limiting their ability to capture semantic overlap. We propose BioHiCL (Biomedical Retrieval with Hierarchical Multi-Label Contrastive Learning), which leverages hierarchical MeSH annotations to provide structured supervision for multi-label contrastive learning. Our models, BioHiCL-Base (0.1B) and BioHiCL-Large (0.3B), achieve promising performance on biomedical retrieval, sentence similarity, and question answering tasks, while remaining computationally efficient for deployment.", "date": "2026-04-17", "categories": []}
{"id": "2412.14751", "title": "Query pipeline optimization for cancer patient question answering systems", "abstract": "Retrieval-augmented generation (RAG) mitigates hallucination in Large Language Models (LLMs) by using query pipelines to retrieve relevant external information and grounding responses in retrieved knowledge. However, query pipeline optimization for cancer patient question-answering (CPQA) systems requires separately optimizing multiple components with domain-specific considerations. We propose a novel three-aspect optimization approach for the RAG query pipeline in CPQA systems, utilizing public biomedical databases like PubMed and PubMed Central. Our optimization includes: (1) document retrieval, utilizing a comparative analysis of NCBI resources and introducing Hybrid Semantic Real-time Document Retrieval (HSRDR); (2) passage retrieval, identifying optimal pairings of dense retrievers and rerankers; and (3) semantic representation, introducing Semantic Enhanced Overlap Segmentation (SEOS) for improved contextual understanding. On a custom-developed dataset tailored for cancer-related inquiries, our optimized RAG approach improved the answer accuracy of Claude-3-haiku by 5.24% over chain-of-thought prompting and about 3% over a naive RAG setup. This study highlights the importance of domain-specific query optimization in realizing the full potential of RAG and provides a robust framework for building more accurate and reliable CPQA systems, advancing the development of RAG-based biomedical systems.", "date": "2024-12-19", "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": "2604.14175", "title": "QU-NLP at ArchEHR-QA 2026: Two-Stage QLoRA Fine-Tuning of Qwen3-4B for Patient-Oriented Clinical Question Answering and Evidence Sentence Alignment", "abstract": "We present a unified system addressing both Subtask 3 (answer generation) and Subtask 4 (evidence sentence alignment) of the ArchEHR-QA Shared Task. For Subtask 3, we apply two-stage Quantised Low-Rank Adaptation (QLoRA) to Qwen3-4B loaded in 4-bit NF4 quantisation: first on 30,000 samples from the emrQA-MedSQuAD corpus to establish clinical domain competence, then on the 20 annotated development cases to learn the task-specific output style. Our system achieves an overall score of 32.87 on the official test-2026 split (BLEU = 9.42, ROUGE-L = 27.04, SARI = 55.42, BERTScore = 43.00, AlignScore = 25.28, MEDCON = 37.04). For Subtask 4, we develop a weighted ensemble of three retrieval methods - BM25 with relative thresholding, TF-IDF cosine similarity, and a fine-tuned cross-encoder - to identify note sentences supporting a given gold answer, achieving a micro-F1 of 67.16 on the 100-case test set. Experiments reveal that both subtasks expose the same fundamental challenge: 20 annotated training cases are insufficient to distinguish relevant from irrelevant clinical sentences, pointing to data augmentation as the highest-leverage future direction.", "date": "2026-03-26", "categories": []}
{"id": "2604.14815", "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": "2604.14829", "title": "Beyond Literal Summarization: Redefining Hallucination for Medical SOAP Note Evaluation", "abstract": "Evaluating large language models (LLMs) for clinical documentation tasks such as SOAP note generation remains challenging. Unlike standard summarization, these tasks require clinical abstraction, normalization of colloquial language, and medically grounded inference. However, prevailing evaluation methods including automated metrics and LLM as judge frameworks rely on lexical faithfulness, often labeling any information not explicitly present in the transcript as hallucination.\n  We show that such approaches systematically misclassify clinically valid outputs as errors, inflating hallucination rates and distorting model assessment. Our analysis reveals that many flagged hallucinations correspond to legitimate clinical transformations, including synonym mapping, abstraction of examination findings, diagnostic inference, and guideline consistent care planning.\n  By aligning evaluation criteria with clinical reasoning through calibrated prompting and retrieval grounded in medical ontologies we observe a significant shift in outcomes. Under a lexical evaluation regime, the mean hallucination rate is 35%, heavily penalizing valid reasoning. With inference aware evaluation, this drops to 9%, with remaining cases reflecting genuine safety concerns. These findings suggest that current evaluation practices over penalize valid clinical reasoning and may measure artifacts of evaluation design rather than true errors, underscoring the need for clinically informed evaluation in high context domains like medicine.", "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": "2604.13059", "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": "2604.13328", "title": "Multi-Task LLM with LoRA Fine-Tuning for Automated Cancer Staging and Biomarker Extraction", "abstract": "Pathology reports serve as the definitive record for breast cancer staging, yet their unstructured format impedes large-scale data curation. While Large Language Models (LLMs) offer semantic reasoning, their deployment is often limited by high computational costs and hallucination risks. This study introduces a parameter-efficient, multi-task framework for automating the extraction of Tumor-Node-Metastasis (TNM) staging, histologic grade, and biomarkers. We fine-tune a Llama-3-8B-Instruct encoder using Low-Rank Adaptation (LoRA) on a curated, expert-verified dataset of 10,677 reports. Unlike generative approaches, our architecture utilizes parallel classification heads to enforce consistent schema adherence. Experimental results demonstrate that the model achieves a Macro F1 score of 0.976, successfully resolving complex contextual ambiguities and heterogeneous reporting formats that challenge traditional extraction methods including rule-based natural language processing (NLP) pipelines, zero-shot LLMs, and single-task LLM baselines. The proposed adapter-efficient, multi-task architecture enables reliable, scalable pathology-derived cancer staging and biomarker profiling, with the potential to enhance clinical decision support and accelerate data-driven oncology research.", "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": "2604.06214", "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": "2604.10316", "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": "2603.19275", "title": "Improving Automatic Summarization of Radiology Reports through Mid-Training of Large Language Models", "abstract": "Automatic summarization of radiology reports is an essential application to reduce the burden on physicians. Previous studies have widely used the \"pre-training, fine-tuning\" strategy to adapt large language models (LLMs) for summarization. This study proposed a subdomain adaptation through a mid-training method to improve summarization. We explored three adaptation strategies: (1) general-domain pre-training, (2) clinical-domain pre-training, and (3) clinical-domain pre-training followed by subdomain mid-training. We developed models using large-scale clinical text from the University of Florida (UF) Health and conducted mid-training and fine-tuning experiments using widely used benchmark datasets including OpenI and MIMIC-CXR. The experimental results show that the mid-trained model, GatorTronT5-Radio, achieved the best performance, outperforming models without mid-training in both text-based measures (ROUGE-L) and factuality measures (RadGraph-F1). Our mid-training methods also demonstrate better few-shot learning and could alleviate the \"cold start\" problem reported in previous studies as a learning barrier. Our findings support the use of \"pre-training, mid-training, fine-tuning,\" instead of the widely used direct fine-tuning strategy.", "date": "2026-02-28", "categories": []}
{"id": "2604.08549", "title": "VerifAI: A Verifiable Open-Source Search Engine for Biomedical Question Answering", "abstract": "We introduce VerifAI, an open-source expert system for biomedical question answering that integrates retrieval-augmented generation (RAG) with a novel post-hoc claim verification mechanism. Unlike standard RAG systems, VerifAI ensures factual consistency by decomposing generated answers into atomic claims and validating them against retrieved evidence using a fine-tuned natural language inference (NLI) engine. The system comprises three modular components: (1) a hybrid Information Retrieval (IR) module optimized for biomedical queries (MAP@10 of 42.7%), (2) a citation-aware Generative Component fine-tuned on a custom dataset to produce referenced answers, and (3) a Verification Component that detects hallucinations with state-of-the-art accuracy, outperforming GPT-4 on the HealthVer benchmark. Evaluations demonstrate that VerifAI significantly reduces hallucinated citations compared to zero-shot baselines and provides a transparent, verifiable lineage for every claim. The full pipeline, including code, models, and datasets, is open-sourced to facilitate reliable AI deployment in high-stakes domains.", "date": "2026-01-16", "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": "2604.06346", "title": "Severity-Aware Weighted Loss for Arabic Medical Text Generation", "abstract": "Large language models have shown strong potential for Arabic medical text generation; however, traditional fine-tuning objectives treat all medical cases uniformly, ignoring differences in clinical severity. This limitation is particularly critical in healthcare settings, where errors in severe cases contain higher clinical risk. In this work, we propose a severity-aware weighted loss for fine-tuning Arabic language models on medical complaint-response data. The method depends on soft severity probabilities to dynamically scale token-level loss contributions during optimization, thereby prioritizing clinically critical interactions without modifying model architectures. Experiments are conducted using the MAQA dataset, which provides Arabic medical complaints and trusted human responses. Severity labels and probabilistic scores are automatically derived using a fine-tuned AraBERT-based classifier and incorporated exclusively at the loss level. The proposed approach is evaluated across ten Arabic large language models of varying architectures and parameter scales. While standard cross-entropy fine-tuning yields only modest improvements, severity-aware optimization consistently achieves larger gains. Using a balanced weighting configuration, performance improves from 54.04% to 66.14% for AraGPT2-Base, from 59.16% to 67.18% for AraGPT2-Medium, and from 57.83% to 66.86% for Qwen2.5-0.5B, with peak performance reaching 67.18%. Overall, severity-aware fine-tuning delivers improvements of up to 12.10% over non-fine-tuned baselines, demonstrating robust and architecture-consistent gains.", "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": "2604.06505", "title": "MedConclusion: A Benchmark for Biomedical Conclusion Generation from Structured Abstracts", "abstract": "Large language models (LLMs) are widely explored for reasoning-intensive research tasks, yet resources for testing whether they can infer scientific conclusions from structured biomedical evidence remain limited. We introduce $\\textbf{MedConclusion}$, a large-scale dataset of $\\textbf{5.7M}$ PubMed structured abstracts for biomedical conclusion generation. Each instance pairs the non-conclusion sections of an abstract with the original author-written conclusion, providing naturally occurring supervision for evidence-to-conclusion reasoning. MedConclusion also includes journal-level metadata such as biomedical category and SJR, enabling subgroup analysis across biomedical domains. As an initial study, we evaluate diverse LLMs under conclusion and summary prompting settings and score outputs with both reference-based metrics and LLM-as-a-judge. We find that conclusion writing is behaviorally distinct from summary writing, strong models remain closely clustered under current automatic metrics, and judge identity can substantially shift absolute scores. MedConclusion provides a reusable data resource for studying scientific evidence-to-conclusion reasoning. Our code and data are available at: https://github.com/Harvard-AI-and-Robotics-Lab/MedConclusion.", "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": "2604.06854", "title": "To Adapt or not to Adapt, Rethinking the Value of Medical Knowledge-Aware Large Language Models", "abstract": "BACKGROUND: Recent studies have shown that domain-adapted large language models (LLMs) do not consistently outperform general-purpose counterparts on standard medical benchmarks, raising questions about the need for specialized clinical adaptation.\n  METHODS: We systematically compare general and clinical LLMs on a diverse set of multiple choice clinical question answering tasks in English and Spanish. We introduce a perturbation based evaluation benchmark that probes model robustness, instruction following, and sensitivity to adversarial variations. Our evaluation includes, one-step and two-step question transformations, multi prompt testing and instruction guided assessment. We analyze a range of state-of-the-art clinical models and their general-purpose counterparts, focusing on Llama 3.1-based models. Additionally, we introduce Marmoka, a family of lightweight 8B-parameter clinical LLMs for English and Spanish, developed via continual domain-adaptive pretraining on medical corpora and instructions.\n  RESULTS: The experiments show that clinical LLMs do not consistently outperform their general purpose counterparts on English clinical tasks, even under the proposed perturbation based benchmark. However, for the Spanish subsets the proposed Marmoka models obtain better results compared to Llama.\n  CONCLUSIONS: Our results show that, under current short-form MCQA benchmarks, clinical LLMs offer only marginal and unstable improvements over general-purpose models in English, suggesting that existing evaluation frameworks may be insufficient to capture genuine medical expertise. We further find that both general and clinical models exhibit substantial limitations in instruction following and strict output formatting. Finally, we demonstrate that robust medical LLMs can be successfully developed for low-resource languages such as Spanish, as evidenced by the Marmoka models.", "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": "2507.19315", "title": "AutoPCR: Automated Phenotype Concept Recognition by Prompting", "abstract": "Motivation: Phenotype concept recognition (CR) is a fundamental task in biomedical text mining. However, existing methods either require ontology-specific training, making them struggle to generalize across diverse text styles and evolving biomedical terminology, or depend on general-purpose large language models (LLMs) that lack necessary domain knowledge. Results: To address these limitations, we propose AutoPCR, a prompt-based phenotype CR method designed to automatically generalize to new ontologies and unseen data without ontology-specific training. To further boost performance, we also introduce an optional self-supervised training strategy. Experiments show that AutoPCR achieves the best average and most robust performance across datasets. Further ablation and transfer studies demonstrate its inductive capability and generalizability to new ontologies. Availability and Implementation: Our code is available at https://github.com/yctao7/AutoPCR. Contact: drjieliu@umich.edu", "date": "2025-07-25", "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": "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": "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": "2504.19467", "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": "2506.04156", "title": "A Dataset for Addressing Patient's Information Needs related to Clinical Course of Hospitalization", "abstract": "Patients have distinct information needs about their hospitalization that can be addressed using clinical evidence from electronic health records (EHRs). While artificial intelligence (AI) systems show promise in meeting these needs, robust datasets are needed to evaluate the factual accuracy and relevance of AI-generated responses. To our knowledge, no existing dataset captures patient information needs in the context of their EHRs. We introduce ArchEHR-QA, an expert-annotated dataset based on real-world patient cases from intensive care unit and emergency department settings. The cases comprise questions posed by patients to public health forums, clinician-interpreted counterparts, relevant clinical note excerpts with sentence-level relevance annotations, and clinician-authored answers. To establish benchmarks for grounded EHR question answering (QA), we evaluated three open-weight large language models (LLMs)--Llama 4, Llama 3, and Mixtral--across three prompting strategies: generating (1) answers with citations to clinical note sentences, (2) answers before citations, and (3) answers from filtered citations. We assessed performance on two dimensions: Factuality (overlap between cited note sentences and ground truth) and Relevance (textual and semantic similarity between system and reference answers). The final dataset contains 134 patient cases. The answer-first prompting approach consistently performed best, with Llama 4 achieving the highest scores. Manual error analysis supported these findings and revealed common issues such as omitted key clinical evidence and contradictory or hallucinated content. Overall, ArchEHR-QA provides a strong benchmark for developing and evaluating patient-centered EHR QA systems, underscoring the need for further progress toward generating factual and relevant responses in clinical contexts.", "date": "2025-06-04", "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": "2511.15355", "title": "HEAD-QA v2: Expanding a Healthcare Benchmark for Reasoning", "abstract": "We introduce HEAD-QA v2, an expanded and updated version of a Spanish/English healthcare multiple-choice reasoning dataset originally released by Vilares and G\\'omez-Rodr\\'iguez (2019). The update responds to the growing need for high-quality datasets that capture the linguistic and conceptual complexity of healthcare reasoning. We extend the dataset to over 12,000 questions from ten years of Spanish professional exams, benchmark several open-source LLMs using prompting, RAG, and probability-based answer selection, and provide additional multilingual versions to support future work. Results indicate that performance is mainly driven by model scale and intrinsic reasoning ability, with complex inference strategies obtaining limited gains. Together, these results establish HEAD-QA v2 as a reliable resource for advancing research on biomedical reasoning and model improvement.", "date": "2025-11-19", "categories": []}
{"id": "2603.13962", "title": "sebis at ArchEHR-QA 2026: How Much Can You Do Locally? Evaluating Grounded EHR QA on a Single Notebook", "abstract": "Clinical question answering over electronic health records (EHRs) can help clinicians and patients access relevant medical information more efficiently. However, many recent approaches rely on large cloud-based models, which are difficult to deploy in clinical environments due to privacy constraints and computational requirements. In this work, we investigate how far grounded EHR question answering can be pushed when restricted to a single notebook. We participate in all four subtasks of the ArchEHR-QA 2026 shared task and evaluate several approaches designed to run on commodity hardware. All experiments are conducted locally without external APIs or cloud infrastructure. Our results show that such systems can achieve competitive performance on the shared task leaderboards. In particular, our submissions perform above average in two subtasks, and we observe that smaller models can approach the performance of much larger systems when properly configured. These findings suggest that privacy-preserving EHR QA systems running fully locally are feasible with current models and commodity hardware. The source code is available at https://github.com/ibrahimey/ArchEHR-QA-2026.", "date": "2026-03-14", "categories": []}
{"id": "2603.27335", "title": "PubMed Reasoner: Dynamic Reasoning-based Retrieval for Evidence-Grounded Biomedical Question Answering", "abstract": "Trustworthy biomedical question answering (QA) systems must not only provide accurate answers but also justify them with current, verifiable evidence. Retrieval-augmented approaches partially address this gap but lack mechanisms to iteratively refine poor queries, whereas self-reflection methods kick in only after full retrieval is completed. In this context, we introduce PubMed Reasoner, a biomedical QA agent composed of three stages: self-critic query refinement evaluates MeSH terms for coverage, alignment, and redundancy to enhance PubMed queries based on partial (metadata) retrieval; reflective retrieval processes articles in batches until sufficient evidence is gathered; and evidence-grounded response generation produces answers with explicit citations. PubMed Reasoner with a GPT-4o backbone achieves 78.32% accuracy on PubMedQA, slightly surpassing human experts, and showing consistent gains on MMLU Clinical Knowledge. Moreover, LLM-as-judge evaluations prefer our responses across: reasoning soundness, evidence grounding, clinical relevance, and trustworthiness. By orchestrating retrieval-first reasoning over authoritative sources, our approach provides practical assistance to clinicians and biomedical researchers while controlling compute and token costs.", "date": "2026-03-28", "categories": []}
{"id": "2603.28167", "title": "Automating Early Disease Prediction Via Structured and Unstructured Clinical Data", "abstract": "This study presents a fully automated methodology for early prediction studies in clinical settings, leveraging information extracted from unstructured discharge reports. The proposed pipeline uses discharge reports to support the three main steps of early prediction: cohort selection, dataset generation, and outcome labeling. By processing discharge reports with natural language processing techniques, we can efficiently identify relevant patient cohorts, enrich structured datasets with additional clinical variables, and generate high-quality labels without manual intervention. This approach addresses the frequent issue of missing or incomplete data in codified electronic health records (EHR), capturing clinically relevant information that is often underrepresented. We evaluate the methodology in the context of predicting atrial fibrillation (AF) progression, showing that predictive models trained on datasets enriched with discharge report information achieve higher accuracy and correlation with true outcomes compared to models trained solely on structured EHR data, while also surpassing traditional clinical scores. These results demonstrate that automating the integration of unstructured clinical text can streamline early prediction studies, improve data quality, and enhance the reliability of predictive models for clinical decision-making.", "date": "2026-03-30", "categories": []}
{"id": "2603.28698", "title": "EpiScreen: Early Epilepsy Detection from Electronic Health Records with Large Language Models", "abstract": "Epilepsy and psychogenic non-epileptic seizures often present with similar seizure-like manifestations but require fundamentally different management strategies. Misdiagnosis is common and can lead to prolonged diagnostic delays, unnecessary treatments, and substantial patient morbidity. Although prolonged video-electroencephalography is the diagnostic gold standard, its high cost and limited accessibility hinder timely diagnosis. Here, we developed a low-cost, effective approach, EpiScreen, for early epilepsy detection by utilizing routinely collected clinical notes from electronic health records. Through fine-tuning large language models on labeled notes, EpiScreen achieved an AUC of up to 0.875 on the MIMIC-IV dataset and 0.980 on a private cohort of the University of Minnesota. In a clinician-AI collaboration setting, EpiScreen-assisted neurologists outperformed unaided experts by up to 10.9%. Overall, this study demonstrates that EpiScreen supports early epilepsy detection, facilitating timely and cost-effective screening that may reduce diagnostic delays and avoid unnecessary interventions, particularly in resource-limited regions.", "date": "2026-03-30", "categories": []}
{"id": "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": "2603.26046", "title": "Retrieval-Augmented Generation Based Nurse Observation Extraction", "abstract": "Recent advancements in Large Language Models (LLMs) have played a significant role in reducing human workload across various domains, a trend that is increasingly extending into the medical field. In this paper, we propose an automated pipeline designed to alleviate the burden on nurses by automatically extracting clinical observations from nurse dictations. To ensure accurate extraction, we introduce a method based on Retrieval-Augmented Generation (RAG). Our approach demonstrates effective performance, achieving an F1-score of 0.796 on the MEDIQA-SYNUR test dataset.", "date": "2026-03-27", "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": "2603.26510", "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": "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": "2603.21970", "title": "Parameter-Efficient Fine-Tuning for Medical Text Summarization: A Comparative Study of Lora, Prompt Tuning, and Full Fine-Tuning", "abstract": "Fine-tuning large language models for domain-specific tasks such as medical text summarization demands substantial computational resources. Parameter-efficient fine-tuning (PEFT) methods offer promising alternatives by updating only a small fraction of parameters. This paper compares three adaptation approaches-Low-Rank Adaptation (LoRA), Prompt Tuning, and Full Fine-Tuning-across the Flan-T5 model family on the PubMed medical summarization dataset. Through experiments with multiple random seeds, we demonstrate that LoRA consistently outperforms full fine-tuning, achieving 43.52 +/- 0.18 ROUGE-1 on Flan-T5-Large with only 0.6% trainable parameters compared to 40.67 +/- 0.21 for full fine-tuning. Sensitivity analyses examine the impact of LoRA rank and prompt token count. Our findings suggest the low-rank constraint provides beneficial regularization, challenging assumptions about the necessity of full parameter updates. Code is available at https://github.com/eracoding/llm-medical-summarization", "date": "2026-03-23", "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": "2603.22625", "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": "2603.22752", "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": "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": "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": "2603.19539", "title": "FDARxBench: Benchmarking Regulatory and Clinical Reasoning on FDA Generic Drug Assessment", "abstract": "We introduce an expert curated, real-world benchmark for evaluating document-grounded question-answering (QA) motivated by generic drug assessment, using the U.S. Food and Drug Administration (FDA) drug label documents. Drug labels contain rich but heterogeneous clinical and regulatory information, making accurate question answering difficult for current language models. In collaboration with FDA regulatory assessors, we introduce FDARxBench, and construct a multi-stage pipeline for generating high-quality, expert curated, QA examples spanning factual, multi-hop, and refusal tasks, and design evaluation protocols to assess both open-book and closed-book reasoning. Experiments across proprietary and open-weight models reveal substantial gaps in factual grounding, long-context retrieval, and safe refusal behavior. While motivated by FDA generic drug assessment needs, this benchmark also provides a substantial foundation for challenging regulatory-grade evaluation of label comprehension. The benchmark is designed to support evaluation of LLM behavior on drug-label questions.", "date": "2026-03-20", "categories": []}
{"id": "2503.08890", "title": "PlainQAFact: Retrieval-augmented Factual Consistency Evaluation Metric for Biomedical Plain Language Summarization", "abstract": "Hallucinated outputs from large language models (LLMs) pose risks in the medical domain, especially for lay audiences making health-related decisions. Existing automatic factual consistency evaluation methods, such as entailment- and question-answering (QA) -based, struggle with plain language summarization (PLS) due to elaborative explanation phenomenon, which introduces external content (e.g., definitions, background, examples) absent from the scientific abstract to enhance comprehension. To address this, we introduce PlainQAFact, an automatic factual consistency evaluation metric trained on a fine-grained, human-annotated dataset PlainFact, for evaluating factual consistency of both source-simplified and elaborately explained sentences. PlainQAFact first classifies sentence type, then applies a retrieval-augmented QA scoring method. Empirical results show that existing evaluation metrics fail to evaluate the factual consistency in PLS, especially for elaborative explanations, whereas PlainQAFact consistently outperforms them across all evaluation settings. We further analyze PlainQAFact's effectiveness across external knowledge sources, answer extraction strategies, answer overlap measures, and document granularity levels, refining its overall factual consistency assessment. Taken together, our work presents a sentence-aware, retrieval-augmented metric targeted at elaborative explanations in biomedical PLS tasks, providing the community with both a new benchmark and a practical evaluation tool to advance reliable and safe plain language communication in the medical domain. PlainQAFact and PlainFact are available at: https://github.com/zhiwenyou103/PlainQAFact", "date": "2025-03-11", "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": "2603.18327", "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": "2603.15711", "title": "Knowledge Graph Extraction from Biomedical Literature for Alkaptonuria Rare Disease", "abstract": "Alkaptonuria (AKU) is an ultra-rare autosomal recessive metabolic disorder caused by mutations in the HGD (Homogentisate 1,2-Dioxygenase) gene, leading to a pathological accumulation of homogentisic acid (HGA) in body fluids and tissues. This leads to systemic manifestations, including premature spondyloarthropathy, renal and prostatic stones, and cardiovascular complications. Being ultra-rare, the amount of data related to the disease is limited, both in terms of clinical data and literature. Knowledge graphs (KGs) can help connect the limited knowledge about the disease (basic mechanisms, manifestations and existing therapies) with other knowledge; however, AKU is frequently underrepresented or entirely absent in existing biomedical KGs. In this work, we apply a text-mining methodology based on PubTator3 for large-scale extraction of biomedical relations. We construct two KGs of different sizes, validate them using existing biochemical knowledge and use them to extract genes, diseases and therapies possibly related to AKU. This computational framework reveals the systemic interactions of the disease, its comorbidities, and potential therapeutic targets, demonstrating the efficacy of our approach in analyzing rare metabolic disorders.", "date": "2026-03-16", "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": "2603.16738", "title": "MedCL-Bench: Benchmarking stability-efficiency trade-offs and scaling in biomedical continual learning", "abstract": "Medical language models must be updated as evidence and terminology evolve, yet sequential updating can trigger catastrophic forgetting. Although biomedical NLP has many static benchmarks, no unified, task-diverse benchmark exists for evaluating continual learning under standardized protocols, robustness to task order and compute-aware reporting. We introduce MedCL-Bench, which streams ten biomedical NLP datasets spanning five task families and evaluates eleven continual learning strategies across eight task orders, reporting retention, transfer, and GPU-hour cost. Across backbones and task orders, direct sequential fine-tuning on incoming tasks induces catastrophic forgetting, causing update-induced performance regressions on prior tasks. Continual learning methods occupy distinct retention-compute frontiers: parameter-isolation provides the best retention per GPU-hour, replay offers strong protection at higher cost, and regularization yields limited benefit. Forgetting is task-dependent, with multi-label topic classification most vulnerable and constrained-output tasks more robust. MedCL-Bench provides a reproducible framework for auditing model updates before deployment.", "date": "2026-03-17", "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": "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": "2603.11597", "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": "2603.11780", "title": "Large Language Models for Biomedical Article Classification", "abstract": "This work presents a systematic and in-depth investigation of the utility of large language models as text classifiers for biomedical article classification. The study uses several small and mid-size open source models, as well as selected closed source ones, and is more comprehensive than most prior work with respect to the scope of evaluated configurations: different types of prompts, output processing methods for generating both class and class probability predictions, as well as few-shot example counts and selection methods. The performance of the most successful configurations is compared to that of conventional classification algorithms. The obtained average PR AUC over 15 challenging datasets above 0.4 for zero-shot prompting and nearly 0.5 for few-shot prompting comes close to that of the na\\\"ive Bayes classifier (0.5), the random forest algorithm (0.5 with default settings or 0.55 with hyperparameter tuning) and fine-tuned transformer models (0.5). These results confirm the utility of large language models as text classifiers for non-trivial domains and provide practical recommendations of the most promising setups, including in particular using output token probabilities for class probability prediction.", "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": "2603.09685", "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": "2603.00924", "title": "Conformal Prediction for Risk-Controlled Medical Entity Extraction Across Clinical Domains", "abstract": "Large Language Models (LLMs) are increasingly used for medical entity extraction, yet their confidence scores are often miscalibrated, limiting safe deployment in clinical settings. We present a conformal prediction framework that provides finite-sample coverage guarantees for LLM-based extraction across two clinical domains. First, we extract structured entities from 1,000 FDA drug labels across eight sections using GPT-4.1, verified via FactScore-based atomic statement evaluation (97.7\\% accuracy over 128,906 entities). Second, we extract radiological entities from MIMIC-CXR reports using the RadGraph schema with GPT-4.1 and Llama-4-Maverick, evaluated against physician annotations (entity F1: 0.81 to 0.84). Our central finding is that miscalibration direction reverses across domains: on well-structured FDA labels, models are underconfident, requiring modest conformal thresholds ($\\tau \\approx 0.06$), while on free-text radiology reports, models are overconfident, demanding strict thresholds ($\\tau$ up to 0.99). Despite this heterogeneity, conformal prediction achieves target coverage ($\\geq 90\\%$) in both settings with manageable rejection rates (9--13\\%). These results demonstrate that calibration is not a global model property but depends on document structure, extraction category, and model architecture, motivating domain-specific conformal calibration for safe clinical deployment.", "date": "2026-03-01", "categories": []}
{"id": "2603.06905", "title": "MedInjection-FR: Exploring the Role of Native, Synthetic, and Translated Data in Biomedical Instruction Tuning", "abstract": "Instruction tuning has become essential for adapting large language models (LLMs) to follow domain-specific prompts. Yet, in specialized fields such as medicine, the scarcity of high-quality French instruction data limits effective supervision. To address this gap, we introduce MedInjection-FR, a large-scale French biomedical instruction dataset comprising 571K instruction-response pairs drawn from three complementary sources: native, synthetic, and translated data. We design a controlled experimental framework to systematically assess how data provenance affects instruction tuning, using Qwen-4B-Instruct fine-tuned across seven configurations combining these sources. Results show that native data yield the strongest performance, while mixed setups, particularly native and translated, provide complementary benefits. Synthetic data alone remains less effective but contributes positively when balanced with native supervision. Evaluation on open-ended QA combines automatic metrics, LLM-as-a-judge assessment, and human expert review; although LLM-based judgments correlate best with human ratings, they show sensitivity to verbosity. These findings highlight that data authenticity and diversity jointly shape downstream adaptation and that heterogeneous supervision can mitigate the scarcity of native French medical instructions.", "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": "2603.08166", "title": "RexDrug: Reliable Multi-Drug Combination Extraction through Reasoning-Enhanced LLMs", "abstract": "Automated Drug Combination Extraction (DCE) from large-scale biomedical literature is crucial for advancing precision medicine and pharmacological research. However, existing relation extraction methods primarily focus on binary interactions and struggle to model variable-length n-ary drug combinations, where complex compatibility logic and distributed evidence need to be considered. To address these limitations, we propose RexDrug, an end-to-end reasoning-enhanced relation extraction framework for n-ary drug combination extraction based on large language models. RexDrug adopts a two-stage training strategy. First, a multi-agent collaborative mechanism is utilized to automatically generate high-quality expert-like reasoning traces for supervised fine-tuning. Second, reinforcement learning with a multi-dimensional reward function specifically tailored for DCE is applied to further refine reasoning quality and extraction accuracy. Extensive experiments on the DrugComb dataset show that RexDrug consistently outperforms state-of-the-art baselines for n-ary extraction. Additional evaluation on the DDI13 corpus confirms its generalizability to binary drugdrug interaction tasks. Human expert assessment and automatic reasoning metrics further indicates that RexDrug produces coherent medical reasoning while accurately identifying complex therapeutic regimens. These results establish RexDrug as a scalable and reliable solution for complex biomedical relation extraction from unstructured text. The source code and data are available at https://github.com/DUTIR-BioNLP/RexDrug", "date": "2026-03-09", "categories": []}
{"id": "2509.08087", "title": "Performance Assessment Strategies for Language Model Applications in Healthcare", "abstract": "Language models (LMs) represent an emerging paradigm within artificial intelligence, with applications throughout the medical enterprise. A comprehensive understanding of the clinical task and awareness of the variability in performance when implemented in actual clinical environments lays the foundation for the LM application assessment. Presently, a prevalent method for evaluating the performance of these generative models relies on quantitative benchmarks. Such benchmarks have limitations and may suffer from train-to-the-test overfitting, optimizing performance for a specified test set at the cost of generalizability across other tasks and data distributions. Evaluation strategies leveraging human expertise and utilizing cost-effective computational models as evaluators are gaining interest. We discuss current state-of-the-art methodologies for assessing the performance of LM applications in healthcare and medical devices.", "date": "2025-09-09", "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": "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": "2511.03441", "title": "CareMedEval dataset: Evaluating Critical Appraisal and Reasoning in the Biomedical Field", "abstract": "Critical appraisal of scientific literature is an essential skill in the biomedical field. While large language models (LLMs) can offer promising support in this task, their reliability remains limited, particularly for critical reasoning in specialized domains. We introduce CareMedEval, an original dataset designed to evaluate LLMs on biomedical critical appraisal and reasoning tasks. Derived from authentic exams taken by French medical students, the dataset contains 534 questions based on 37 scientific articles. Unlike existing benchmarks, CareMedEval explicitly evaluates critical reading and reasoning grounded in scientific papers. Benchmarking state-of-the-art generalist and biomedical-specialized LLMs under various context conditions reveals the difficulty of the task: open and commercial models fail to exceed an Exact Match Rate of 0.5 even though generating intermediate reasoning tokens considerably improves the results. Yet, models remain challenged especially on questions about study limitations and statistical analysis. CareMedEval provides a challenging benchmark for grounded reasoning, exposing current LLM limitations and paving the way for future development of automated support for critical appraisal.", "date": "2025-11-05", "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": "2603.04033", "title": "Who Judges the Judge? Evaluating LLM-as-a-Judge for French Medical open-ended QA", "abstract": "Automatic evaluation of medical open-ended question answering (OEQA) remains challenging due to the need for expert annotations. We evaluate whether large language models (LLMs) can act as judges of semantic equivalence in French medical OEQA, comparing closed-access, general-purpose, and biomedical domain-adapted models. Our results show that LLM-based judgments are strongly influenced by the model that generated the answer, with agreement varying substantially across generators. Domain-adapted and large general-purpose models achieve the highest alignment with expert annotations. We further show that lightweight adaptation of a compact model using supervised fine-tuning (SFT) and Group Relative Policy Optimization (GRPO) substantially improves performance and reduces generator sensitivity, even with limited data. Overall, our findings highlight the need for generator-aware evaluation and suggest that carefully adapted small models can support scalable evaluation in low-resource medical settings.", "date": "2026-03-04", "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": "2310.03766", "title": "Literature Based Discovery (LBD): Towards Hypothesis Generation and Knowledge Discovery in Biomedical Text Mining", "abstract": "Biomedical knowledge is growing in an astounding pace with a majority of this knowledge is represented as scientific publications. Text mining tools and methods represents automatic approaches for extracting hidden patterns and trends from this semi structured and unstructured data. In Biomedical Text mining, Literature Based Discovery (LBD) is the process of automatically discovering novel associations between medical terms otherwise mentioned in disjoint literature sets. LBD approaches proven to be successfully reducing the discovery time of potential associations that are hidden in the vast amount of scientific literature. The process focuses on creating concept profiles for medical terms such as a disease or symptom and connecting it with a drug and treatment based on the statistical significance of the shared profiles. This knowledge discovery approach introduced in 1989 still remains as a core task in text mining. Currently the ABC principle based two approaches namely open discovery and closed discovery are mostly explored in LBD process. This review starts with general introduction about text mining followed by biomedical text mining and introduces various literature resources such as MEDLINE, UMLS, MESH, and SemMedDB. This is followed by brief introduction of the core ABC principle and its associated two approaches open discovery and closed discovery in LBD process. This review also discusses the deep learning applications in LBD by reviewing the role of transformer models and neural networks based LBD models and its future aspects. Finally, reviews the key biomedical discoveries generated through LBD approaches in biomedicine and conclude with the current limitations and future directions of LBD.", "date": "2023-10-04", "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": "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": "2602.22483", "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": "2602.23199", "title": "SC-Arena: A Natural Language Benchmark for Single-Cell Reasoning with Knowledge-Augmented Evaluation", "abstract": "Large language models (LLMs) are increasingly applied in scientific research, offering new capabilities for knowledge discovery and reasoning. In single-cell biology, however, evaluation practices for both general and specialized LLMs remain inadequate: existing benchmarks are fragmented across tasks, adopt formats such as multiple-choice classification that diverge from real-world usage, and rely on metrics lacking interpretability and biological grounding. We present SC-ARENA, a natural language evaluation framework tailored to single-cell foundation models. SC-ARENA formalizes a virtual cell abstraction that unifies evaluation targets by representing both intrinsic attributes and gene-level interactions. Within this paradigm, we define five natural language tasks (cell type annotation, captioning, generation, perturbation prediction, and scientific QA) that probe core reasoning capabilities in cellular biology. To overcome the limitations of brittle string-matching metrics, we introduce knowledge-augmented evaluation, which incorporates external ontologies, marker databases, and scientific literature to support biologically faithful and interpretable judgments. Experiments and analysis across both general-purpose and domain-specialized LLMs demonstrate that (i) under the Virtual Cell unified evaluation paradigm, current models achieve uneven performance on biologically complex tasks, particularly those demanding mechanistic or causal understanding; and (ii) our knowledge-augmented evaluation framework ensures biological correctness, provides interpretable, evidence-grounded rationales, and achieves high discriminative capacity, overcoming the brittleness and opacity of conventional metrics. SC-Arena thus provides a unified and interpretable framework for assessing LLMs in single-cell biology, pointing toward the development of biology-aligned, generalizable foundation models.", "date": "2026-02-26", "categories": []}
{"id": "2602.21216", "title": "EQ-5D Classification Using Biomedical Entity-Enriched Pre-trained Language Models and Multiple Instance Learning", "abstract": "The EQ-5D (EuroQol 5-Dimensions) is a standardized instrument for the evaluation of health-related quality of life. In health economics, systematic literature reviews (SLRs) depend on the correct identification of publications that use the EQ-5D, but manual screening of large volumes of scientific literature is time-consuming, error-prone, and inconsistent. In this study, we investigate fine-tuning of general-purpose (BERT) and domain-specific (SciBERT, BioBERT) pre-trained language models (PLMs), enriched with biomedical entity information extracted through scispaCy models for each statement, to improve EQ-5D detection from abstracts. We conduct nine experimental setups, including combining three scispaCy models with three PLMs, and evaluate their performance at both the sentence and study levels. Furthermore, we explore a Multiple Instance Learning (MIL) approach with attention pooling to aggregate sentence-level information into study-level predictions, where each abstract is represented as a bag of enriched sentences (by scispaCy). The findings indicate consistent improvements in F1-scores (reaching 0.82) and nearly perfect recall at the study-level, significantly exceeding classical bag-of-words baselines and recently reported PLM baselines. These results show that entity enrichment significantly improves domain adaptation and model generalization, enabling more accurate automated screening in systematic reviews.", "date": "2026-01-30", "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": "2602.20324", "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": "2602.20040", "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": "2506.16777", "title": "DistillNote: Toward a Functional Evaluation Framework of LLM-Generated Clinical Note Summaries", "abstract": "Large language models (LLMs) are increasingly used to generate summaries from clinical notes. However, their ability to preserve essential diagnostic information remains underexplored, which could lead to serious risks for patient care. This study introduces DistillNote, an evaluation framework for LLM summaries that targets their functional utility by applying the generated summary downstream in a complex clinical prediction task, explicitly quantifying how much prediction signal is retained. We generated over 192,000 LLM summaries from MIMIC-IV clinical notes with increasing compression rates: standard, section-wise, and distilled section-wise. Heart failure diagnosis was chosen as the prediction task, as it requires integrating a wide range of clinical signals. LLMs were fine-tuned on both the original notes and their summaries, and their diagnostic performance was compared using the AUROC metric. We contrasted DistillNote's results with evaluations from LLM-as-judge and clinicians, assessing consistency across different evaluation methods. Summaries generated by LLMs maintained a strong level of heart failure diagnostic signal despite substantial compression. Models trained on the most condensed summaries (about 20 times smaller) achieved an AUROC of 0.92, compared to 0.94 with the original note baseline (97 percent retention). Functional evaluation provided a new lens for medical summary assessment, emphasizing clinical utility as a key dimension of quality. DistillNote introduces a new scalable, task-based method for assessing the functional utility of LLM-generated clinical summaries. Our results detail compression-to-performance tradeoffs from LLM clinical summarization for the first time. The framework is designed to be adaptable to other prediction tasks and clinical domains, aiding data-driven decisions about deploying LLM summarizers in real-world healthcare settings.", "date": "2025-06-20", "categories": []}
{"id": "2602.15852", "title": "Building Safe and Deployable Clinical Natural Language Processing under Temporal Leakage Constraints", "abstract": "Clinical natural language processing (NLP) models have shown promise for supporting hospital discharge planning by leveraging narrative clinical documentation. However, note-based models are particularly vulnerable to temporal and lexical leakage, where documentation artifacts encode future clinical decisions and inflate apparent predictive performance. Such behavior poses substantial risks for real-world deployment, where overconfident or temporally invalid predictions can disrupt clinical workflows and compromise patient safety. This study focuses on system-level design choices required to build safe and deployable clinical NLP under temporal leakage constraints. We present a lightweight auditing pipeline that integrates interpretability into the model development process to identify and suppress leakage-prone signals prior to final training. Using next-day discharge prediction after elective spine surgery as a case study, we evaluate how auditing affects predictive behavior, calibration, and safety-relevant trade-offs. Results show that audited models exhibit more conservative and better-calibrated probability estimates, with reduced reliance on discharge-related lexical cues. These findings emphasize that deployment-ready clinical NLP systems should prioritize temporal validity, calibration, and behavioral robustness over optimistic performance.", "date": "2026-01-24", "categories": []}
{"id": "2602.16747", "title": "LiveClin: A Live Clinical Benchmark without Leakage", "abstract": "The reliability of medical LLM evaluation is critically undermined by data contamination and knowledge obsolescence, leading to inflated scores on static benchmarks. To address these challenges, we introduce LiveClin, a live benchmark designed for approximating real-world clinical practice. Built from contemporary, peer-reviewed case reports and updated biannually, LiveClin ensures clinical currency and resists data contamination. Using a verified AI-human workflow involving 239 physicians, we transform authentic patient cases into complex, multimodal evaluation scenarios that span the entire clinical pathway. The benchmark currently comprises 1,407 case reports and 6,605 questions. Our evaluation of 26 models on LiveClin reveals the profound difficulty of these real-world scenarios, with the top-performing model achieving a Case Accuracy of just 35.7%. In benchmarking against human experts, Chief Physicians achieved the highest accuracy, followed closely by Attending Physicians, with both surpassing most models. LiveClin thus provides a continuously evolving, clinically grounded framework to guide the development of medical LLMs towards closing this gap and achieving greater reliability and real-world utility. Our data and code are publicly available at https://github.com/AQ-MedAI/LiveClin.", "date": "2026-02-18", "categories": []}
{"id": "2602.17475", "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": "2602.15874", "title": "P-RAG: Prompt-Enhanced Parametric RAG with LoRA and Selective CoT for Biomedical and Multi-Hop QA", "abstract": "Large Language Models (LLMs) demonstrate remarkable capabilities but remain limited by their reliance on static training data. Retrieval-Augmented Generation (RAG) addresses this constraint by retrieving external knowledge during inference, though it still depends heavily on knowledge base quality. To explore potential improvements, we evaluated three RAG variants-Standard RAG, DA-RAG, and our proposed Prompt-Enhanced Parametric RAG (P-RAG), a hybrid architecture that integrates parametric knowledge within the LLM and retrieved evidence, guided by Chain-of-Thought (CoT) prompting and Low-Rank Adaptation (LoRA) fine-tuning-on both general and biomedical datasets. Using LLaMA-3.2-1B-Instruct fine-tuned via LoRA, we evaluate on PubMedQA and 2WikiMultihopQA. P-RAG outperforms Standard RAG on PubMedQA by 10.47 percentage points in F1 (93.33% vs. 82.86%; 12.64% relative). On 2WikiMultihopQA, P-RAG nearly doubles the overall score vs. Standard RAG (33.44% vs. 17.83%) and achieves 44.03% on the Compare subset (with 42.74% Bridge, 21.84% Inference, 8.60% Compose). CoT prompting substantially improves multi-hop reasoning but yields mixed results for simpler, single-hop queries. These findings underscore P-RAG's potential for accurate, scalable, and contextually adaptive biomedical question answering. Our contributions include: (1) LoRA-based fine-tuning of LLaMA-3.2-1B-Instruct for biomedical QA, (2) introduction of P-RAG with Chain-of-Thought prompting, and (3) state-of-the-art results on PubMedQA and 2WikiMultihopQA.", "date": "2026-02-02", "categories": []}
{"id": "2502.16051", "title": "Moving Beyond Medical Exams: A Clinician-Annotated Fairness Dataset of Real-World Tasks and Ambiguity in Mental Healthcare", "abstract": "Current medical language model (LM) benchmarks often over-simplify the complexities of day-to-day clinical practice tasks and instead rely on evaluating LMs on multiple-choice board exam questions. In psychiatry especially, these challenges are worsened by fairness and bias issues, since models can be swayed by patient demographics even when those factors should not influence clinical decisions. Thus, we present an expert-created and annotated dataset spanning five critical domains of decision-making in mental healthcare: treatment, diagnosis, documentation, monitoring, and triage. This U.S.-centric dataset - created without any LM assistance - is designed to capture the nuanced clinical reasoning and daily ambiguities mental health practitioners encounter, reflecting the inherent complexities of care delivery that are missing from existing datasets. Almost all base questions with five answer options each have had the decision-irrelevant demographic patient information removed and replaced with variables, e.g., for age or ethnicity, and are available for male, female, or non-binary-coded patients. This design enables systematic evaluations of model performance and bias by studying how demographic factors affect decision-making. For question categories dealing with ambiguity and multiple valid answer options, we create a preference dataset with uncertainties from the expert annotations. We outline a series of intended use cases and demonstrate the usability of our dataset by evaluating sixteen off-the-shelf and six (mental) health fine-tuned LMs on category-specific task accuracy, on the fairness impact of patient demographic information on decision-making, and how consistently free-form responses deviate from human-annotated samples.", "date": "2025-02-22", "categories": []}
{"id": "2506.16594", "title": "A Scoping Review of Synthetic Data Generation by Language Models in Biomedical Research and Application: Data Utility and Quality Perspectives", "abstract": "Synthetic data generation using large language models (LLMs) demonstrates substantial promise in addressing biomedical data challenges and shows increasing adoption in biomedical research. This study systematically reviews recent advances in synthetic data generation for biomedical applications and clinical research, focusing on how LLMs address data scarcity, utility, and quality issues with different modalities. We conducted a scoping review following PRISMA-ScR guidelines and searched literature published between 2020 and 2025 through PubMed, ACM, Web of Science, and Google Scholar. A total of 59 studies were included based on relevance to synthetic data generation in biomedical contexts. Among the reviewed studies, the predominant data modalities were unstructured texts (78.0\\%), tabular data (13.6\\%), and multimodal sources (8.4\\%). Common generation methods included LLM prompting (74.6\\%), fine-tuning (20.3\\%), and specialized models (5.1\\%). Evaluations were heterogeneous: intrinsic metrics (27.1\\%), human-in-the-loop assessments (44.1\\%), and LLM-based evaluations (13.6\\%). However, limitations and key barriers persist in data modalities, domain utility, resource and model accessibility, and standardized evaluation protocols. Future efforts may focus on developing standardized, transparent evaluation frameworks and expanding accessibility to support effective applications in biomedical research.", "date": "2025-06-19", "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": "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": "2602.10345", "title": "Identifying Evidence-Based Nudges in Biomedical Literature with Large Language Models", "abstract": "We present a scalable, AI-powered system that identifies and extracts evidence-based behavioral nudges from unstructured biomedical literature. Nudges are subtle, non-coercive interventions that influence behavior without limiting choice, showing strong impact on health outcomes like medication adherence. However, identifying these interventions from PubMed's 8 million+ articles is a bottleneck. Our system uses a novel multi-stage pipeline: first, hybrid filtering (keywords, TF-IDF, cosine similarity, and a \"nudge-term bonus\") reduces the corpus to about 81,000 candidates. Second, we use OpenScholar (quantized LLaMA 3.1 8B) to classify papers and extract structured fields like nudge type and target behavior in a single pass, validated against a JSON schema.\n  We evaluated four configurations on a labeled test set (N=197). The best setup (Title/Abstract/Intro) achieved a 67.0% F1 score and 72.0% recall, ideal for discovery. A high-precision variant using self-consistency (7 randomized passes) achieved 100% precision with 12% recall, demonstrating a tunable trade-off for high-trust use cases. This system is being integrated into Agile Nudge+, a real-world platform, to ground LLM-generated interventions in peer-reviewed evidence. This work demonstrates interpretable, domain-specific retrieval pipelines for evidence synthesis and personalized healthcare.", "date": "2026-02-10", "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": "2602.04982", "title": "BioACE: An Automated Framework for Biomedical Answer and Citation Evaluations", "abstract": "With the increasing use of large language models (LLMs) for generating answers to biomedical questions, it is crucial to evaluate the quality of the generated answers and the references provided to support the facts in the generated answers. Evaluation of text generated by LLMs remains a challenge for question answering, retrieval-augmented generation (RAG), summarization, and many other natural language processing tasks in the biomedical domain, due to the requirements of expert assessment to verify consistency with the scientific literature and complex medical terminology. In this work, we propose BioACE, an automated framework for evaluating biomedical answers and citations against the facts stated in the answers. The proposed BioACE framework considers multiple aspects, including completeness, correctness, precision, and recall, in relation to the ground-truth nuggets for answer evaluation. We developed automated approaches to evaluate each of the aforementioned aspects and performed extensive experiments to assess and analyze their correlation with human evaluations. In addition, we considered multiple existing approaches, such as natural language inference (NLI) and pre-trained language models and LLMs, to evaluate the quality of evidence provided to support the generated answers in the form of citations into biomedical literature. With the detailed experiments and analysis, we provide the best approaches for biomedical answer and citation evaluation as a part of BioACE (https://github.com/deepaknlp/BioACE) evaluation package.", "date": "2026-02-04", "categories": []}
{"id": "2602.05374", "title": "Cross-Lingual Empirical Evaluation of Large Language Models for Arabic Medical Tasks", "abstract": "In recent years, Large Language Models (LLMs) have become widely used in medical applications, such as clinical decision support, medical education, and medical question answering. Yet, these models are often English-centric, limiting their robustness and reliability for linguistically diverse communities. Recent work has highlighted discrepancies in performance in low-resource languages for various medical tasks, but the underlying causes remain poorly understood. In this study, we conduct a cross-lingual empirical analysis of LLM performance on Arabic and English medical question and answering. Our findings reveal a persistent language-driven performance gap that intensifies with increasing task complexity. Tokenization analysis exposes structural fragmentation in Arabic medical text, while reliability analysis suggests that model-reported confidence and explanations exhibit limited correlation with correctness. Together, these findings underscore the need for language-aware design and evaluation strategies in LLMs for medical tasks.", "date": "2026-02-05", "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": "2405.18605", "title": "Merged ChemProt-DrugProt for Relation Extraction from Biomedical Literature", "abstract": "The extraction of chemical-gene relations plays a pivotal role in understanding the intricate interactions between chemical compounds and genes, with significant implications for drug discovery, disease understanding, and biomedical research. This paper presents a data set created by merging the ChemProt and DrugProt datasets to augment sample counts and improve model accuracy. We evaluate the merged dataset using two state of the art relationship extraction algorithms: Bidirectional Encoder Representations from Transformers (BERT) specifically BioBERT, and Graph Convolutional Networks (GCNs) combined with BioBERT. While BioBERT excels at capturing local contexts, it may benefit from incorporating global information essential for understanding chemical-gene interactions. This can be achieved by integrating GCNs with BioBERT to harness both global and local context. Our results show that by integrating the ChemProt and DrugProt datasets, we demonstrated significant improvements in model performance, particularly in CPR groups shared between the datasets. Incorporating the global context using GCN can help increase the overall precision and recall in some of the CPR groups over using just BioBERT.", "date": "2024-05-28", "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": "2602.04320", "title": "A Domain-Specific Curated Benchmark for Entity and Document-Level Relation Extraction", "abstract": "Information Extraction (IE), encompassing Named Entity Recognition (NER), Named Entity Linking (NEL), and Relation Extraction (RE), is critical for transforming the rapidly growing volume of scientific publications into structured, actionable knowledge. This need is especially evident in fast-evolving biomedical fields such as the gut-brain axis, where research investigates complex interactions between the gut microbiota and brain-related disorders. Existing biomedical IE benchmarks, however, are often narrow in scope and rely heavily on distantly supervised or automatically generated annotations, limiting their utility for advancing robust IE methods. We introduce GutBrainIE, a benchmark based on more than 1,600 PubMed abstracts, manually annotated by biomedical and terminological experts with fine-grained entities, concept-level links, and relations. While grounded in the gut-brain axis, the benchmark's rich schema, multiple tasks, and combination of highly curated and weakly supervised data make it broadly applicable to the development and evaluation of biomedical IE systems across domains.", "date": "2026-02-04", "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": "2602.01714", "title": "MedAraBench: Large-Scale Arabic Medical Question Answering Dataset and Benchmark", "abstract": "Arabic remains one of the most underrepresented languages in natural language processing research, particularly in medical applications, due to the limited availability of open-source data and benchmarks. The lack of resources hinders efforts to evaluate and advance the multilingual capabilities of Large Language Models (LLMs). In this paper, we introduce MedAraBench, a large-scale dataset consisting of Arabic multiple-choice question-answer pairs across various medical specialties. We constructed the dataset by manually digitizing a large repository of academic materials created by medical professionals in the Arabic-speaking region. We then conducted extensive preprocessing and split the dataset into training and test sets to support future research efforts in the area. To assess the quality of the data, we adopted two frameworks, namely expert human evaluation and LLM-as-a-judge. Our dataset is diverse and of high quality, spanning 19 specialties and five difficulty levels. For benchmarking purposes, we assessed the performance of eight state-of-the-art open-source and proprietary models, such as GPT-5, Gemini 2.0 Flash, and Claude 4-Sonnet. Our findings highlight the need for further domain-specific enhancements. We release the dataset and evaluation scripts to broaden the diversity of medical data benchmarks, expand the scope of evaluation suites for LLMs, and enhance the multilingual capabilities of models for deployment in clinical settings.", "date": "2026-02-02", "categories": []}
{"id": "2602.01779", "title": "LingLanMiDian: Systematic Evaluation of LLMs on TCM Knowledge and Clinical Reasoning", "abstract": "Large language models (LLMs) are advancing rapidly in medical NLP, yet Traditional Chinese Medicine (TCM) with its distinctive ontology, terminology, and reasoning patterns requires domain-faithful evaluation. Existing TCM benchmarks are fragmented in coverage and scale and rely on non-unified or generation-heavy scoring that hinders fair comparison. We present the LingLanMiDian (LingLan) benchmark, a large-scale, expert-curated, multi-task suite that unifies evaluation across knowledge recall, multi-hop reasoning, information extraction, and real-world clinical decision-making. LingLan introduces a consistent metric design, a synonym-tolerant protocol for clinical labels, a per-dataset 400-item Hard subset, and a reframing of diagnosis and treatment recommendation into single-choice decision recognition. We conduct comprehensive, zero-shot evaluations on 14 leading open-source and proprietary LLMs, providing a unified perspective on their strengths and limitations in TCM commonsense knowledge understanding, reasoning, and clinical decision support; critically, the evaluation on Hard subset reveals a substantial gap between current models and human experts in TCM-specialized reasoning. By bridging fundamental knowledge and applied reasoning through standardized evaluation, LingLan establishes a unified, quantitative, and extensible foundation for advancing TCM LLMs and domain-specific medical AI research. All evaluation data and code are available at https://github.com/TCMAI-BJTU/LingLan and http://tcmnlp.com.", "date": "2026-02-02", "categories": []}
{"id": "2510.13926", "title": "BioMedSearch: A Multi-Source Biomedical Retrieval Framework Based on LLMs", "abstract": "Biomedical queries often rely on a deep understanding of specialized knowledge such as gene regulatory mechanisms and pathological processes of diseases. They require detailed analysis of complex physiological processes and effective integration of information from multiple data sources to support accurate retrieval and reasoning. Although large language models (LLMs) perform well in general reasoning tasks, their generated biomedical content often lacks scientific rigor due to the inability to access authoritative biomedical databases and frequently fabricates protein functions, interactions, and structural details that deviate from authentic information. Therefore, we present BioMedSearch, a multi-source biomedical information retrieval framework based on LLMs. The method integrates literature retrieval, protein database and web search access to support accurate and efficient handling of complex biomedical queries. Through sub-queries decomposition, keywords extraction, task graph construction, and multi-source information filtering, BioMedSearch generates high-quality question-answering results. To evaluate the accuracy of question answering, we constructed a multi-level dataset, BioMedMCQs, consisting of 3,000 questions. The dataset covers three levels of reasoning: mechanistic identification, non-adjacent semantic integration, and temporal causal reasoning, and is used to assess the performance of BioMedSearch and other methods on complex QA tasks. Experimental results demonstrate that BioMedSearch consistently improves accuracy over all baseline models across all levels. Specifically, at Level 1, the average accuracy increases from 59.1% to 91.9%; at Level 2, it rises from 47.0% to 81.0%; and at the most challenging Level 3, the average accuracy improves from 36.3% to 73.4%. The code and BioMedMCQs are available at: https://github.com/CyL-ucas/BioMed_Search", "date": "2025-10-15", "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": "2601.20674", "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": "2409.07314", "title": "MEDIC: Comprehensive Evaluation of Leading Indicators for LLM Safety and Utility in Clinical Applications", "abstract": "While Large Language Models (LLMs) achieve superhuman performance on standardized medical licensing exams, these static benchmarks have become saturated and increasingly disconnected from the functional requirements of clinical workflows. To bridge the gap between theoretical capability and verified utility, we introduce MEDIC, a comprehensive evaluation framework establishing leading indicators across various clinical dimensions. Beyond standard question-answering, we assess operational capabilities using deterministic execution protocols and a novel Cross-Examination Framework (CEF), which quantifies information fidelity and hallucination rates without reliance on reference texts. Our evaluation across a heterogeneous task suite exposes critical performance trade-offs: we identify a significant knowledge-execution gap, where proficiency in static retrieval does not predict success in operational tasks such as clinical calculation or SQL generation. Furthermore, we observe a divergence between passive safety (refusal) and active safety (error detection), revealing that models fine-tuned for high refusal rates often fail to reliably audit clinical documentation for factual accuracy. These findings demonstrate that no single architecture dominates across all dimensions, highlighting the necessity of a portfolio approach to clinical model deployment. As part of this investigation, we released a public leaderboard on Hugging Face.\\footnote{https://huggingface.co/spaces/m42-health/MEDIC-Benchmark}", "date": "2024-09-11", "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": "2601.18796", "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": "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": "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": "2505.07889", "title": "BioProBench: Comprehensive Dataset and Benchmark in Biological Protocol Understanding and Reasoning", "abstract": "The realization of autonomous scientific experimentation is currently limited by LLMs' struggle to grasp the strict procedural logic and accuracy required by biological protocols. To address this fundamental challenge, we present \\textbf{BioProBench}, a comprehensive resource for procedural reasoning in biology. BioProBench is grounded in \\textbf{BioProCorpus}, a foundational collection of 27,000 human-written protocols. From this corpus, we systematically constructed a dataset of over 550,000 task instances, offering both a large-scale training resource and a rigorous benchmark with novel metrics. Evaluating 10 mainstream LLMs, we find that while general comprehension is high, performance drops significantly on tasks demanding deep reasoning, quantitative precision, and safety awareness. To demonstrate the value of BioProCorpus in mitigating these issues, we developed \\textbf{ProAgent}, grounded in our corpus, ProAgent substantially advances the state-of-the-art. BioProBench provides a rigorous diagnostic benchmark and a foundational resource for developing the next generation of reliable scientific AI. Code and data are available at: https://github.com/YuyangSunshine/bioprotocolbench and https://huggingface.co/datasets/BioProBench/BioProBench.", "date": "2025-05-11", "categories": []}
{"id": "2601.14267", "title": "From Chaos to Clarity: Schema-Constrained AI for Auditable Biomedical Evidence Extraction from Full-Text PDFs", "abstract": "Biomedical evidence synthesis relies on accurate extraction of methodological, laboratory, and outcome variables from full-text research articles, yet these variables are embedded in complex scientific PDFs that make manual abstraction time-consuming and difficult to scale. Existing document AI systems remain limited by OCR errors, long-document fragmentation, constrained throughput, and insufficient auditability for high-stakes synthesis. We present a schema-constrained AI extraction system that transforms full-text biomedical PDFs into structured, analysis-ready records by explicitly restricting model inference through typed schemas, controlled vocabularies, and evidence-gated decisions. Documents are ingested using resume-aware hashing, partitioned into caption-aware page-level chunks, and processed asynchronously under explicit concurrency controls. Chunk-level outputs are deterministically merged into study-level records using conflict-aware consolidation, set-based aggregation, and sentence-level provenance to support traceability and post-hoc audit. Evaluated on a corpus of studies on direct oral anticoagulant level measurement, the pipeline processed all documents without manual intervention, maintained stable throughput under service constraints, and exhibited strong internal consistency across document chunks. Iterative schema refinement substantially improved extraction fidelity for synthesis-critical variables, including assay classification, outcome definitions, follow-up duration, and timing of measurement. These results demonstrate that schema-constrained, provenance-aware extraction enables scalable and auditable transformation of heterogeneous scientific PDFs into structured evidence, aligning modern document AI with the transparency and reliability requirements of biomedical evidence synthesis.", "date": "2025-12-31", "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": "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": "2601.11825", "title": "AI Co-Scientist for Knowledge Synthesis in Medical Contexts: A Proof of Concept", "abstract": "Research waste in biomedical science is driven by redundant studies, incomplete reporting, and the limited scalability of traditional evidence synthesis workflows. We present an AI co-scientist for scalable and transparent knowledge synthesis based on explicit formalization of Population, Intervention, Comparator, Outcome, and Study design (PICOS). The platform integrates relational storage, vector-based semantic retrieval, and a Neo4j knowledge graph. Evaluation was conducted on dementia-sport and non-communicable disease corpora. Automated PICOS compliance and study design classification from titles and abstracts were performed using a Bidirectional Long Short-Term Memory baseline and a transformer-based multi-task classifier fine-tuned from PubMedBERT. Full-text synthesis employed retrieval-augmented generation with hybrid vector and graph retrieval, while BERTopic was used to identify thematic structure, redundancy, and evidence gaps. The transformer model achieved 95.7% accuracy for study design classification with strong agreement against expert annotations, while the Bi-LSTM achieved 87% accuracy for PICOS compliance detection. Retrieval-augmented generation outperformed non-retrieval generation for queries requiring structured constraints, cross-study integration, and graph-based reasoning, whereas non-retrieval approaches remained competitive for high-level summaries. Topic modeling revealed substantial thematic redundancy and identified underexplored research areas. These results demonstrate that PICOS-aware and explainable natural language processing can improve the scalability, transparency, and efficiency of evidence synthesis. The proposed architecture is domain-agnostic and offers a practical framework for reducing research waste across biomedical disciplines.", "date": "2026-01-16", "categories": []}
{"id": "2601.12632", "title": "BioPulse-QA: A Dynamic Biomedical Question-Answering Benchmark for Evaluating Factuality, Robustness, and Bias in Large Language Models", "abstract": "Objective: Large language models (LLMs) are increasingly applied in biomedical settings, and existing benchmark datasets have played an important role in supporting model development and evaluation. However, these benchmarks often have limitations. Many rely on static or outdated datasets that fail to capture the dynamic, context-rich, and high-stakes nature of biomedical knowledge. They also carry increasing risk of data leakage due to overlap with model pretraining corpora and often overlook critical dimensions such as robustness to linguistic variation and potential demographic biases.\n  Materials and Methods: To address these gaps, we introduce BioPulse-QA, a benchmark that evaluates LLMs on answering questions from newly published biomedical documents including drug labels, trial protocols, and clinical guidelines. BioPulse-QA includes 2,280 expert-verified question answering (QA) pairs and perturbed variants, covering both extractive and abstractive formats. We evaluate four LLMs - GPT-4o, GPT-o1, Gemini-2.0-Flash, and LLaMA-3.1 8B Instruct - released prior to the publication dates of the benchmark documents.\n  Results: GPT-o1 achieves the highest relaxed F1 score (0.92), followed by Gemini-2.0-Flash (0.90) on drug labels. Clinical trials are the most challenging source, with extractive F1 scores as low as 0.36.\n  Discussion and Conclusion: Performance differences are larger for paraphrasing than for typographical errors, while bias testing shows negligible differences. BioPulse-QA provides a scalable and clinically relevant framework for evaluating biomedical LLMs.", "date": "2026-01-19", "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": "2601.13178", "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": "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": "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": "2510.18691", "title": "Investigating LLM Capabilities on Long Context Comprehension for Medical Question Answering", "abstract": "This study is the first to investigate LLM comprehension capabilities over long-context (LC), clinically relevant medical Question Answering (QA) beyond MCQA. Our comprehensive approach considers a range of settings based on content inclusion of varying size and relevance, LLM models of different capabilities and a variety of datasets across task formulations. We reveal insights on model size effects and their limitations, underlying memorization issues and the benefits of reasoning models, while demonstrating the value and challenges of leveraging the full long patient's context. Importantly, we examine the effect of Retrieval Augmented Generation (RAG) on medical LC comprehension, showcasing best settings in single versus multi-document QA datasets. We shed light into some of the evaluation aspects using a multi-faceted approach uncovering common metric challenges. Our quantitative analysis reveals challenging cases where RAG excels while still showing limitations in cases requiring temporal reasoning.", "date": "2025-10-21", "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": "2601.10020", "title": "EHRNavigator: A Multi-Agent System for Patient-Level Clinical Question Answering over Heterogeneous Electronic Health Records", "abstract": "Clinical decision-making increasingly relies on timely and context-aware access to patient information within Electronic Health Records (EHRs), yet most existing natural language question-answering (QA) systems are evaluated solely on benchmark datasets, limiting their practical relevance. To overcome this limitation, we introduce EHRNavigator, a multi-agent framework that harnesses AI agents to perform patient-level question answering across heterogeneous and multimodal EHR data. We assessed its performance using both public benchmark and institutional datasets under realistic hospital conditions characterized by diverse schemas, temporal reasoning demands, and multimodal evidence integration. Through quantitative evaluation and clinician-validated chart review, EHRNavigator demonstrated strong generalization, achieving 86% accuracy on real-world cases while maintaining clinically acceptable response times. Overall, these findings confirm that EHRNavigator effectively bridges the gap between benchmark evaluation and clinical deployment, offering a robust, adaptive, and efficient solution for real-world EHR question answering.", "date": "2026-01-15", "categories": []}
{"id": "2601.10581", "title": "From Single to Multi-Agent Reasoning: Advancing GeneGPT for Genomics QA", "abstract": "Comprehending genomic information is essential for biomedical research, yet extracting data from complex distributed databases remains challenging. Large language models (LLMs) offer potential for genomic Question Answering (QA) but face limitations due to restricted access to domain-specific databases. GeneGPT is the current state-of-the-art system that enhances LLMs by utilizing specialized API calls, though it is constrained by rigid API dependencies and limited adaptability. We replicate GeneGPT and propose GenomAgent, a multi-agent framework that efficiently coordinates specialized agents for complex genomics queries. Evaluated on nine tasks from the GeneTuring benchmark, GenomAgent outperforms GeneGPT by 12% on average, and its flexible architecture extends beyond genomics to various scientific domains needing expert knowledge extraction.", "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": "2504.04083", "title": "A Benchmark for End-to-End Zero-Shot Biomedical Relation Extraction with LLMs: Experiments with OpenAI Models", "abstract": "Extracting relations from scientific literature is a fundamental task in biomedical NLP because entities and relations among them drive hypothesis generation and knowledge discovery. As literature grows rapidly, relation extraction (RE) is indispensable to curate knowledge graphs to be used as computable structured and symbolic representations. With the rise of LLMs, it is pertinent to examine if it is better to skip tailoring supervised RE methods, save annotation burden, and just use zero shot RE (ZSRE) via LLM API calls. In this paper, we propose a benchmark with seven biomedical RE datasets with interesting characteristics and evaluate three Open AI models (GPT-4, o1, and GPT-OSS-120B) for end-to-end ZSRE. We show that LLM-based ZSRE is inching closer to supervised methods in performances on some datasets but still struggles on complex inputs expressing multiple relations with different predicates. Our error analysis reveals scope for improvements.", "date": "2025-04-05", "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": "2505.07917", "title": "Efficient and Reproducible Biomedical Question Answering using Retrieval Augmented Generation", "abstract": "Biomedical question-answering (QA) systems require effective retrieval and generation components to ensure accuracy, efficiency, and scalability. This study systematically examines a Retrieval-Augmented Generation (RAG) system for biomedical QA, evaluating retrieval strategies and response time trade-offs. We first assess state-of-the-art retrieval methods, including BM25, BioBERT, MedCPT, and a hybrid approach, alongside common data stores such as Elasticsearch, MongoDB, and FAISS, on a ~10% subset of PubMed (2.4M documents) to measure indexing efficiency, retrieval latency, and retriever performance in the end-to-end RAG system. Based on these insights, we deploy the final RAG system on the full 24M PubMed corpus, comparing different retrievers' impact on overall performance. Evaluations of the retrieval depth show that retrieving 50 documents with BM25 before reranking with MedCPT optimally balances accuracy (0.90), recall (0.90), and response time (1.91s). BM25 retrieval time remains stable (82ms), while MedCPT incurs the main computational cost. These results highlight previously not well-known trade-offs in retrieval depth, efficiency, and scalability for biomedical QA. With open-source code, the system is fully reproducible and extensible.", "date": "2025-05-12", "categories": []}
{"id": "2601.07935", "title": "Towards Specialized Generalists: A Multi-Task MoE-LoRA Framework for Domain-Specific LLM Adaptation", "abstract": "The rapid evolution of Large Language Models (LLMs) has shifted focus from general-purpose capabilities to domain-specific expertise. However, adapting LLMs to specialized fields such as medicine presents two challenge: (1) the \"Stability-Plasticity Dilemma\", where the model must acquire complex clinical knowledge without suffering from catastrophic forgetting of general world knowledge; and (2) \"Task Interference\", where disparate sub-tasks, such as medical diagnosis, report summarization, and drug-drug interaction prediction, compete for limited low-rank parameter space. In this paper, we propose Med-MoE-LoRA, a novel framework that integrates Mixture-of-Experts (MoE) with Low-Rank Adaptation (LoRA) to enable efficient multi-task domain adaptation, especially for medical scenarios. Drawing inspiration from recent advances, our framework employs an asymmetric expert distribution where deeper layers are equipped with a higher density of LoRA experts to capture complex semantic abstractions. We further introduce a \"Knowledge-Preservation Plugin\", inspired by LoRA MoE, to isolate and protect general-purpose reasoning. By utilizing soft merging with adaptive routing and rank-wise decoupling, Med-MoE-LoRA achieves superior performance in medical benchmarks while reducing interference. Experimental results demonstrate that our approach consistently outperforms standard LoRA and conventional MoE architectures across multiple clinical NLP tasks while retaining the model's general cognitive capabilities.", "date": "2026-01-12", "categories": []}
{"id": "2503.10662", "title": "Evaluation of the Automated Labeling Method for Taxonomic Nomenclature Through Prompt-Optimized Large Language Model", "abstract": "Scientific names of organisms consist of a genus name and a species epithet, with the latter often reflecting aspects such as morphology, ecology, distribution, and cultural background. Traditionally, researchers have manually labeled species names by carefully examining taxonomic descriptions, a process that demands substantial time and effort when dealing with large datasets. This study evaluates the feasibility of automatic species name labeling using large language model (LLM) by leveraging their text classification and semantic extraction capabilities. Using the spider name dataset compiled by Mammola et al., we compared LLM-based labeling results-enhanced through prompt engineering-with human annotations. The results indicate that LLM-based classification achieved high accuracy in Morphology, Geography, and People categories. However, classification accuracy was lower in Ecology & Behavior and Modern & Past Culture, revealing challenges in interpreting animal behavior and cultural contexts. Future research will focus on improving accuracy through optimized few-shot learning and retrieval-augmented generation techniques, while also expanding the applicability of LLM-based labeling to diverse biological taxa.", "date": "2025-03-08", "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": "2601.06193", "title": "MLB: A Scenario-Driven Benchmark for Evaluating Large Language Models in Clinical Applications", "abstract": "The proliferation of Large Language Models (LLMs) presents transformative potential for healthcare, yet practical deployment is hindered by the absence of frameworks that assess real-world clinical utility. Existing benchmarks test static knowledge, failing to capture the dynamic, application-oriented capabilities required in clinical practice. To bridge this gap, we introduce a Medical LLM Benchmark MLB, a comprehensive benchmark evaluating LLMs on both foundational knowledge and scenario-based reasoning. MLB is structured around five core dimensions: Medical Knowledge (MedKQA), Safety and Ethics (MedSE), Medical Record Understanding (MedRU), Smart Services (SmartServ), and Smart Healthcare (SmartCare). The benchmark integrates 22 datasets (17 newly curated) from diverse Chinese clinical sources, covering 64 clinical specialties. Its design features a rigorous curation pipeline involving 300 licensed physicians. Besides, we provide a scalable evaluation methodology, centered on a specialized judge model trained via Supervised Fine-Tuning (SFT) on expert annotations. Our comprehensive evaluation of 10 leading models reveals a critical translational gap: while the top-ranked model, Kimi-K2-Instruct (77.3% accuracy overall), excels in structured tasks like information extraction (87.8% accuracy in MedRU), performance plummets in patient-facing scenarios (61.3% in SmartServ). Moreover, the exceptional safety score (90.6% in MedSE) of the much smaller Baichuan-M2-32B highlights that targeted training is equally critical. Our specialized judge model, trained via SFT on a 19k expert-annotated medical dataset, achieves 92.1% accuracy, an F1-score of 94.37%, and a Cohen's Kappa of 81.3% for human-AI consistency, validating a reproducible and expert-aligned evaluation protocol. MLB thus provides a rigorous framework to guide the development of clinically viable LLMs.", "date": "2026-01-08", "categories": []}
{"id": "2601.06300", "title": "$\\texttt{AMEND++}$: Benchmarking Eligibility Criteria Amendments in Clinical Trials", "abstract": "Clinical trial amendments frequently introduce delays, increased costs, and administrative burden, with eligibility criteria being the most commonly amended component. We introduce \\textit{eligibility criteria amendment prediction}, a novel NLP task that aims to forecast whether the eligibility criteria of an initial trial protocol will undergo future amendments. To support this task, we release $\\texttt{AMEND++}$, a benchmark suite comprising two datasets: $\\texttt{AMEND}$, which captures eligibility-criteria version histories and amendment labels from public clinical trials, and $\\verb|AMEND_LLM|$, a refined subset curated using an LLM-based denoising pipeline to isolate substantive changes. We further propose $\\textit{Change-Aware Masked Language Modeling}$ (CAMLM), a revision-aware pretraining strategy that leverages historical edits to learn amendment-sensitive representations. Experiments across diverse baselines show that CAMLM consistently improves amendment prediction, enabling more robust and cost-effective clinical trial design.", "date": "2026-01-09", "categories": []}
{"id": "2601.06519", "title": "MedRAGChecker: Claim-Level Verification for Biomedical Retrieval-Augmented Generation", "abstract": "Biomedical retrieval-augmented generation (RAG) can ground LLM answers in medical literature, yet long-form outputs often contain isolated unsupported or contradictory claims with safety implications.\n  We introduce MedRAGChecker, a claim-level verification and diagnostic framework for biomedical RAG.\n  Given a question, retrieved evidence, and a generated answer, MedRAGChecker decomposes the answer into atomic claims and estimates claim support by combining evidence-grounded natural language inference (NLI) with biomedical knowledge-graph (KG) consistency signals.\n  Aggregating claim decisions yields answer-level diagnostics that help disentangle retrieval and generation failures, including faithfulness, under-evidence, contradiction, and safety-critical error rates.\n  To enable scalable evaluation, we distill the pipeline into compact biomedical models and use an ensemble verifier with class-specific reliability weighting.\n  Experiments on four biomedical QA benchmarks show that MedRAGChecker reliably flags unsupported and contradicted claims and reveals distinct risk profiles across generators, particularly on safety-critical biomedical relations.", "date": "2026-01-10", "categories": []}
{"id": "2601.06624", "title": "Efficient and Reliable Estimation of Named Entity Linking Quality: A Case Study on GutBrainIE", "abstract": "Named Entity Linking (NEL) is a core component of biomedical Information Extraction (IE) pipelines, yet assessing its quality at scale is challenging due to the high cost of expert annotations and the large size of corpora. In this paper, we present a sampling-based framework to estimate the NEL accuracy of large-scale IE corpora under statistical guarantees and constrained annotation budgets. We frame NEL accuracy estimation as a constrained optimization problem, where the objective is to minimize expected annotation cost subject to a target Margin of Error (MoE) for the corpus-level accuracy estimate. Building on recent works on knowledge graph accuracy estimation, we adapt Stratified Two-Stage Cluster Sampling (STWCS) to the NEL setting, defining label-based strata and global surface-form clusters in a way that is independent of NEL annotations. Applied to 11,184 NEL annotations in GutBrainIE -- a new biomedical corpus openly released in fall 2025 -- our framework reaches a MoE $\\leq 0.05$ by manually annotating only 2,749 triples (24.6%), leading to an overall accuracy estimate of $0.915 \\pm 0.0473$. A time-based cost model and simulations against a Simple Random Sampling (SRS) baseline show that our design reduces expert annotation time by about 29% at fixed sample size. The framework is generic and can be applied to other NEL benchmarks and IE pipelines that require scalable and statistically robust accuracy assessment.", "date": "2026-01-10", "categories": []}
{"id": "2601.06974", "title": "UETQuintet at BioCreative IX -- MedHopQA: Enhancing Biomedical QA with Selective Multi-hop Reasoning and Contextual Retrieval", "abstract": "Biomedical Question Answering systems play a critical role in processing complex medical queries, yet they often struggle with the intricate nature of medical data and the demand for multi-hop reasoning. In this paper, we propose a model designed to effectively address both direct and sequential questions. While sequential questions are decomposed into a chain of sub-questions to perform reasoning across a chain of steps, direct questions are processed directly to ensure efficiency and minimise processing overhead. Additionally, we leverage multi-source information retrieval and in-context learning to provide rich, relevant context for generating answers. We evaluated our model on the BioCreative IX - MedHopQA Shared Task datasets. Our approach achieves an Exact Match score of 0.84, ranking second on the current leaderboard. These results highlight the model's capability to meet the challenges of Biomedical Question Answering, offering a versatile solution for advancing medical research and practice.", "date": "2026-01-11", "categories": []}
{"id": "2503.18526", "title": "SciClaims: An End-to-End Generative System for Biomedical Claim Analysis", "abstract": "We present SciClaims, an interactive web-based system for end-to-end scientific claim analysis in the biomedical domain. Designed for high-stakes use cases such as systematic literature reviews and patent validation, SciClaims extracts claims from text, retrieves relevant evidence from PubMed, and verifies their veracity. The system features a user-friendly interface where users can input scientific text and view extracted claims, predictions, supporting or refuting evidence, and justifications in natural language. Unlike prior approaches, SciClaims seamlessly integrates the entire scientific claim analysis process using a single large language model, without requiring additional fine-tuning. SciClaims is optimized to run efficiently on a single GPU and is publicly available for live interaction.", "date": "2025-03-24", "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": "2601.04524", "title": "BioPIE: A Biomedical Protocol Information Extraction Dataset for High-Reasoning-Complexity Experiment Question Answer", "abstract": "Question Answer (QA) systems for biomedical experiments facilitate cross-disciplinary communication, and serve as a foundation for downstream tasks, e.g., laboratory automation. High Information Density (HID) and Multi-Step Reasoning (MSR) pose unique challenges for biomedical experimental QA. While extracting structured knowledge, e.g., Knowledge Graphs (KGs), can substantially benefit biomedical experimental QA. Existing biomedical datasets focus on general or coarsegrained knowledge and thus fail to support the fine-grained experimental reasoning demanded by HID and MSR. To address this gap, we introduce Biomedical Protocol Information Extraction Dataset (BioPIE), a dataset that provides procedure-centric KGs of experimental entities, actions, and relations at a scale that supports reasoning over biomedical experiments across protocols. We evaluate information extraction methods on BioPIE, and implement a QA system that leverages BioPIE, showcasing performance gains on test, HID, and MSR question sets, showing that the structured experimental knowledge in BioPIE underpins both AI-assisted and more autonomous biomedical experimentation.", "date": "2026-01-08", "categories": []}
{"id": "2601.04531", "title": "Self-MedRAG: a Self-Reflective Hybrid Retrieval-Augmented Generation Framework for Reliable Medical Question Answering", "abstract": "Large Language Models (LLMs) have demonstrated significant potential in medical Question Answering (QA), yet they remain prone to hallucinations and ungrounded reasoning, limiting their reliability in high-stakes clinical scenarios. While Retrieval-Augmented Generation (RAG) mitigates these issues by incorporating external knowledge, conventional single-shot retrieval often fails to resolve complex biomedical queries requiring multi-step inference. To address this, we propose Self-MedRAG, a self-reflective hybrid framework designed to mimic the iterative hypothesis-verification process of clinical reasoning. Self-MedRAG integrates a hybrid retrieval strategy, combining sparse (BM25) and dense (Contriever) retrievers via Reciprocal Rank Fusion (RRF) to maximize evidence coverage. It employs a generator to produce answers with supporting rationales, which are then assessed by a lightweight self-reflection module using Natural Language Inference (NLI) or LLM-based verification. If the rationale lacks sufficient evidentiary support, the system autonomously reformulates the query and iterates to refine the context. We evaluated Self-MedRAG on the MedQA and PubMedQA benchmarks. The results demonstrate that our hybrid retrieval approach significantly outperforms single-retriever baselines. Furthermore, the inclusion of the self-reflective loop yielded substantial gains, increasing accuracy on MedQA from 80.00% to 83.33% and on PubMedQA from 69.10% to 79.82%. These findings confirm that integrating hybrid retrieval with iterative, evidence-based self-reflection effectively reduces unsupported claims and enhances the clinical reliability of LLM-based systems.", "date": "2026-01-08", "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": "2509.04458", "title": "Predicting Failures of LLMs to Link Biomedical Ontology Terms to Identifiers Evidence Across Models and Ontologies", "abstract": "Large language models often perform well on biomedical NLP tasks but may fail to link ontology terms to their correct identifiers. We investigate why these failures occur by analyzing predictions across two major ontologies, Human Phenotype Ontology and Gene Ontology, and two high-performing models, GPT-4o and LLaMa 3.1 405B. We evaluate nine candidate features related to term familiarity, identifier usage, morphology, and ontology structure. Univariate and multivariate analyses show that exposure to ontology identifiers is the strongest predictor of linking success.", "date": "2025-08-27", "categories": []}
{"id": "2601.02604", "title": "Scalable Construction of a Lung Cancer Knowledge Base: Profiling Semantic Reasoning in LLMs", "abstract": "The integration of Large Language Models (LLMs) into biomedical research offers new opportunities for domainspecific reasoning and knowledge representation. However, their performance depends heavily on the semantic quality of training data. In oncology, where precision and interpretability are vital, scalable methods for constructing structured knowledge bases are essential for effective fine-tuning. This study presents a pipeline for developing a lung cancer knowledge base using Open Information Extraction (OpenIE). The process includes: (1) identifying medical concepts with the MeSH thesaurus; (2) filtering open-access PubMed literature with permissive licenses (CC0); (3) extracting (subject, relation, object) triplets using OpenIE method; and (4) enriching triplet sets with Named Entity Recognition (NER) to ensure biomedical relevance. The resulting triplet sets provide a domain-specific, large-scale, and noise-aware resource for fine-tuning LLMs. We evaluated T5 models finetuned on this dataset through Supervised Semantic Fine-Tuning. Comparative assessments with ROUGE and BERTScore show significantly improved performance and semantic coherence, demonstrating the potential of OpenIE-derived resources as scalable, low-cost solutions for enhancing biomedical NLP.", "date": "2026-01-05", "categories": []}
{"id": "2510.01612", "title": "RAG-BioQA: A Retrieval-Augmented Generation Framework for Long-Form Biomedical Question Answering", "abstract": "The rapidly growth of biomedical literature creates challenges acquiring specific medical information. Current biomedical question-answering systems primarily focus on short-form answers, failing to provide comprehensive explanations necessary for clinical decision-making. We present RAG-BioQA, a retrieval-augmented generation framework for long-form biomedical question answering. Our system integrates BioBERT embeddings with FAISS indexing for retrieval and a LoRA fine-tuned FLAN-T5 model for answer generation. We train on 181k QA pairs from PubMedQA, MedDialog, and MedQuAD, and evaluate on a held-out PubMedQA test set. We compare four retrieval strategies: dense retrieval (FAISS), BM25, ColBERT, and MonoT5. Our results show that domain-adapted dense retrieval outperforms zero-shot neural re-rankers, with the best configuration achieving 0.24 BLEU-1 and 0.29 ROUGE-1. Fine-tuning improves BERTScore by 81\\% over the base model. We release our framework to support reproducible biomedical QA research.", "date": "2025-10-02", "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": "2512.24733", "title": "BIOME-Bench: A Benchmark for Biomolecular Interaction Inference and Multi-Omics Pathway Mechanism Elucidation from Scientific Literature", "abstract": "Multi-omics studies often rely on pathway enrichment to interpret heterogeneous molecular changes, but pathway enrichment (PE)-based workflows inherit structural limitations of pathway resources, including curation lag, functional redundancy, and limited sensitivity to molecular states and interventions. Although recent work has explored using large language models (LLMs) to improve PE-based interpretation, the lack of a standardized benchmark for end-to-end multi-omics pathway mechanism elucidation has largely confined evaluation to small, manually curated datasets or ad hoc case studies, hindering reproducible progress. To address this issue, we introduce BIOME-Bench, constructed via a rigorous four-stage workflow, to evaluate two core capabilities of LLMs in multi-omics analysis: Biomolecular Interaction Inference and end-to-end Multi-Omics Pathway Mechanism Elucidation. We develop evaluation protocols for both tasks and conduct comprehensive experiments across multiple strong contemporary models. Experimental results demonstrate that existing models still exhibit substantial deficiencies in multi-omics analysis, struggling to reliably distinguish fine-grained biomolecular relation types and to generate faithful, robust pathway-level mechanistic explanations.", "date": "2025-12-31", "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": "2508.09323", "title": "Leveraging Large Language Models for Rare Disease Named Entity Recognition", "abstract": "Named Entity Recognition (NER) in the rare disease domain poses unique challenges due to limited labeled data, semantic ambiguity between entity types, and long-tail distributions. In this study, we evaluate the capabilities of GPT-4o for rare disease NER under low-resource settings, using a range of prompt-based strategies including zero-shot prompting, few-shot in-context learning, retrieval-augmented generation (RAG), and task-level fine-tuning. We design a structured prompting framework that encodes domain-specific knowledge and disambiguation rules for four entity types. We further introduce two semantically guided few-shot example selection methods to improve in-context performance while reducing labeling effort. Experiments on the RareDis Corpus show that GPT-4o achieves competitive or superior performance compared to BioClinicalBERT, with task-level fine-tuning yielding the strongest performance among the evaluated approaches and improving upon the previously reported BioClinicalBERT baseline. Cost-performance analysis reveals that few-shot prompting delivers high returns at low token budgets. RAG provides limited overall gains but can improve recall for challenging entity types, especially signs and symptoms. An error taxonomy highlights common failure modes such as boundary drift and type confusion, suggesting opportunities for post-processing and hybrid refinement. Our results demonstrate that prompt-optimized LLMs can serve as effective, scalable alternatives to traditional supervised models in biomedical NER, particularly in rare disease applications where annotated data is scarce.", "date": "2025-08-12", "categories": []}
{"id": "2512.22225", "title": "Literature Mining System for Nutraceutical Biosynthesis: From AI Framework to Biological Insight", "abstract": "The extraction of structured knowledge from scientific literature remains a major bottleneck in nutraceutical research, particularly when identifying microbial strains involved in compound biosynthesis. This study presents a domain-adapted system powered by large language models (LLMs) and guided by advanced prompt engineering techniques to automate the identification of nutraceutical-producing microbes from unstructured scientific text. By leveraging few-shot prompting and tailored query designs, the system demonstrates robust performance across multiple configurations, with DeepSeekV3 outperforming LLaMA2 in accuracy, especially when domain-specific strain information is included. A structured and validated dataset comprising 35 nutraceutical-strain associations was generated, spanning amino acids, fibers, phytochemicals, and vitamins. The results reveal significant microbial diversity across monoculture and co-culture systems, with dominant contributions from Corynebacterium glutamicum, Escherichia coli, and Bacillus subtilis, alongside emerging synthetic consortia. This AI-driven framework not only enhances the scalability and interpretability of literature mining but also provides actionable insights for microbial strain selection, synthetic biology design, and precision fermentation strategies in the production of high-value nutraceuticals.", "date": "2025-12-23", "categories": []}
{"id": "2512.22738", "title": "Harnessing Large Language Models for Biomedical Named Entity Recognition", "abstract": "Background and Objective: Biomedical Named Entity Recognition (BioNER) is a foundational task in medical informatics, crucial for downstream applications like drug discovery and clinical trial matching. However, adapting general-domain Large Language Models (LLMs) to this task is often hampered by their lack of domain-specific knowledge and the performance degradation caused by low-quality training data. To address these challenges, we introduce BioSelectTune, a highly efficient, data-centric framework for fine-tuning LLMs that prioritizes data quality over quantity. Methods and Results: BioSelectTune reformulates BioNER as a structured JSON generation task and leverages our novel Hybrid Superfiltering strategy, a weak-to-strong data curation method that uses a homologous weak model to distill a compact, high-impact training dataset. Conclusions: Through extensive experiments, we demonstrate that BioSelectTune achieves state-of-the-art (SOTA) performance across multiple BioNER benchmarks. Notably, our model, trained on only 50% of the curated positive data, not only surpasses the fully-trained baseline but also outperforms powerful domain-specialized models like BioMedBERT.", "date": "2025-12-28", "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": "2512.23637", "title": "A Dataset and Benchmark for Consumer Healthcare Question Summarization", "abstract": "The quest for seeking health information has swamped the web with consumers health-related questions. Generally, consumers use overly descriptive and peripheral information to express their medical condition or other healthcare needs, contributing to the challenges of natural language understanding. One way to address this challenge is to summarize the questions and distill the key information of the original question. Recently, large-scale datasets have significantly propelled the development of several summarization tasks, such as multi-document summarization and dialogue summarization. However, a lack of a domain-expert annotated dataset for the consumer healthcare questions summarization task inhibits the development of an efficient summarization system. To address this issue, we introduce a new dataset, CHQ-Sum,m that contains 1507 domain-expert annotated consumer health questions and corresponding summaries. The dataset is derived from the community question answering forum and therefore provides a valuable resource for understanding consumer health-related posts on social media. We benchmark the dataset on multiple state-of-the-art summarization models to show the effectiveness of the dataset", "date": "2025-12-29", "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": "2511.21701", "title": "47B Mixture-of-Experts Beats 671B Dense Models on Chinese Medical Examinations", "abstract": "The rapid advancement of large language models(LLMs) has prompted significant interest in their potential applications in medical domains. This paper presents a comprehensive benchmark evaluation of 27 state-of-the-art LLMs on Chinese medical examination questions, encompassing seven medical specialties across two professional levels. We introduce a robust evaluation framework that assesses model performance on 2,800 carefully curated questions from cardiovascular, gastroenterology, hematology, infectious diseases, nephrology, neurology, and respiratory medicine domains. Our dataset distinguishes between attending physician and senior physician difficulty levels, providing nuanced insights into model capabilities across varying complexity. Our empirical analysis reveals substantial performance variations among models, with Mixtral-8x7B achieving the highest overall accuracy of 74.25%, followed by DeepSeek-R1-671B at 64.07%. Notably, we observe no consistent correlation between model size and performance, as evidenced by the strong performance of smaller mixture-of-experts architectures. The evaluation demonstrates significant performance gaps between medical specialties, with models generally performing better on cardiovascular and neurology questions compared to gastroenterology and nephrology domains. Furthermore, our analysis indicates minimal performance degradation between attending and senior physician levels for top-performing models, suggesting robust generalization capabilities. This benchmark provides critical insights for the deployment of LLMs in medical education and clinical decision support systems, highlighting both the promise and current limitations of these technologies in specialized medical contexts.", "date": "2025-11-16", "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": "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": "2501.15108", "title": "Knowledge Hierarchy Guided Biological-Medical Dataset Distillation for Domain LLM Training", "abstract": "The rapid advancement of large language models (LLMs) in biological-medical applications has highlighted a gap between their potential and the limited scale and often low quality of available open-source annotated textual datasets. In addition, the inherent complexity of the biomedical knowledge hierarchy significantly hampers efforts to bridge this gap.Can LLMs themselves play a pivotal role in overcoming this limitation? Motivated by this question, we investigate this challenge in the present study.We propose a framework that automates the distillation of high-quality textual training data from the extensive scientific literature. Our approach self-evaluates and generates questions that are more closely aligned with the biomedical domain, guided by the biomedical knowledge hierarchy through medical subject headings (MeSH). This comprehensive framework establishes an automated workflow, thereby eliminating the need for manual intervention. Furthermore, we conducted comprehensive experiments to evaluate the impact of our framework-generated data on downstream language models of varying sizes. Our approach substantially improves question-answering tasks compared to pre-trained models from the life sciences domain and powerful close-source models represented by GPT-4. Notably, the generated AI-Ready dataset enabled the Llama3-70B base model to outperform GPT-4 using MedPrompt with multiple times the number of parameters. Detailed case studies and ablation experiments underscore the significance of each component within our framework", "date": "2025-01-25", "categories": []}
{"id": "2504.19565", "title": "Knowledge-Driven Agentic Scientific Corpus Distillation Framework for Biomedical Large Language Models Training", "abstract": "Corpus distillation for biomedical large language models (LLMs) seeks to address the pressing challenge of insufficient quantity and quality in open-source annotated scientific corpora, which remains a bottleneck for effective LLM training in biomedical research. This paper proposes a knowledge-driven, agentic framework for scientific corpus distillation, tailored explicitly for LLM training in the biomedical domain, addressing the challenge posed by the complex hierarchy of biomedical knowledge. Central to our approach is a collaborative multi-agent architecture, where specialized agents, each guided by the Medical Subject Headings (MeSH) hierarchy, work in concert to autonomously extract, synthesize, and self-evaluate high-quality textual data from vast scientific literature. This agentic framework collectively generates and refines domain-specific question-answer pairs, ensuring comprehensive coverage and consistency with biomedical ontologies while minimizing manual involvement. Extensive experimental results show that language models trained on our multi-agent distilled datasets achieve notable improvements in biomedical question-answering tasks, outperforming both strong life sciences LLM baselines and advanced proprietary models. Notably, our AI-Ready dataset enables Llama3-70B to surpass GPT-4 with MedPrompt and Med-PaLM-2, despite their larger scale. Detailed ablation studies and case analyses further validate the effectiveness and synergy of each agent within the framework, highlighting the potential of multi-agent collaboration in biomedical LLM training.", "date": "2025-04-28", "categories": []}
{"id": "2512.16530", "title": "Plain language adaptations of biomedical text using LLMs: Comparision of evaluation metrics", "abstract": "This study investigated the application of Large Language Models (LLMs) for simplifying biomedical texts to enhance health literacy. Using a public dataset, which included plain language adaptations of biomedical abstracts, we developed and evaluated several approaches, specifically a baseline approach using a prompt template, a two AI agent approach, and a fine-tuning approach. We selected OpenAI gpt-4o and gpt-4o mini models as baselines for further research. We evaluated our approaches with quantitative metrics, such as Flesch-Kincaid grade level, SMOG Index, SARI, and BERTScore, G-Eval, as well as with qualitative metric, more precisely 5-point Likert scales for simplicity, accuracy, completeness, brevity. Results showed a superior performance of gpt-4o-mini and an underperformance of FT approaches. G-Eval, a LLM based quantitative metric, showed promising results, ranking the approaches similarly as the qualitative metric.", "date": "2025-12-18", "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": "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": "2512.11437", "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": "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": "2512.08617", "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": "2512.07694", "title": "Automated Generation of Custom MedDRA Queries Using SafeTerm Medical Map", "abstract": "In pre-market drug safety review, grouping related adverse event terms into standardised MedDRA queries or the FDA Office of New Drugs Custom Medical Queries (OCMQs) is critical for signal detection. We present a novel quantitative artificial intelligence system that understands and processes medical terminology and automatically retrieves relevant MedDRA Preferred Terms (PTs) for a given input query, ranking them by a relevance score using multi-criteria statistical methods. The system (SafeTerm) embeds medical query terms and MedDRA PTs in a multidimensional vector space, then applies cosine similarity and extreme-value clustering to generate a ranked list of PTs. Validation was conducted against the FDA OCMQ v3.0 (104 queries), restricted to valid MedDRA PTs. Precision, recall and F1 were computed across similarity-thresholds. High recall (>95%) is achieved at moderate thresholds. Higher thresholds improve precision (up to 86%). The optimal threshold (~0.70 - 0.75) yielded recall ~50% and precision ~33%. Narrow-term PT subsets performed similarly but required slightly higher similarity thresholds. The SafeTerm AI-driven system provides a viable supplementary method for automated MedDRA query generation. A similarity threshold of ~0.60 is recommended initially, with increased thresholds for refined term selection.", "date": "2025-12-08", "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": "2512.05537", "title": "Automated Identification of Incidentalomas Requiring Follow-Up: A Multi-Anatomy Evaluation of LLM-Based and Supervised Approaches", "abstract": "Objective: To evaluate large language models (LLMs) against supervised baselines for fine-grained, lesion-level detection of incidentalomas requiring follow-up, addressing the limitations of current document-level classification systems.\n  Methods: We utilized a dataset of 400 annotated radiology reports containing 1,623 verified lesion findings. We compared three supervised transformer-based encoders (BioClinicalModernBERT, ModernBERT, Clinical Longformer) against four generative LLM configurations (Llama 3.1-8B, GPT-4o, GPT-OSS-20b). We introduced a novel inference strategy using lesion-tagged inputs and anatomy-aware prompting to ground model reasoning. Performance was evaluated using class-specific F1-scores.\n  Results: The anatomy-informed GPT-OSS-20b model achieved the highest performance, yielding an incidentaloma-positive macro-F1 of 0.79. This surpassed all supervised baselines (maximum macro-F1: 0.70) and closely matched the inter-annotator agreement of 0.76. Explicit anatomical grounding yielded statistically significant performance gains across GPT-based models (p < 0.05), while a majority-vote ensemble of the top systems further improved the macro-F1 to 0.90. Error analysis revealed that anatomy-aware LLMs demonstrated superior contextual reasoning in distinguishing actionable findings from benign lesions.\n  Conclusion: Generative LLMs, when enhanced with structured lesion tagging and anatomical context, significantly outperform traditional supervised encoders and achieve performance comparable to human experts. This approach offers a reliable, interpretable pathway for automated incidental finding surveillance in radiology workflows.", "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": "2512.03340", "title": "PERCS: Persona-Guided Controllable Biomedical Summarization Dataset", "abstract": "Automatic medical text simplification plays a key role in improving health literacy by making complex biomedical research accessible to diverse readers. However, most existing resources assume a single generic audience, overlooking the wide variation in medical literacy and information needs across user groups. To address this limitation, we introduce PERCS (Persona-guided Controllable Summarization), a dataset of biomedical abstracts paired with summaries tailored to four personas: Laypersons, Premedical Students, Non-medical Researchers, and Medical Experts. These personas represent different levels of medical literacy and information needs, emphasizing the need for targeted, audience-specific summarization. Each summary in PERCS was reviewed by physicians for factual accuracy and persona alignment using a detailed error taxonomy. Technical validation shows clear differences in readability, vocabulary, and content depth across personas. Along with describing the dataset, we benchmark four large language models on PERCS using automatic evaluation metrics that assess comprehensiveness, readability, and faithfulness, establishing baseline results for future research. The dataset, annotation guidelines, and evaluation materials are publicly available to support research on persona-specific communication and controllable biomedical summarization.", "date": "2025-12-03", "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": "2512.02299", "title": "HealthContradict: Evaluating Biomedical Knowledge Conflicts in Language Models", "abstract": "How do language models use contextual information to answer health questions? How are their responses impacted by conflicting contexts? We assess the ability of language models to reason over long, conflicting biomedical contexts using HealthContradict, an expert-verified dataset comprising 920 unique instances, each consisting of a health-related question, a factual answer supported by scientific evidence, and two documents presenting contradictory stances. We consider several prompt settings, including correct, incorrect or contradictory context, and measure their impact on model outputs. Compared to existing medical question-answering evaluation benchmarks, HealthContradict provides greater distinctions of language models' contextual reasoning capabilities. Our experiments show that the strength of fine-tuned biomedical language models lies not only in their parametric knowledge from pretraining, but also in their ability to exploit correct context while resisting incorrect context.", "date": "2025-12-02", "categories": []}
{"id": "2512.02499", "title": "COPE: Chain-Of-Thought Prediction Engine for Open-Source Large Language Model Based Stroke Outcome Prediction from Clinical Notes", "abstract": "Predicting outcomes in acute ischemic stroke (AIS) guides clinical decision-making, patient counseling, and resource allocation. Clinical notes contain rich contextual information, but their unstructured nature limits their use in traditional predictive models. We developed and evaluated the Chain-of-Thought (CoT) Outcome Prediction Engine (COPE), a reasoning-enhanced large language model framework, for predicting 90-day functional outcomes after AIS from unstructured clinical notes. This study included 464 AIS patients with discharge summaries and 90-day modified Rankin Scale (mRS) scores. COPE uses a two-step CoT framework based on sequential open-source LLaMA-3-8B models: the first generates clinical reasoning, and the second outputs an mRS prediction. We compared COPE with GPT-4.1, ClinicalBERT, a structured variable-based machine learning model (Clinical ML), and a single-step LLM without CoT. Performance was evaluated using mean absolute error (MAE), accuracy within +/-1 mRS point, and exact accuracy. COPE achieved an MAE of 1.01 (95% CI 0.92-1.11), +/-1 accuracy of 74.4% (69.9, 78.8%), and exact accuracy of 32.8% (28.0, 37.6%), comparable to GPT-4.1 and superior to ClinicalBERT [MAE 1.24 (1.13-1.36)], Clinical ML [1.28 (1.18-1.39)], and the single-step LLM [1.20 (1.09-1.33)]. Subgroup analyses showed consistent performance across sex and age, with slightly higher error among older patients, those undergoing thrombectomy, and those with longer summaries. These findings demonstrate that COPE, a lightweight, interpretable, and privacy-preserving open-source framework, provides an accurate and practical solution for outcome prediction from unstructured clinical text.", "date": "2025-12-02", "categories": []}
{"id": "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": "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": "2505.12964", "title": "MA-COIR: Leveraging Semantic Search Index and Generative Models for Ontology-Driven Biomedical Concept Recognition", "abstract": "Recognizing biomedical concepts in the text is vital for ontology refinement, knowledge graph construction, and concept relationship discovery. However, traditional concept recognition methods, relying on explicit mention identification, often fail to capture complex concepts not explicitly stated in the text. To overcome this limitation, we introduce MA-COIR, a framework that reformulates concept recognition as an indexing-recognition task. By assigning semantic search indexes (ssIDs) to concepts, MA-COIR resolves ambiguities in ontology entries and enhances recognition efficiency. Using a pretrained BART-based model fine-tuned on small datasets, our approach reduces computational requirements to facilitate adoption by domain experts. Furthermore, we incorporate large language models (LLMs)-generated queries and synthetic data to improve recognition in low-resource settings. Experimental results on three scenarios (CDR, HPO, and HOIP) highlight the effectiveness of MA-COIR in recognizing both explicit and implicit concepts without the need for mention-level annotations during inference, advancing ontology-driven concept recognition in biomedical domain applications. Our code and constructed data are available at https://github.com/sl-633/macoir-master.", "date": "2025-05-19", "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": "2409.09825", "title": "GP-GPT: Large Language Model for Gene-Phenotype Mapping", "abstract": "Pre-trained large language models(LLMs) have attracted increasing attention in biomedical domains due to their success in natural language processing. However, the complex traits and heterogeneity of multi-sources genomics data pose significant challenges when adapting these models to the bioinformatics and biomedical field. To address these challenges, we present GP-GPT, the first specialized large language model for genetic-phenotype knowledge representation and genomics relation analysis. Our model is fine-tuned in two stages on a comprehensive corpus composed of over 3,000,000 terms in genomics, proteomics, and medical genetics, derived from multiple large-scale validated datasets and scientific publications. GP-GPT demonstrates proficiency in accurately retrieving medical genetics information and performing common genomics analysis tasks, such as genomics information retrieval and relationship determination. Comparative experiments across domain-specific tasks reveal that GP-GPT outperforms state-of-the-art LLMs, including Llama2, Llama3 and GPT-4. These results highlight GP-GPT's potential to enhance genetic disease relation research and facilitate accurate and efficient analysis in the fields of genomics and medical genetics. Our investigation demonstrated the subtle changes of bio-factor entities' representations in the GP-GPT, which suggested the opportunities for the application of LLMs to advancing gene-phenotype research.", "date": "2024-09-15", "categories": []}
{"id": "2502.12183", "title": "Leveraging large language models for structured information extraction from pathology reports", "abstract": "Background: Structured information extraction from unstructured histopathology reports facilitates data accessibility for clinical research. Manual extraction by experts is time-consuming and expensive, limiting scalability. Large language models (LLMs) offer efficient automated extraction through zero-shot prompting, requiring only natural language instructions without labeled data or training. We evaluate LLMs' accuracy in extracting structured information from breast cancer histopathology reports, compared to manual extraction by a trained human annotator.\n  Methods: We developed the Medical Report Information Extractor, a web application leveraging LLMs for automated extraction. We developed a gold standard extraction dataset to evaluate the human annotator alongside five LLMs including GPT-4o, a leading proprietary model, and the Llama 3 model family, which allows self-hosting for data privacy. Our assessment involved 111 histopathology reports from the Breast Cancer Now (BCN) Generations Study, extracting 51 pathology features specified in the study's data dictionary.\n  Results: Evaluation against the gold standard dataset showed that both Llama 3.1 405B (94.7% accuracy) and GPT-4o (96.1%) achieved extraction accuracy comparable to the human annotator (95.4%; p = 0.146 and p = 0.106, respectively). While Llama 3.1 70B (91.6%) performed below human accuracy (p <0.001), its reduced computational requirements make it a viable option for self-hosting.\n  Conclusion: We developed an open-source tool for structured information extraction that can be customized by non-programmers using natural language. Its modular design enables reuse for various extraction tasks, producing standardized, structured data from unstructured text reports to facilitate analytics through improved accessibility and interoperability.", "date": "2025-02-14", "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": "2506.19268", "title": "Health App Reviews for Privacy & Trust (HARPT): A Corpus for Analyzing Patient Privacy Concerns, Trust in Providers and Trust in Applications", "abstract": "Background: User reviews of Telehealth and Patient Portal mobile applications (apps) hereon referred to as electronic health (eHealth) apps are a rich source of unsolicited patient feedback, revealing critical insights into patient perceptions. However, the lack of large-scale, annotated datasets specific to privacy and trust has limited the ability of researchers to systematically analyze these concerns using natural language processing (NLP) techniques.\n  Objective: This study aims to develop and benchmark Health App Reviews for Privacy & Trust (HARPT), a large-scale annotated corpus of patient reviews from eHealth apps to advance research in patient privacy and trust.\n  Methods: We employed a multistage data construction strategy. This integrated keyword-based filtering, iterative manual labeling with review, targeted data augmentation, and weak supervision using transformer-based classifiers. A curated subset of 7,000 reviews was manually annotated to support machine learning model development and evaluation. The resulting dataset was used to benchmark a broad range of models.\n  Results: The HARPT corpus comprises 480,000 patient reviews annotated across seven categories capturing critical aspects of trust in the application (TA), trust in the provider (TP), and privacy concerns (PC). We provide comprehensive benchmark performance for a range of machine learning models on the manually annotated subset, establishing a baseline for future research.\n  Conclusions: The HARPT corpus is a significant resource for advancing the study of privacy and trust in the eHealth domain. By providing a large-scale, annotated dataset and initial benchmarks, this work supports reproducible research in usable privacy and trust within health informatics. HARPT is released under an open resource license.", "date": "2025-06-24", "categories": []}
{"id": "2511.04079", "title": "Improving the Performance of Radiology Report De-identification with Large-Scale Training and Benchmarking Against Cloud Vendor Methods", "abstract": "Objective: To enhance automated de-identification of radiology reports by scaling transformer-based models through extensive training datasets and benchmarking performance against commercial cloud vendor systems for protected health information (PHI) detection. Materials and Methods: In this retrospective study, we built upon a state-of-the-art, transformer-based, PHI de-identification pipeline by fine-tuning on two large annotated radiology corpora from Stanford University, encompassing chest X-ray, chest CT, abdomen/pelvis CT, and brain MR reports and introducing an additional PHI category (AGE) into the architecture. Model performance was evaluated on test sets from Stanford and the University of Pennsylvania (Penn) for token-level PHI detection. We further assessed (1) the stability of synthetic PHI generation using a \"hide-in-plain-sight\" method and (2) performance against commercial systems. Precision, recall, and F1 scores were computed across all PHI categories. Results: Our model achieved overall F1 scores of 0.973 on the Penn dataset and 0.996 on the Stanford dataset, outperforming or maintaining the previous state-of-the-art model performance. Synthetic PHI evaluation showed consistent detectability (overall F1: 0.959 [0.958-0.960]) across 50 independently de-identified Penn datasets. Our model outperformed all vendor systems on synthetic Penn reports (overall F1: 0.960 vs. 0.632-0.754). Discussion: Large-scale, multimodal training improved cross-institutional generalization and robustness. Synthetic PHI generation preserved data utility while ensuring privacy. Conclusion: A transformer-based de-identification model trained on diverse radiology datasets outperforms prior academic and commercial systems in PHI detection and establishes a new benchmark for secure clinical text processing.", "date": "2025-11-06", "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": "2504.08764", "title": "Evaluation of the phi-3-mini SLM for identification of texts related to medicine, health, and sports injuries", "abstract": "Small Language Models (SLMs) have potential to be used for automatically labelling and identifying aspects of text data for medicine/health-related purposes from documents and the web. As their resource requirements are significantly lower than Large Language Models (LLMs), these can be deployed potentially on more types of devices. SLMs often are benchmarked on health/medicine-related tasks, such as MedQA, although performance on these can vary especially depending on the size of the model in terms of number of parameters. Furthermore, these test results may not necessarily reflect real-world performance regarding the automatic labelling or identification of texts in documents and the web. As a result, we compared topic-relatedness scores from Microsofts phi-3-mini-4k-instruct SLM to the topic-relatedness scores from 7 human evaluators on 1144 samples of medical/health-related texts and 1117 samples of sports injury-related texts. These texts were from a larger dataset of about 9 million news headlines, each of which were processed and assigned scores by phi-3-mini-4k-instruct. Our sample was selected (filtered) based on 1 (low filtering) or more (high filtering) Boolean conditions on the phi-3 SLM scores. We found low-moderate significant correlations between the scores from the SLM and human evaluators for sports injury texts with low filtering (\\r{ho} = 0.3413, p < 0.001) and medicine/health texts with high filtering (\\r{ho} = 0.3854, p < 0.001), and low significant correlation for medicine/health texts with low filtering (\\r{ho} = 0.2255, p < 0.001). There was negligible, insignificant correlation for sports injury-related texts with high filtering (\\r{ho} = 0.0318, p = 0.4466).", "date": "2025-03-31", "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": "2511.16548", "title": "Utilizing Large Language Models for Zero-Shot Medical Ontology Extension from Clinical Notes", "abstract": "Integrating novel medical concepts and relationships into existing ontologies can significantly enhance their coverage and utility for both biomedical research and clinical applications. Clinical notes, as unstructured documents rich with detailed patient observations, offer valuable context-specific insights and represent a promising yet underutilized source for ontology extension. Despite this potential, directly leveraging clinical notes for ontology extension remains largely unexplored. To address this gap, we propose CLOZE, a novel framework that uses large language models (LLMs) to automatically extract medical entities from clinical notes and integrate them into hierarchical medical ontologies. By capitalizing on the strong language understanding and extensive biomedical knowledge of pre-trained LLMs, CLOZE effectively identifies disease-related concepts and captures complex hierarchical relationships. The zero-shot framework requires no additional training or labeled data, making it a cost-efficient solution. Furthermore, CLOZE ensures patient privacy through automated removal of protected health information (PHI). Experimental results demonstrate that CLOZE provides an accurate, scalable, and privacy-preserving ontology extension framework, with strong potential to support a wide range of downstream applications in biomedical research and clinical informatics.", "date": "2025-11-20", "categories": []}
{"id": "2511.15061", "title": "Beyond GeneGPT: A Multi-Agent Architecture with Open-Source LLMs for Enhanced Genomic Question Answering", "abstract": "Genomic question answering often requires complex reasoning and integration across diverse biomedical sources. GeneGPT addressed this challenge by combining domain-specific APIs with OpenAI's code-davinci-002 large language model to enable natural language interaction with genomic databases. However, its reliance on a proprietary model limits scalability, increases operational costs, and raises concerns about data privacy and generalization.\n  In this work, we revisit and reproduce GeneGPT in a pilot study using open source models, including Llama 3.1, Qwen2.5, and Qwen2.5 Coder, within a monolithic architecture; this allows us to identify the limitations of this approach. Building on this foundation, we then develop OpenBioLLM, a modular multi-agent framework that extends GeneGPT by introducing agent specialization for tool routing, query generation, and response validation. This enables coordinated reasoning and role-based task execution.\n  OpenBioLLM matches or outperforms GeneGPT on over 90% of the benchmark tasks, achieving average scores of 0.849 on Gene-Turing and 0.830 on GeneHop, while using smaller open-source models without additional fine-tuning or tool-specific pretraining. OpenBioLLM's modular multi-agent design reduces latency by 40-50% across benchmark tasks, significantly improving efficiency without compromising model capability. The results of our comprehensive evaluation highlight the potential of open-source multi-agent systems for genomic question answering. Code and resources are available at https://github.com/ielab/OpenBioLLM.", "date": "2025-11-19", "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": "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": "2511.10768", "title": "Faithful Summarization of Consumer Health Queries: A Cross-Lingual Framework with LLMs", "abstract": "Summarizing consumer health questions (CHQs) can ease communication in healthcare, but unfaithful summaries that misrepresent medical details pose serious risks. We propose a framework that combines TextRank-based sentence extraction and medical named entity recognition with large language models (LLMs) to enhance faithfulness in medical text summarization. In our experiments, we fine-tuned the LLaMA-2-7B model on the MeQSum (English) and BanglaCHQ-Summ (Bangla) datasets, achieving consistent improvements across quality (ROUGE, BERTScore, readability) and faithfulness (SummaC, AlignScore) metrics, and outperforming zero-shot baselines and prior systems. Human evaluation further shows that over 80\\% of generated summaries preserve critical medical information. These results highlight faithfulness as an essential dimension for reliable medical summarization and demonstrate the potential of our approach for safer deployment of LLMs in healthcare contexts.", "date": "2025-11-13", "categories": []}
{"id": "2511.10887", "title": "MedPath: Multi-Domain Cross-Vocabulary Hierarchical Paths for Biomedical Entity Linking", "abstract": "Progress in biomedical Named Entity Recognition (NER) and Entity Linking (EL) is currently hindered by a fragmented data landscape, a lack of resources for building explainable models, and the limitations of semantically-blind evaluation metrics. To address these challenges, we present MedPath, a large-scale and multi-domain biomedical EL dataset that builds upon nine existing expert-annotated EL datasets. In MedPath, all entities are 1) normalized using the latest version of the Unified Medical Language System (UMLS), 2) augmented with mappings to 62 other biomedical vocabularies and, crucially, 3) enriched with full ontological paths -- i.e., from general to specific -- in up to 11 biomedical vocabularies. MedPath directly enables new research frontiers in biomedical NLP, facilitating training and evaluation of semantic-rich and interpretable EL systems, and the development of the next generation of interoperable and explainable clinical NLP models.", "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": "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": "2511.10014", "title": "fastbmRAG: A Fast Graph-Based RAG Framework for Efficient Processing of Large-Scale Biomedical Literature", "abstract": "Large language models (LLMs) are rapidly transforming various domains, including biomedicine and healthcare, and demonstrate remarkable potential from scientific research to new drug discovery. Graph-based retrieval-augmented generation (RAG) systems, as a useful application of LLMs, can improve contextual reasoning through structured entity and relationship identification from long-context knowledge, e.g. biomedical literature. Even though many advantages over naive RAGs, most of graph-based RAGs are computationally intensive, which limits their application to large-scale dataset. To address this issue, we introduce fastbmRAG, an fast graph-based RAG optimized for biomedical literature. Utilizing well organized structure of biomedical papers, fastbmRAG divides the construction of knowledge graph into two stages, first drafting graphs using abstracts; and second, refining them using main texts guided by vector-based entity linking, which minimizes redundancy and computational load. Our evaluations demonstrate that fastbmRAG is over 10x faster than existing graph-RAG tools and achieve superior coverage and accuracy to input knowledge. FastbmRAG provides a fast solution for quickly understanding, summarizing, and answering questions about biomedical literature on a large scale. FastbmRAG is public available in https://github.com/menggf/fastbmRAG.", "date": "2025-11-13", "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": "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": "2511.08024", "title": "Knowledge-Augmented Long-CoT Generation for Complex Biomolecular Reasoning", "abstract": "Understanding complex biomolecular mechanisms requires multi-step reasoning across molecular interactions, signaling cascades, and metabolic pathways. While large language models(LLMs) show promise in such tasks, their application to biomolecular problems is hindered by logical inconsistencies and the lack of grounding in domain knowledge. Existing approaches often exacerbate these issues: reasoning steps may deviate from biological facts or fail to capture long mechanistic dependencies. To address these challenges, we propose a Knowledge-Augmented Long-CoT Reasoning framework that integrates LLMs with knowledge graph-based multi-hop reasoning chains. The framework constructs mechanistic chains via guided multi-hop traversal and pruning on the knowledge graph; these chains are then incorporated into supervised fine-tuning to improve factual grounding and further refined with reinforcement learning to enhance reasoning reliability and consistency. Furthermore, to overcome the shortcomings of existing benchmarks, which are often restricted in scale and scope and lack annotations for deep reasoning chains, we introduce PrimeKGQA, a comprehensive benchmark for biomolecular question answering. Experimental results on both PrimeKGQA and existing datasets demonstrate that although larger closed-source models still perform well on relatively simple tasks, our method demonstrates clear advantages as reasoning depth increases, achieving state-of-the-art performance on multi-hop tasks that demand traversal of structured biological knowledge. These findings highlight the effectiveness of combining structured knowledge with advanced reasoning strategies for reliable and interpretable biomolecular reasoning.", "date": "2025-11-11", "categories": []}
{"id": "2511.05498", "title": "Biomedical Hypothesis Explainability with Graph-Based Context Retrieval", "abstract": "We introduce an explainability method for biomedical hypothesis generation systems, built on top of the novel Hypothesis Generation Context Retriever framework. Our approach combines semantic graph-based retrieval and relevant data-restrictive training to simulate real-world discovery constraints. Integrated with large language models (LLMs) via retrieval-augmented generation, the system explains hypotheses with contextual evidence using published scientific literature. We also propose a novel feedback loop approach, which iteratively identifies and corrects flawed parts of LLM-generated explanations, refining both the evidence paths and supporting context. We demonstrate the performance of our method with multiple large language models and evaluate the explanation and context retrieval quality through both expert-curated assessment and large-scale automated analysis. Our code is available at: https://github.com/IlyaTyagin/HGCR.", "date": "2025-09-15", "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": "2511.06418", "title": "How Well Do LLMs Understand Drug Mechanisms? A Knowledge + Reasoning Evaluation Dataset", "abstract": "Two scientific fields showing increasing interest in pre-trained large language models (LLMs) are drug development / repurposing, and personalized medicine. For both, LLMs have to demonstrate factual knowledge as well as a deep understanding of drug mechanisms, so they can recall and reason about relevant knowledge in novel situations. Drug mechanisms of action are described as a series of interactions between biomedical entities, which interlink into one or more chains directed from the drug to the targeted disease. Composing the effects of the interactions in a candidate chain leads to an inference about whether the drug might be useful or not for that disease. We introduce a dataset that evaluates LLMs on both factual knowledge of known mechanisms, and their ability to reason about them under novel situations, presented as counterfactuals that the models are unlikely to have seen during training. Using this dataset, we show that o4-mini outperforms the 4o, o3, and o3-mini models from OpenAI, and the recent small Qwen3-4B-thinking model closely matches o4-mini's performance, even outperforming it in some cases. We demonstrate that the open world setting for reasoning tasks, which requires the model to recall relevant knowledge, is more challenging than the closed world setting where the needed factual knowledge is provided. We also show that counterfactuals affecting internal links in the reasoning chain present a much harder task than those affecting a link from the drug mentioned in the prompt.", "date": "2025-11-09", "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": "2511.04560", "title": "BanglaMedQA and BanglaMMedBench: Evaluating Retrieval-Augmented Generation Strategies for Bangla Biomedical Question Answering", "abstract": "Developing accurate biomedical Question Answering (QA) systems in low-resource languages remains a major challenge, limiting equitable access to reliable medical knowledge. This paper introduces BanglaMedQA and BanglaMMedBench, the first large-scale Bangla biomedical Multiple Choice Question (MCQ) datasets designed to evaluate reasoning and retrieval in medical artificial intelligence (AI). The study applies and benchmarks several Retrieval-Augmented Generation (RAG) strategies, including Traditional, Zero-Shot Fallback, Agentic, Iterative Feedback, and Aggregate RAG, combining textbook-based and web retrieval with generative reasoning to improve factual accuracy. A key novelty lies in integrating a Bangla medical textbook corpus through Optical Character Recognition (OCR) and implementing an Agentic RAG pipeline that dynamically selects between retrieval and reasoning strategies. Experimental results show that the Agentic RAG achieved the highest accuracy 89.54% with openai/gpt-oss-120b, outperforming other configurations and demonstrating superior rationale quality. These findings highlight the potential of RAG-based methods to enhance the reliability and accessibility of Bangla medical QA, establishing a foundation for future research in multilingual medical artificial intelligence.", "date": "2025-11-06", "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": "2505.00814", "title": "Knowledge-augmented Pre-trained Language Models for Biomedical Relation Extraction", "abstract": "Automatic relationship extraction (RE) from biomedical literature is critical for managing the vast amount of scientific knowledge produced each year. In recent years, utilizing pre-trained language models (PLMs) has become the prevalent approach in RE. Several studies report improved performance when incorporating additional context information while fine-tuning PLMs for RE. However, variations in the PLMs applied, the databases used for augmentation, hyper-parameter optimization, and evaluation methods complicate direct comparisons between studies and raise questions about the generalizability of these findings. Our study addresses this research gap by evaluating PLMs enhanced with contextual information on five datasets spanning four relation scenarios within a consistent evaluation framework. We evaluate three baseline PLMs and first conduct extensive hyperparameter optimization. After selecting the top-performing model, we enhance it with additional data, including textual entity descriptions, relational information from knowledge graphs, and molecular structure encodings. Our findings illustrate the importance of i) the choice of the underlying language model and ii) a comprehensive hyperparameter optimization for achieving strong extraction performance. Although inclusion of context information yield only minor overall improvements, an ablation study reveals substantial benefits for smaller PLMs when such external data was included during fine-tuning.", "date": "2025-05-01", "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": "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": "2510.26032", "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": "2510.25087", "title": "BioCoref: Benchmarking Biomedical Coreference Resolution with LLMs", "abstract": "Coreference resolution in biomedical texts presents unique challenges due to complex domain-specific terminology, high ambiguity in mention forms, and long-distance dependencies between coreferring expressions. In this work, we present a comprehensive evaluation of generative large language models (LLMs) for coreference resolution in the biomedical domain. Using the CRAFT corpus as our benchmark, we assess the LLMs' performance with four prompting experiments that vary in their use of local, contextual enrichment, and domain-specific cues such as abbreviations and entity dictionaries. We benchmark these approaches against a discriminative span-based encoder, SpanBERT, to compare the efficacy of generative versus discriminative methods. Our results demonstrate that while LLMs exhibit strong surface-level coreference capabilities, especially when supplemented with domain-grounding prompts, their performance remains sensitive to long-range context and mentions ambiguity. Notably, the LLaMA 8B and 17B models show superior precision and F1 scores under entity-augmented prompting, highlighting the potential of lightweight prompt engineering for enhancing LLM utility in biomedical NLP tasks.", "date": "2025-10-29", "categories": []}
{"id": "2510.19879", "title": "Automated HIV Screening on Dutch Electronic Health Records with Large Language Models", "abstract": "Efficient screening and early diagnosis of HIV are critical for reducing onward transmission. Although large scale laboratory testing is not feasible, the widespread adoption of Electronic Health Records (EHRs) offers new opportunities to address this challenge. Existing research primarily focuses on applying machine learning methods to structured data, such as patient demographics, for improving HIV diagnosis. However, these approaches often overlook unstructured text data such as clinical notes, which potentially contain valuable information relevant to HIV risk. In this study, we propose a novel pipeline that leverages a Large Language Model (LLM) to analyze unstructured EHR text and determine a patient's eligibility for further HIV testing. Experimental results on clinical data from Erasmus University Medical Center Rotterdam demonstrate that our pipeline achieved high accuracy while maintaining a low false negative rate.", "date": "2025-10-22", "categories": []}
{"id": "2510.22285", "title": "Supervised Fine-Tuning or In-Context Learning? Evaluating LLMs for Clinical NER", "abstract": "We study clinical Named Entity Recognition (NER) on the CADEC corpus and compare three families of approaches: (i) BERT-style encoders (BERT Base, BioClinicalBERT, RoBERTa-large), (ii) GPT-4o used with few-shot in-context learning (ICL) under simple vs.\\ complex prompts, and (iii) GPT-4o with supervised fine-tuning (SFT). All models are evaluated on standard NER metrics over CADEC's five entity types (ADR, Drug, Disease, Symptom, Finding). RoBERTa-large and BioClinicalBERT offer limited improvements over BERT Base, showing the limit of these family of models. Among LLM settings, simple ICL outperforms a longer, instruction-heavy prompt, and SFT achieves the strongest overall performance (F1 $\\approx$ 87.1%), albeit with higher cost. We find that the LLM achieve higher accuracy on simplified tasks, restricting classification to two labels.", "date": "2025-10-25", "categories": []}
{"id": "2510.23189", "title": "DREaM: Drug-Drug Relation Extraction via Transfer Learning Method", "abstract": "Relation extraction between drugs plays a crucial role in identifying drug drug interactions and predicting side effects. The advancement of machine learning methods in relation extraction, along with the development of large medical text databases, has enabled the low cost extraction of such relations compared to other approaches that typically require expert knowledge. However, to the best of our knowledge, there are limited datasets specifically designed for drug drug relation extraction currently available. Therefore, employing transfer learning becomes necessary to apply machine learning methods in this domain. In this study, we propose DREAM, a method that first employs a trained relation extraction model to discover relations between entities and then applies this model to a corpus of medical texts to construct an ontology of drug relationships. The extracted relations are subsequently validated using a large language model. Quantitative results indicate that the LLM agreed with 71 of the relations extracted from a subset of PubMed abstracts. Furthermore, our qualitative analysis indicates that this approach can uncover ambiguities in the medical domain, highlighting the challenges inherent in relation extraction in this field.", "date": "2025-10-27", "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": "2510.19036", "title": "From Memorization to Generalization: Fine-Tuning Large Language Models for Biomedical Term-to-Identifier Normalization", "abstract": "Effective biomedical data integration depends on automated term normalization, the mapping of natural language biomedical terms to standardized identifiers. This linking of terms to identifiers is essential for semantic interoperability. Large language models (LLMs) show promise for this task but perform unevenly across terminologies. We evaluated both memorization (training-term performance) and generalization (validation-term performance) across multiple biomedical ontologies. Fine-tuning Llama 3.1 8B revealed marked differences by terminology. GO mappings showed strong memorization gains (up to 77% improvement in term-to-identifier accuracy), whereas HPO showed minimal improvement. Generalization occurred only for protein-gene (GENE) mappings (13.9% gain), while fine-tuning for HPO and GO yielded negligible transfer. Baseline accuracy varied by model scale, with GPT-4o outperforming both Llama variants for all terminologies. Embedding analyses showed tight semantic alignment between gene symbols and protein names but weak alignment between terms and identifiers for GO or HPO, consistent with limited lexicalization. Fine-tuning success depended on two interacting factors: identifier popularity and lexicalization. Popular identifiers were more likely encountered during pretraining, enhancing memorization. Lexicalized identifiers, such as gene symbols, enabled semantic generalization. By contrast, arbitrary identifiers in GO and HPO constrained models to rote learning. These findings provide a predictive framework for when fine-tuning enhances factual recall versus when it fails due to sparse or non-lexicalized identifiers.", "date": "2025-10-21", "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": "2510.18297", "title": "From Retrieval to Generation: Unifying External and Parametric Knowledge for Medical Question Answering", "abstract": "Medical question answering (QA) requires extensive access to domain-specific knowledge. A promising direction is to enhance large language models (LLMs) with external knowledge retrieved from medical corpora or parametric knowledge stored in model parameters. Existing approaches typically fall into two categories: Retrieval-Augmented Generation (RAG), which grounds model reasoning on externally retrieved evidence, and Generation-Augmented Generation (GAG), which depends solely on the models internal knowledge to generate contextual documents. However, RAG often suffers from noisy or incomplete retrieval, while GAG is vulnerable to hallucinated or inaccurate information due to unconstrained generation. Both issues can mislead reasoning and undermine answer reliability. To address these challenges, we propose MedRGAG, a unified retrieval-generation augmented framework that seamlessly integrates external and parametric knowledge for medical QA. MedRGAG comprises two key modules: Knowledge-Guided Context Completion (KGCC), which directs the generator to produce background documents that complement the missing knowledge revealed by retrieval; and Knowledge-Aware Document Selection (KADS), which adaptively selects an optimal combination of retrieved and generated documents to form concise yet comprehensive evidence for answer generation. Extensive experiments on five medical QA benchmarks demonstrate that MedRGAG achieves a 12.5% improvement over MedRAG and a 4.5% gain over MedGENIE, highlighting the effectiveness of unifying retrieval and generation for knowledge-intensive reasoning. Our code and data are publicly available at https://anonymous.4open.science/r/MedRGAG", "date": "2025-10-21", "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": "2510.18556", "title": "Building Trust in Clinical LLMs: Bias Analysis and Dataset Transparency", "abstract": "Large language models offer transformative potential for healthcare, yet their responsible and equitable development depends critically on a deeper understanding of how training data characteristics influence model behavior, including the potential for bias. Current practices in dataset curation and bias assessment often lack the necessary transparency, creating an urgent need for comprehensive evaluation frameworks to foster trust and guide improvements. In this study, we present an in-depth analysis of potential downstream biases in clinical language models, with a focus on differential opioid prescription tendencies across diverse demographic groups, such as ethnicity, gender, and age. As part of this investigation, we introduce HC4: Healthcare Comprehensive Commons Corpus, a novel and extensively curated pretraining dataset exceeding 89 billion tokens. Our evaluation leverages both established general benchmarks and a novel, healthcare-specific methodology, offering crucial insights to support fairness and safety in clinical AI applications.", "date": "2025-10-21", "categories": []}
{"id": "2510.14400", "title": "MedTrust-RAG: Evidence Verification and Trust Alignment for Biomedical Question Answering", "abstract": "Biomedical question answering (QA) requires accurate interpretation of complex medical knowledge. Large language models (LLMs) have shown promising capabilities in this domain, with retrieval-augmented generation (RAG) systems enhancing performance by incorporating external medical literature. However, RAG-based approaches in biomedical QA suffer from hallucinations due to post-retrieval noise and insufficient verification of retrieved evidence, undermining response reliability. We propose MedTrust-Guided Iterative RAG, a framework designed to enhance factual consistency and mitigate hallucinations in medical QA. Our method introduces three key innovations. First, it enforces citation-aware reasoning by requiring all generated content to be explicitly grounded in retrieved medical documents, with structured Negative Knowledge Assertions used when evidence is insufficient. Second, it employs an iterative retrieval-verification process, where a verification agent assesses evidence adequacy and refines queries through Medical Gap Analysis until reliable information is obtained. Third, it integrates the MedTrust-Align Module (MTAM) that combines verified positive examples with hallucination-aware negative samples, leveraging Direct Preference Optimization to reinforce citation-grounded reasoning while penalizing hallucination-prone response patterns.", "date": "2025-10-16", "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": "2304.07805", "title": "EasyNER: A Customizable Easy-to-Use Pipeline for Deep Learning- and Dictionary-based Named Entity Recognition from Medical and Life Science Text", "abstract": "Background Medical and life science research generates millions of publications, and it is a great challenge for researchers to utilize this information in full since its scale and complexity greatly surpasses human reading capabilities. Automated text mining can help extract and connect information spread across this large body of literature, but this technology is not easily accessible to life scientists.\n  Methods and Results Here, we developed an easy-to-use end-to-end pipeline for deep learning- and dictionary-based named entity recognition (NER) of typical entities found in medical and life science research articles, including diseases, cells, chemicals, genes/proteins, species and others. The pipeline can access and process large medical research article collections (PubMed, CORD-19) or raw text and incorporates a series of deep learning models fine-tuned on the HUNER corpora collection. In addition, the pipeline can perform dictionary-based NER related to COVID-19 and other medical topics. Users can also load their own NER models and dictionaries to include additional entities. The output consists of publication-ready ranked lists and graphs of detected entities and files containing the annotated texts. In addition, we provide two accessory scripts which allow processing of files in PubTator format and rapid inspection of the results for specific entities of interest. As model use cases, the pipeline was deployed on two collections of autophagy-related abstracts from PubMed and on the CORD19 dataset, a collection of 764 398 research article abstracts related to COVID-19.\n  Conclusions The NER pipeline we present is applicable in a variety of medical research settings and makes customizable text mining accessible to life scientists.", "date": "2023-04-16", "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": "2510.14332", "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": "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": "2404.04067", "title": "Does Biomedical Training Lead to Better Medical Performance?", "abstract": "Large Language Models (LLMs) are expected to significantly contribute to patient care, diagnostics, and administrative processes. Emerging biomedical LLMs aim to address healthcare-specific challenges, including privacy demands and computational constraints. Assessing the models' suitability for this sensitive application area is of the utmost importance. However, biomedical training has not been systematically evaluated on medical tasks. This study investigates the effect of biomedical training in the context of six practical medical tasks evaluating $25$ models. In contrast to previous evaluations, our results reveal a performance decline in nine out of twelve biomedical models after fine-tuning, particularly on tasks involving hallucinations, ICD10 coding, and instruction adherence. General-domain models like Meta-Llama-3.1-70B-Instruct outperformed their biomedical counterparts, indicating a trade-off between domain-specific fine-tuning and general medical task performance. We open-source all evaluation scripts and datasets at https://github.com/TIO-IKIM/CLUE to support further research in this critical area.", "date": "2024-04-05", "categories": []}
{"id": "2410.15198", "title": "R-GAT: Cancer Document Classification Leveraging Graph-Based Residual Network for Scenarios with Limited Data", "abstract": "Accurate classification of cancer-related biomedical abstracts is critical for advancing cancer informatics and supporting decision-making in healthcare research. Yet progress in this domain is often constrained by limited availability of labeled corpora and the high computational demands of transformer-based approaches. To address these challenges, we propose a Residual Graph Attention Network (R-GAT) that integrates multi-head attention with residual connections to capture semantic and relational dependencies in biomedical texts. Evaluated on a curated dataset of 1,875 PubMed abstracts spanning thyroid, colon, lung, and generic cancer topics, R-GAT achieves stable and competitive performance, comparable to transformer-based models such as BioBERT and BioClinicalBERT and strong classical baselines like Logistic Regression, while requiring significantly fewer computational resources. Ablation studies confirm the importance of attention and residual connections in ensuring robustness under limited-data conditions. To support reproducibility and facilitate future research, we also release the curated dataset. Together, these contributions demonstrate the value of lightweight graph-based architectures as reliable and resource-efficient alternatives to computationally intensive transformers in biomedical NLP.", "date": "2024-10-19", "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": "2510.08588", "title": "Enhancing Biomedical Named Entity Recognition using GLiNER-BioMed with Targeted Dictionary-Based Post-processing for BioASQ 2025 task 6", "abstract": "Biomedical Named Entity Recognition (BioNER), task6 in BioASQ (A challenge in large-scale biomedical semantic indexing and question answering), is crucial for extracting information from scientific literature but faces hurdles such as distinguishing between similar entity types like genes and chemicals. This study evaluates the GLiNER-BioMed model on a BioASQ dataset and introduces a targeted dictionary-based post-processing strategy to address common misclassifications. While this post-processing approach demonstrated notable improvement on our development set, increasing the micro F1-score from a baseline of 0.79 to 0.83, this enhancement did not generalize to the blind test set, where the post-processed model achieved a micro F1-score of 0.77 compared to the baselines 0.79. We also discuss insights gained from exploring alternative methodologies, including Conditional Random Fields. This work highlights the potential of dictionary-based refinement for pre-trained BioNER models but underscores the critical challenge of overfitting to development data and the necessity of ensuring robust generalization for real-world applicability.", "date": "2025-10-03", "categories": []}
{"id": "2510.08902", "title": "A Unified Biomedical Named Entity Recognition Framework with Large Language Models", "abstract": "Accurate recognition of biomedical named entities is critical for medical information extraction and knowledge discovery. However, existing methods often struggle with nested entities, entity boundary ambiguity, and cross-lingual generalization. In this paper, we propose a unified Biomedical Named Entity Recognition (BioNER) framework based on Large Language Models (LLMs). We first reformulate BioNER as a text generation task and design a symbolic tagging strategy to jointly handle both flat and nested entities with explicit boundary annotation. To enhance multilingual and multi-task generalization, we perform bilingual joint fine-tuning across multiple Chinese and English datasets. Additionally, we introduce a contrastive learning-based entity selector that filters incorrect or spurious predictions by leveraging boundary-sensitive positive and negative samples. Experimental results on four benchmark datasets and two unseen corpora show that our method achieves state-of-the-art performance and robust zero-shot generalization across languages. The source codes are freely available at https://github.com/dreamer-tx/LLMNER.", "date": "2025-10-10", "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": "2510.05335", "title": "Biomedical reasoning in action: Multi-agent System for Auditable Biomedical Evidence Synthesis", "abstract": "We present M-Reason, a demonstration system for transparent, agent-based reasoning and evidence integration in the biomedical domain, with a focus on cancer research. M-Reason leverages recent advances in large language models (LLMs) and modular agent orchestration to automate evidence retrieval, appraisal, and synthesis across diverse biomedical data sources. Each agent specializes in a specific evidence stream, enabling parallel processing and fine-grained analysis. The system emphasizes explainability, structured reporting, and user auditability, providing complete traceability from source evidence to final conclusions. We discuss critical tradeoffs between agent specialization, system complexity, and resource usage, as well as the integration of deterministic code for validation. An open, interactive user interface allows researchers to directly observe, explore and evaluate the multi-agent workflow. Our evaluation demonstrates substantial gains in efficiency and output consistency, highlighting M-Reason's potential as both a practical tool for evidence synthesis and a testbed for robust multi-agent LLM systems in scientific research, available at https://m-reason.digitalecmt.com.", "date": "2025-10-06", "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": "2510.04001", "title": "Named Entity Recognition in COVID-19 tweets with Entity Knowledge Augmentation", "abstract": "The COVID-19 pandemic causes severe social and economic disruption around the world, raising various subjects that are discussed over social media. Identifying pandemic-related named entities as expressed on social media is fundamental and important to understand the discussions about the pandemic. However, there is limited work on named entity recognition on this topic due to the following challenges: 1) COVID-19 texts in social media are informal and their annotations are rare and insufficient to train a robust recognition model, and 2) named entity recognition in COVID-19 requires extensive domain-specific knowledge. To address these issues, we propose a novel entity knowledge augmentation approach for COVID-19, which can also be applied in general biomedical named entity recognition in both informal text format and formal text format. Experiments carried out on the COVID-19 tweets dataset and PubMed dataset show that our proposed entity knowledge augmentation improves NER performance in both fully-supervised and few-shot settings. Our source code is publicly available: https://github.com/kkkenshi/LLM-EKA/tree/master", "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": "2510.04671", "title": "FocusMed: A Large Language Model-based Framework for Enhancing Medical Question Summarization with Focus Identification", "abstract": "With the rapid development of online medical platforms, consumer health questions (CHQs) are inefficient in diagnosis due to redundant information and frequent non-professional terms. The medical question summary (MQS) task aims to transform CHQs into streamlined doctors' frequently asked questions (FAQs), but existing methods still face challenges such as poor identification of question focus and model hallucination. This paper explores the potential of large language models (LLMs) in the MQS task and finds that direct fine-tuning is prone to focus identification bias and generates unfaithful content. To this end, we propose an optimization framework based on core focus guidance. First, a prompt template is designed to drive the LLMs to extract the core focus from the CHQs that is faithful to the original text. Then, a fine-tuning dataset is constructed in combination with the original CHQ-FAQ pairs to improve the ability to identify the focus of the question. Finally, a multi-dimensional quality evaluation and selection mechanism is proposed to comprehensively improve the quality of the summary from multiple dimensions. We conduct comprehensive experiments on two widely-adopted MQS datasets using three established evaluation metrics. The proposed framework achieves state-of-the-art performance across all measures, demonstrating a significant boost in the model's ability to identify critical focus of questions and a notable mitigation of hallucinations. The source codes are freely available at https://github.com/DUT-LiuChao/FocusMed.", "date": "2025-10-06", "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": "2304.07437", "title": "Medical Question Summarization with Entity-driven Contrastive Learning", "abstract": "By summarizing longer consumer health questions into shorter and essential ones, medical question-answering systems can more accurately understand consumer intentions and retrieve suitable answers. However, medical question summarization is very challenging due to obvious distinctions in health trouble descriptions from patients and doctors. Although deep learning has been applied to successfully address the medical question summarization (MQS) task, two challenges remain: how to correctly capture question focus to model its semantic intention, and how to obtain reliable datasets to fairly evaluate performance. To address these challenges, this paper proposes a novel medical question summarization framework based on entity-driven contrastive learning (ECL). ECL employs medical entities present in frequently asked questions (FAQs) as focuses and devises an effective mechanism to generate hard negative samples. This approach compels models to focus on essential information and consequently generate more accurate question summaries. Furthermore, we have discovered that some MQS datasets, such as the iCliniq dataset with a 33% duplicate rate, have significant data leakage issues. To ensure an impartial evaluation of the related methods, this paper carefully examines leaked samples to reorganize more reasonable datasets. Extensive experiments demonstrate that our ECL method outperforms the existing methods and achieves new state-of-the-art performance, i.e., 52.85, 43.16, 41.31, 43.52 in terms of ROUGE-1 metric on MeQSum, CHQ-Summ, iCliniq, HealthCareMagic dataset, respectively. The code and datasets are available at https://github.com/yrbobo/MQS-ECL.", "date": "2023-04-15", "categories": []}
{"id": "2503.05750", "title": "CSTRL: Context-Driven Sequential Transfer Learning for Abstractive Radiology Report Summarization", "abstract": "A radiology report comprises several sections, including the Findings and Impression of the diagnosis. Automatically generating the Impression from the Findings is crucial for reducing radiologists' workload and improving diagnostic accuracy. Pretrained models that excel in common abstractive summarization problems encounter challenges when applied to specialized medical domains largely due to the complex terminology and the necessity for accurate clinical context. Such tasks in medical domains demand extracting core information, avoiding context shifts, and maintaining proper flow. Misuse of medical terms can lead to drastic clinical errors. To address these issues, we introduce a sequential transfer learning that ensures key content extraction and coherent summarization. Sequential transfer learning often faces challenges like initial parameter decay and knowledge loss, which we resolve with the Fisher matrix regularization. Using MIMIC-CXR and Open-I datasets, our model, CSTRL - Context-driven Sequential TRansfer Learning - achieved state-of-the-art performance, showing 56.2% improvement in BLEU-1, 40.5% in BLEU-2, 84.3% in BLEU-3, 28.9% in ROUGE-1, 41.0% in ROUGE-2 and 26.5% in ROGUE-3 score over benchmark studies. We also analyze factual consistency scores while preserving the medical context. Our code is publicly available at https://github.com/fahmidahossain/Report_Summarization.", "date": "2025-02-21", "categories": []}
{"id": "2509.26128", "title": "MEDAKA: Construction of Biomedical Knowledge Graphs Using Large Language Models", "abstract": "Knowledge graphs (KGs) are increasingly used to represent biomedical information in structured, interpretable formats. However, existing biomedical KGs often focus narrowly on molecular interactions or adverse events, overlooking the rich data found in drug leaflets. In this work, we present (1) a hackable, end-to-end pipeline to create KGs from unstructured online content using a web scraper and an LLM; and (2) a curated dataset, MEDAKA, generated by applying this method to publicly available drug leaflets. The dataset captures clinically relevant attributes such as side effects, warnings, contraindications, ingredients, dosage guidelines, storage instructions and physical characteristics. We evaluate it through manual inspection and with an LLM-as-a-Judge framework, and compare its coverage with existing biomedical KGs and databases. We expect MEDAKA to support tasks such as patient safety monitoring and drug recommendation. The pipeline can also be used for constructing KGs from unstructured texts in other domains. Code and dataset are available at https://github.com/medakakg/medaka.", "date": "2025-09-30", "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": "2509.22713", "title": "RAR$^2$: Retrieval-Augmented Medical Reasoning via Thought-Driven Retrieval", "abstract": "Large Language Models (LLMs) have shown promising performance on diverse medical benchmarks, highlighting their potential in supporting real-world clinical tasks. Retrieval-Augmented Generation (RAG) has emerged as a key approach for mitigating knowledge gaps and hallucinations by incorporating external medical information. However, RAG still struggles with complex medical questions that require intensive reasoning, as surface-level input often fails to reflect the true knowledge needs of the task. Existing methods typically focus on refining queries without explicitly modeling the reasoning process, limiting their ability to retrieve and integrate clinically relevant knowledge. In this work, we propose RAR$^2$, a joint learning framework that improves both Reasoning-Augmented Retrieval and Retrieval-Augmented Reasoning. RAR$^2$ constructs a thought process to uncover implicit knowledge requirements and uses it to guide retrieval and answer generation. We build a training dataset of mixed preference pairs and apply Direct Preference Optimization (DPO) to train the model. Moreover, we design two test-time scaling strategies to explore the boundaries of our framework. Experiments demonstrate the effectiveness of RAR$^2$ across several biomedical question answering datasets, outperforming RAG baselines with or without fine-tuning.", "date": "2025-09-24", "categories": []}
{"id": "2509.22565", "title": "Retrieval-Augmented Guardrails for AI-Drafted Patient-Portal Messages: Error Taxonomy Construction and Large-Scale Evaluation", "abstract": "Asynchronous patient-clinician messaging via EHR portals is a growing source of clinician workload, prompting interest in large language models (LLMs) to assist with draft responses. However, LLM outputs may contain clinical inaccuracies, omissions, or tone mismatches, making robust evaluation essential. Our contributions are threefold: (1) we introduce a clinically grounded error ontology comprising 5 domains and 59 granular error codes, developed through inductive coding and expert adjudication; (2) we develop a retrieval-augmented evaluation pipeline (RAEC) that leverages semantically similar historical message-response pairs to improve judgment quality; and (3) we provide a two-stage prompting architecture using DSPy to enable scalable, interpretable, and hierarchical error detection. Our approach assesses the quality of drafts both in isolation and with reference to similar past message-response pairs retrieved from institutional archives. Using a two-stage DSPy pipeline, we compared baseline and reference-enhanced evaluations on over 1,500 patient messages. Retrieval context improved error identification in domains such as clinical completeness and workflow appropriateness. Human validation on 100 messages demonstrated superior agreement (concordance = 50% vs. 33%) and performance (F1 = 0.500 vs. 0.256) of context-enhanced labels vs. baseline, supporting the use of our RAEC pipeline as AI guardrails for patient messaging.", "date": "2025-09-26", "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": "2504.21191", "title": "Small or Large? Zero-Shot or Finetuned? Guiding Language Model Choice for Specialized Applications in Healthcare", "abstract": "This study aims to guide language model selection by investigating: 1) the necessity of finetuning versus zero-shot usage, 2) the benefits of domain-adjacent versus generic pretrained models, 3) the value of further domain-specific pretraining, and 4) the continued relevance of Small Language Models (SLMs) compared to Large Language Models (LLMs) for specific tasks. Using electronic pathology reports from the British Columbia Cancer Registry (BCCR), three classification scenarios with varying difficulty and data size are evaluated. Models include various SLMs and an LLM. SLMs are evaluated both zero-shot and finetuned; the LLM is evaluated zero-shot only. Finetuning significantly improved SLM performance across all scenarios compared to their zero-shot results. The zero-shot LLM outperformed zero-shot SLMs but was consistently outperformed by finetuned SLMs. Domain-adjacent SLMs generally performed better than the generic SLM after finetuning, especially on harder tasks. Further domain-specific pretraining yielded modest gains on easier tasks but significant improvements on the complex, data-scarce task. The results highlight the critical role of finetuning for SLMs in specialized domains, enabling them to surpass zero-shot LLM performance on targeted classification tasks. Pretraining on domain-adjacent or domain-specific data provides further advantages, particularly for complex problems or limited finetuning data. While LLMs offer strong zero-shot capabilities, their performance on these specific tasks did not match that of appropriately finetuned SLMs. In the era of LLMs, SLMs remain relevant and effective, offering a potentially superior performance-resource trade-off compared to LLMs.", "date": "2025-04-29", "categories": []}
{"id": "2509.19566", "title": "Nano Bio-Agents (NBA): Small Language Model Agents for Genomics", "abstract": "We investigate the application of Small Language Models (<10 billion parameters) for genomics question answering via agentic framework to address hallucination issues and computational cost challenges. The Nano Bio-Agent (NBA) framework we implemented incorporates task decomposition, tool orchestration, and API access into well-established systems such as NCBI and AlphaGenome. Results show that SLMs combined with such agentic framework can achieve comparable and in many cases superior performance versus existing approaches utilising larger models, with our best model-agent combination achieving 98% accuracy on the GeneTuring benchmark. Notably, small 3-10B parameter models consistently achieve 85-97% accuracy while requiring much lower computational resources than conventional approaches. This demonstrates promising potential for efficiency gains, cost savings, and democratization of ML-powered genomics tools while retaining highly robust and accurate performance.", "date": "2025-09-23", "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": "2509.18843", "title": "Are Smaller Open-Weight LLMs Closing the Gap to Proprietary Models for Biomedical Question Answering?", "abstract": "Open-weight versions of large language models (LLMs) are rapidly advancing, with state-of-the-art models like DeepSeek-V3 now performing comparably to proprietary LLMs. This progression raises the question of whether small open-weight LLMs are capable of effectively replacing larger closed-source models. We are particularly interested in the context of biomedical question-answering, a domain we explored by participating in Task 13B Phase B of the BioASQ challenge. In this work, we compare several open-weight models against top-performing systems such as GPT-4o, GPT-4.1, Claude 3.5 Sonnet, and Claude 3.7 Sonnet. To enhance question answering capabilities, we use various techniques including retrieving the most relevant snippets based on embedding distance, in-context learning, and structured outputs. For certain submissions, we utilize ensemble approaches to leverage the diverse outputs generated by different models for exact-answer questions. Our results demonstrate that open-weight LLMs are comparable to proprietary ones. In some instances, open-weight LLMs even surpassed their closed counterparts, particularly when ensembling strategies were applied. All code is publicly available at https://github.com/evidenceprime/BioASQ-13b.", "date": "2025-09-23", "categories": []}
{"id": "2509.18846", "title": "Model selection meets clinical semantics: Optimizing ICD-10-CM prediction via LLM-as-Judge evaluation, redundancy-aware sampling, and section-aware fine-tuning", "abstract": "Accurate International Classification of Diseases (ICD) coding is critical for clinical documentation, billing, and healthcare analytics, yet it remains a labour-intensive and error-prone task. Although large language models (LLMs) show promise in automating ICD coding, their challenges in base model selection, input contextualization, and training data redundancy limit their effectiveness. We propose a modular framework for ICD-10 Clinical Modification (ICD-10-CM) code prediction that addresses these challenges through principled model selection, redundancy-aware data sampling, and structured input design. The framework integrates an LLM-as-judge evaluation protocol with Plackett-Luce aggregation to assess and rank open-source LLMs based on their intrinsic comprehension of ICD-10-CM code definitions. We introduced embedding-based similarity measures, a redundancy-aware sampling strategy to remove semantically duplicated discharge summaries. We leverage structured discharge summaries from Taiwanese hospitals to evaluate contextual effects and examine section-wise content inclusion under universal and section-specific modelling paradigms. Experiments across two institutional datasets demonstrate that the selected base model after fine-tuning consistently outperforms baseline LLMs in internal and external evaluations. Incorporating more clinical sections consistently improves prediction performance. This study uses open-source LLMs to establish a practical and principled approach to ICD-10-CM code prediction. The proposed framework provides a scalable, institution-ready solution for real-world deployment of automated medical coding systems by combining informed model selection, efficient data refinement, and context-aware prompting.", "date": "2025-09-23", "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": "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": "2509.13706", "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": "2509.13879", "title": "Combining Evidence and Reasoning for Biomedical Fact-Checking", "abstract": "Misinformation in healthcare, from vaccine hesitancy to unproven treatments, poses risks to public health and trust in medical systems. While machine learning and natural language processing have advanced automated fact-checking, validating biomedical claims remains uniquely challenging due to complex terminology, the need for domain expertise, and the critical importance of grounding in scientific evidence. We introduce CER (Combining Evidence and Reasoning), a novel framework for biomedical fact-checking that integrates scientific evidence retrieval, reasoning via large language models, and supervised veracity prediction. By integrating the text-generation capabilities of large language models with advanced retrieval techniques for high-quality biomedical scientific evidence, CER effectively mitigates the risk of hallucinations, ensuring that generated outputs are grounded in verifiable, evidence-based sources. Evaluations on expert-annotated datasets (HealthFC, BioASQ-7b, SciFact) demonstrate state-of-the-art performance and promising cross-dataset generalization. Code and data are released for transparency and reproducibility: https: //github.com/PRAISELab-PicusLab/CER.", "date": "2025-09-17", "categories": []}
{"id": "2509.13888", "title": "Combating Biomedical Misinformation through Multi-modal Claim Detection and Evidence-based Verification", "abstract": "Misinformation in healthcare, from vaccine hesitancy to unproven treatments, poses risks to public health and trust in medical systems. While machine learning and natural language processing have advanced automated fact-checking, validating biomedical claims remains uniquely challenging due to complex terminology, the need for domain expertise, and the critical importance of grounding in scientific evidence. We introduce CER (Combining Evidence and Reasoning), a novel framework for biomedical fact-checking that integrates scientific evidence retrieval, reasoning via large language models, and supervised veracity prediction. By integrating the text-generation capabilities of large language models with advanced retrieval techniques for high-quality biomedical scientific evidence, CER effectively mitigates the risk of hallucinations, ensuring that generated outputs are grounded in verifiable, evidence-based sources. Evaluations on expert-annotated datasets (HealthFC, BioASQ-7b, SciFact) demonstrate state-of-the-art performance and promising cross-dataset generalization. Code and data are released for transparency and reproducibility: https://github.com/PRAISELab-PicusLab/CER", "date": "2025-09-17", "categories": []}
{"id": "2508.20047", "title": "AraHealthQA 2025: The First Shared Task on Arabic Health Question Answering", "abstract": "We introduce AraHealthQA 2025, the Comprehensive Arabic Health Question Answering Shared Task, held in conjunction with ArabicNLP 2025 (co-located with EMNLP 2025). This shared task addresses the paucity of high-quality Arabic medical QA resources by offering two complementary tracks: MentalQA, focusing on Arabic mental health Q&A (e.g., anxiety, depression, stigma reduction), and MedArabiQ, covering broader medical domains such as internal medicine, pediatrics, and clinical decision making. Each track comprises multiple subtasks, evaluation datasets, and standardized metrics, facilitating fair benchmarking. The task was structured to promote modeling under realistic, multilingual, and culturally nuanced healthcare contexts. We outline the dataset creation, task design and evaluation framework, participation statistics, baseline systems, and summarize the overall outcomes. We conclude with reflections on the performance trends observed and prospects for future iterations in Arabic health QA.", "date": "2025-08-27", "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": "2509.11191", "title": "RanAT4BIE: Random Adversarial Training for Biomedical Information Extraction", "abstract": "We introduce random adversarial training (RAT), a novel framework successfully applied to biomedical information extraction (BioIE) tasks. Building on PubMedBERT as the foundational architecture, our study first validates the effectiveness of conventional adversarial training in enhancing pre-trained language models' performance on BioIE tasks. While adversarial training yields significant improvements across various performance metrics, it also introduces considerable computational overhead. To address this limitation, we propose RAT as an efficiency solution for biomedical information extraction. This framework strategically integrates random sampling mechanisms with adversarial training principles, achieving dual objectives: enhanced model generalization and robustness while significantly reducing computational costs. Through comprehensive evaluations, RAT demonstrates superior performance compared to baseline models in BioIE tasks. The results highlight RAT's potential as a transformative framework for biomedical natural language processing, offering a balanced solution to the model performance and computational efficiency.", "date": "2025-09-14", "categories": []}
{"id": "2509.11365", "title": "!MSA at AraHealthQA 2025 Shared Task: Enhancing LLM Performance for Arabic Clinical Question Answering through Prompt Engineering and Ensemble Learning", "abstract": "We present our systems for Track 2 (General Arabic Health QA, MedArabiQ) of the AraHealthQA-2025 shared task, where our methodology secured 2nd place in both Sub-Task 1 (multiple-choice question answering) and Sub-Task 2 (open-ended question answering) in Arabic clinical contexts. For Sub-Task 1, we leverage the Gemini 2.5 Flash model with few-shot prompting, dataset preprocessing, and an ensemble of three prompt configurations to improve classification accuracy on standard, biased, and fill-in-the-blank questions. For Sub-Task 2, we employ a unified prompt with the same model, incorporating role-playing as an Arabic medical expert, few-shot examples, and post-processing to generate concise responses across fill-in-the-blank, patient-doctor Q&A, GEC, and paraphrased variants.", "date": "2025-09-14", "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": "2509.09725", "title": "BIBERT-Pipe on Biomedical Nested Named Entity Linking at BioASQ 2025", "abstract": "Entity linking (EL) for biomedical text is typically benchmarked on English-only corpora with flat mentions, leaving the more realistic scenario of nested and multilingual mentions largely unexplored. We present our system for the BioNNE 2025 Multilingual Biomedical Nested Named Entity Linking shared task (English & Russian), closing this gap with a lightweight pipeline that keeps the original EL model intact and modifies only three task-aligned components: Two-stage retrieval-ranking. We leverage the same base encoder model in both stages: the retrieval stage uses the original pre-trained model, while the ranking stage applies domain-specific fine-tuning. Boundary cues. In the ranking stage, we wrap each mention with learnable [Ms] / [Me] tags, providing the encoder with an explicit, language-agnostic span before robustness to overlap and nesting. Dataset augmentation. We also automatically expand the ranking training corpus with three complementary data sources, enhancing coverage without extra manual annotation. On the BioNNE 2025 leaderboard, our two stage system, bilingual bert (BIBERT-Pipe), ranks third in the multilingual track, demonstrating the effectiveness and competitiveness of these minimal yet principled modifications. Code are publicly available at https://github.com/Kaggle-Competitions-Code/BioNNE-L.", "date": "2025-09-10", "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": "2505.16661", "title": "A Japanese Language Model and Three New Evaluation Benchmarks for Pharmaceutical NLP", "abstract": "We present a Japanese domain-specific language model for the pharmaceutical field, developed through continual pretraining on 2 billion Japanese pharmaceutical tokens and 8 billion English biomedical tokens. To enable rigorous evaluation, we introduce three new benchmarks: YakugakuQA, based on national pharmacist licensing exams; NayoseQA, which tests cross-lingual synonym and terminology normalization; and SogoCheck, a novel task designed to assess consistency reasoning between paired statements. We evaluate our model against both open-source medical LLMs and commercial models, including GPT-4o. Results show that our domain-specific model outperforms existing open models and achieves competitive performance with commercial ones, particularly on terminology-heavy and knowledge-based tasks. Interestingly, even GPT-4o performs poorly on SogoCheck, suggesting that cross-sentence consistency reasoning remains an open challenge. Our benchmark suite offers a broader diagnostic lens for pharmaceutical NLP, covering factual recall, lexical variation, and logical consistency. This work demonstrates the feasibility of building practical, secure, and cost-effective language models for Japanese domain-specific applications, and provides reusable evaluation resources for future research in pharmaceutical and healthcare NLP. Our model, codes, and datasets are released at https://github.com/EQUES-Inc/pharma-LLM-eval.", "date": "2025-05-22", "categories": []}
{"id": "2509.07588", "title": "BALI: Enhancing Biomedical Language Representations through Knowledge Graph and Language Model Alignment", "abstract": "In recent years, there has been substantial progress in using pretrained Language Models (LMs) on a range of tasks aimed at improving the understanding of biomedical texts. Nonetheless, existing biomedical LLMs show limited comprehension of complex, domain-specific concept structures and the factual information encoded in biomedical Knowledge Graphs (KGs). In this work, we propose BALI (Biomedical Knowledge Graph and Language Model Alignment), a novel joint LM and KG pre-training method that augments an LM with external knowledge by the simultaneous learning of a dedicated KG encoder and aligning the representations of both the LM and the graph. For a given textual sequence, we link biomedical concept mentions to the Unified Medical Language System (UMLS) KG and utilize local KG subgraphs as cross-modal positive samples for these mentions. Our empirical findings indicate that implementing our method on several leading biomedical LMs, such as PubMedBERT and BioLinkBERT, improves their performance on a range of language understanding tasks and the quality of entity representations, even with minimal pre-training on a small alignment dataset sourced from PubMed scientific abstracts.", "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": "2509.05505", "title": "Biomedical Literature Q&A System Using Retrieval-Augmented Generation (RAG)", "abstract": "This work presents a Biomedical Literature Question Answering (Q&A) system based on a Retrieval-Augmented Generation (RAG) architecture, designed to improve access to accurate, evidence-based medical information. Addressing the shortcomings of conventional health search engines and the lag in public access to biomedical research, the system integrates diverse sources, including PubMed articles, curated Q&A datasets, and medical encyclopedias ,to retrieve relevant information and generate concise, context-aware responses. The retrieval pipeline uses MiniLM-based semantic embeddings and FAISS vector search, while answer generation is performed by a fine-tuned Mistral-7B-v0.3 language model optimized using QLoRA for efficient, low-resource training. The system supports both general medical queries and domain-specific tasks, with a focused evaluation on breast cancer literature demonstrating the value of domain-aligned retrieval. Empirical results, measured using BERTScore (F1), show substantial improvements in factual consistency and semantic relevance compared to baseline models. The findings underscore the potential of RAG-enhanced language models to bridge the gap between complex biomedical literature and accessible public health knowledge, paving the way for future work on multilingual adaptation, privacy-preserving inference, and personalized medical AI systems.", "date": "2025-09-05", "categories": []}
{"id": "2509.05878", "title": "MedFactEval and MedAgentBrief: A Framework and Workflow for Generating and Evaluating Factual Clinical Summaries", "abstract": "Evaluating factual accuracy in Large Language Model (LLM)-generated clinical text is a critical barrier to adoption, as expert review is unscalable for the continuous quality assurance these systems require. We address this challenge with two complementary contributions. First, we introduce MedFactEval, a framework for scalable, fact-grounded evaluation where clinicians define high-salience key facts and an \"LLM Jury\"--a multi-LLM majority vote--assesses their inclusion in generated summaries. Second, we present MedAgentBrief, a model-agnostic, multi-step workflow designed to generate high-quality, factual discharge summaries. To validate our evaluation framework, we established a gold-standard reference using a seven-physician majority vote on clinician-defined key facts from inpatient cases. The MedFactEval LLM Jury achieved almost perfect agreement with this panel (Cohen's kappa=81%), a performance statistically non-inferior to that of a single human expert (kappa=67%, P < 0.001). Our work provides both a robust evaluation framework (MedFactEval) and a high-performing generation workflow (MedAgentBrief), offering a comprehensive approach to advance the responsible deployment of generative AI in clinical workflows.", "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": "2509.04534", "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": "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": "2412.16701", "title": "AlzheimerRAG: Multimodal Retrieval Augmented Generation for Clinical Use Cases using PubMed articles", "abstract": "Recent advancements in generative AI have fostered the development of highly adept Large Language Models (LLMs) that integrate diverse data types to empower decision-making. Among these, multimodal retrieval-augmented generation (RAG) applications are promising because they combine the strengths of information retrieval and generative models, enhancing their utility across various domains, including clinical use cases. This paper introduces AlzheimerRAG, a Multimodal RAG application for clinical use cases, primarily focusing on Alzheimer's Disease case studies from PubMed articles. This application incorporates cross-modal attention fusion techniques to integrate textual and visual data processing by efficiently indexing and accessing vast amounts of biomedical literature. Our experimental results, compared to benchmarks such as BioASQ and PubMedQA, have yielded improved performance in the retrieval and synthesis of domain-specific information. We also present a case study using our multimodal RAG in various Alzheimer's clinical scenarios. We infer that AlzheimerRAG can generate responses with accuracy non-inferior to humans and with low rates of hallucination.", "date": "2024-12-21", "categories": []}
{"id": "2501.12746", "title": "EvidenceMap: Learning Evidence Analysis to Unleash the Power of Small Language Models for Biomedical Question Answering", "abstract": "When addressing professional questions in the biomedical domain, humans typically acquire multiple pieces of information as evidence and engage in multifaceted analysis to provide high-quality answers. Current LLM-based question answering methods lack a detailed definition and learning process for evidence analysis, leading to the risk of error propagation and hallucinations while using evidence. Although increasing the parameter size of LLMs can alleviate these issues, it also presents challenges in training and deployment with limited resources. In this study, we propose EvidenceMap, which aims to enable a tiny pre-trained language model to explicitly learn multiple aspects of biomedical evidence, including supportive evaluation, logical correlation and content summarization, thereby latently guiding a small generative model (around 3B parameters) to provide textual responses. Experimental results demonstrate that our method, learning evidence analysis by fine-tuning a model with only 66M parameters, exceeds the RAG method with an 8B LLM by 19.9% and 5.7% in reference-based quality and accuracy, respectively.", "date": "2025-01-22", "categories": []}
{"id": "2506.04078", "title": "LLMEval-Med: A Real-world Clinical Benchmark for Medical LLMs with Physician Validation", "abstract": "Evaluating large language models (LLMs) in medicine is crucial because medical applications require high accuracy with little room for error. Current medical benchmarks have three main types: medical exam-based, comprehensive medical, and specialized assessments. However, these benchmarks have limitations in question design (mostly multiple-choice), data sources (often not derived from real clinical scenarios), and evaluation methods (poor assessment of complex reasoning). To address these issues, we present LLMEval-Med, a new benchmark covering five core medical areas, including 2,996 questions created from real-world electronic health records and expert-designed clinical scenarios. We also design an automated evaluation pipeline, incorporating expert-developed checklists into our LLM-as-Judge framework. Furthermore, our methodology validates machine scoring through human-machine agreement analysis, dynamically refining checklists and prompts based on expert feedback to ensure reliability. We evaluate 13 LLMs across three categories (specialized medical models, open-source models, and closed-source models) on LLMEval-Med, providing valuable insights for the safe and effective deployment of LLMs in medical domains. The dataset is released in https://github.com/llmeval/LLMEval-Med.", "date": "2025-06-04", "categories": []}
{"id": "2507.08529", "title": "A Multi-granularity Concept Sparse Activation and Hierarchical Knowledge Graph Fusion Framework for Rare Disease Diagnosis", "abstract": "Despite advances from medical large language models in healthcare, rare-disease diagnosis remains hampered by insufficient knowledge-representation depth, limited concept understanding, and constrained clinical reasoning. We propose a framework that couples multi-granularity sparse activation of medical concepts with a hierarchical knowledge graph. Four complementary matching algorithms, diversity control, and a five-level fallback strategy enable precise concept activation, while a three-layer knowledge graph (taxonomy, clinical features, instances) provides structured, up-to-date context. Experiments on the BioASQ rare-disease QA set show BLEU gains of 0.09, ROUGE gains of 0.05, and accuracy gains of 0.12, with peak accuracy of 0.89 approaching the 0.90 clinical threshold. Expert evaluation confirms improvements in information quality, reasoning, and professional expression, suggesting our approach shortens the \"diagnostic odyssey\" for rare-disease patients.", "date": "2025-07-11", "categories": []}
{"id": "2509.00806", "title": "CaresAI at BioCreative IX Track 1 -- LLM for Biomedical QA", "abstract": "Large language models (LLMs) are increasingly evident for accurate question answering across various domains. However, rigorous evaluation of their performance on complex question-answering (QA) capabilities is essential before deployment in real-world biomedical and healthcare applications. This paper presents our approach to the MedHopQA track of the BioCreative IX shared task, which focuses on multi-hop biomedical question answering involving diseases, genes, and chemicals. We adopt a supervised fine-tuning strategy leveraging LLaMA 3 8B, enhanced with a curated biomedical question-answer dataset compiled from external sources including BioASQ, MedQuAD, and TREC. Three experimental setups are explored: fine-tuning on combined short and long answers, short answers only, and long answers only. While our models demonstrate strong domain understanding, achieving concept-level accuracy scores of up to 0.8, their Exact Match (EM) scores remain significantly lower, particularly in the test phase. We introduce a two-stage inference pipeline for precise short-answer extraction to mitigate verbosity and improve alignment with evaluation metrics. Despite partial improvements, challenges persist in generating strictly formatted outputs. Our findings highlight the gap between semantic understanding and exact answer evaluation in biomedical LLM applications, motivating further research in output control and post-processing strategies.", "date": "2025-08-31", "categories": []}
{"id": "2509.01885", "title": "Extracting OPQRST in Electronic Health Records using Large Language Models with Reasoning", "abstract": "The extraction of critical patient information from Electronic Health Records (EHRs) poses significant challenges due to the complexity and unstructured nature of the data. Traditional machine learning approaches often fail to capture pertinent details efficiently, making it difficult for clinicians to utilize these tools effectively in patient care. This paper introduces a novel approach to extracting the OPQRST assessment from EHRs by leveraging the capabilities of Large Language Models (LLMs). We propose to reframe the task from sequence labeling to text generation, enabling the models to provide reasoning steps that mimic a physician's cognitive processes. This approach enhances interpretability and adapts to the limited availability of labeled data in healthcare settings. Furthermore, we address the challenge of evaluating the accuracy of machine-generated text in clinical contexts by proposing a modification to traditional Named Entity Recognition (NER) metrics. This includes the integration of semantic similarity measures, such as the BERT Score, to assess the alignment between generated text and the clinical intent of the original records. Our contributions demonstrate a significant advancement in the use of AI in healthcare, offering a scalable solution that improves the accuracy and usability of information extraction from EHRs, thereby aiding clinicians in making more informed decisions and enhancing patient care outcomes.", "date": "2025-09-02", "categories": []}
{"id": "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": "2509.02198", "title": "FActBench: A Benchmark for Fine-grained Automatic Evaluation of LLM-Generated Text in the Medical Domain", "abstract": "Large Language Models tend to struggle when dealing with specialized domains. While all aspects of evaluation hold importance, factuality is the most critical one. Similarly, reliable fact-checking tools and data sources are essential for hallucination mitigation. We address these issues by providing a comprehensive Fact-checking Benchmark FActBench covering four generation tasks and six state-of-the-art Large Language Models (LLMs) for the Medical domain. We use two state-of-the-art Fact-checking techniques: Chain-of-Thought (CoT) Prompting and Natural Language Inference (NLI). Our experiments show that the fact-checking scores acquired through the Unanimous Voting of both techniques correlate best with Domain Expert Evaluation.", "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": "2508.20532", "title": "Overview of BioASQ 2024: The twelfth BioASQ challenge on Large-Scale Biomedical Semantic Indexing and Question Answering", "abstract": "This is an overview of the twelfth edition of the BioASQ challenge in the context of the Conference and Labs of the Evaluation Forum (CLEF) 2024. BioASQ is a series of international challenges promoting advances in large-scale biomedical semantic indexing and question answering. This year, BioASQ consisted of new editions of the two established tasks b and Synergy, and two new tasks: a) MultiCardioNER on the adaptation of clinical entity detection to the cardiology domain in a multilingual setting, and b) BIONNE on nested NER in Russian and English. In this edition of BioASQ, 37 competing teams participated with more than 700 distinct submissions in total for the four different shared tasks of the challenge. Similarly to previous editions, most of the participating systems achieved competitive performance, suggesting the continuous advancement of the state-of-the-art in the field.", "date": "2025-08-28", "categories": []}
{"id": "2508.20554", "title": "Overview of BioASQ 2025: The Thirteenth BioASQ Challenge on Large-Scale Biomedical Semantic Indexing and Question Answering", "abstract": "This is an overview of the thirteenth edition of the BioASQ challenge in the context of the Conference and Labs of the Evaluation Forum (CLEF) 2025. BioASQ is a series of international challenges promoting advances in large-scale biomedical semantic indexing and question answering. This year, BioASQ consisted of new editions of the two established tasks, b and Synergy, and four new tasks: a) Task MultiClinSum on multilingual clinical summarization. b) Task BioNNE-L on nested named entity linking in Russian and English. c) Task ELCardioCC on clinical coding in cardiology. d) Task GutBrainIE on gut-brain interplay information extraction. In this edition of BioASQ, 83 competing teams participated with more than 1000 distinct submissions in total for the six different shared tasks of the challenge. Similar to previous editions, several participating systems achieved competitive performance, indicating the continuous advancement of the state-of-the-art in the field.", "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": "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": "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": "2508.16833", "title": "ReProCon: Scalable and Resource-Efficient Few-Shot Biomedical Named Entity Recognition", "abstract": "Named Entity Recognition (NER) in biomedical domains faces challenges due to data scarcity and imbalanced label distributions, especially with fine-grained entity types. We propose ReProCon, a novel few-shot NER framework that combines multi-prototype modeling, cosine-contrastive learning, and Reptile meta-learning to tackle these issues. By representing each category with multiple prototypes, ReProCon captures semantic variability, such as synonyms and contextual differences, while a cosine-contrastive objective ensures strong interclass separation. Reptile meta-updates enable quick adaptation with little data. Using a lightweight fastText + BiLSTM encoder with much lower memory usage, ReProCon achieves a macro-$F_1$ score close to BERT-based baselines (around 99 percent of BERT performance). The model remains stable with a label budget of 30 percent and only drops 7.8 percent in $F_1$ when expanding from 19 to 50 categories, outperforming baselines such as SpanProto and CONTaiNER, which see 10 to 32 percent degradation in Few-NERD. Ablation studies highlight the importance of multi-prototype modeling and contrastive learning in managing class imbalance. Despite difficulties with label ambiguity, ReProCon demonstrates state-of-the-art performance in resource-limited settings, making it suitable for biomedical applications.", "date": "2025-08-22", "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": "2508.15149", "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": "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": "2502.14275", "title": "Fact or Guesswork? Evaluating Large Language Models' Medical Knowledge with Structured One-Hop Judgments", "abstract": "Large language models (LLMs) have been widely adopted in various downstream task domains. However, their abilities to directly recall and apply factual medical knowledge remains under-explored. Most existing medical QA benchmarks assess complex reasoning or multi-hop inference, making it difficult to isolate LLMs' inherent medical knowledge from their reasoning capabilities. Given the high-stakes nature of medical applications, where incorrect information can have critical consequences, it is essential to evaluate the factuality of LLMs to retain medical knowledge.\n  To address this challenge, we introduce the Medical Knowledge Judgment Dataset (MKJ), a dataset derived from the Unified Medical Language System (UMLS), a comprehensive repository of standardized biomedical vocabularies and knowledge graphs. Through a binary classification framework, MKJ evaluates LLMs' grasp of fundamental medical facts by having them assess the validity of concise, one-hop statements, enabling direct measurement of their knowledge retention capabilities.\n  Our experiments reveal that LLMs have difficulty accurately recalling medical facts, with performances varying substantially across semantic types and showing notable weakness in uncommon medical conditions. Furthermore, LLMs show poor calibration, often being overconfident in incorrect answers. To mitigate these issues, we explore retrieval-augmented generation, demonstrating its effectiveness in improving factual accuracy and reducing uncertainty in medical decision-making.", "date": "2025-02-20", "categories": []}
{"id": "2506.08897", "title": "PlantDeBERTa: An Open Source Language Model for Plant Science", "abstract": "The rapid advancement of transformer-based language models has catalyzed breakthroughs in biomedical and clinical natural language processing; however, plant science remains markedly underserved by such domain-adapted tools. In this work, we present PlantDeBERTa, a high-performance, open-source language model specifically tailored for extracting structured knowledge from plant stress-response literature. Built upon the DeBERTa architecture-known for its disentangled attention and robust contextual encoding-PlantDeBERTa is fine-tuned on a meticulously curated corpus of expert-annotated abstracts, with a primary focus on lentil (Lens culinaris) responses to diverse abiotic and biotic stressors. Our methodology combines transformer-based modeling with rule-enhanced linguistic post-processing and ontology-grounded entity normalization, enabling PlantDeBERTa to capture biologically meaningful relationships with precision and semantic fidelity. The underlying corpus is annotated using a hierarchical schema aligned with the Crop Ontology, encompassing molecular, physiological, biochemical, and agronomic dimensions of plant adaptation. PlantDeBERTa exhibits strong generalization capabilities across entity types and demonstrates the feasibility of robust domain adaptation in low-resource scientific fields.By providing a scalable and reproducible framework for high-resolution entity recognition, PlantDeBERTa bridges a critical gap in agricultural NLP and paves the way for intelligent, data-driven systems in plant genomics, phenomics, and agronomic knowledge discovery. Our model is publicly released to promote transparency and accelerate cross-disciplinary innovation in computational plant science.", "date": "2025-06-10", "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": "2508.11894", "title": "QuarkMed Medical Foundation Model Technical Report", "abstract": "Recent advancements in large language models have significantly accelerated their adoption in healthcare applications, including AI-powered medical consultations, diagnostic report assistance, and medical search tools. However, medical tasks often demand highly specialized knowledge, professional accuracy, and customization capabilities, necessitating a robust and reliable foundation model. QuarkMed addresses these needs by leveraging curated medical data processing, medical-content Retrieval-Augmented Generation (RAG), and a large-scale, verifiable reinforcement learning pipeline to develop a high-performance medical foundation model. The model achieved 70% accuracy on the Chinese Medical Licensing Examination, demonstrating strong generalization across diverse medical benchmarks. QuarkMed offers a powerful yet versatile personal medical AI solution, already serving over millions of users at ai.quark.cn.", "date": "2025-08-16", "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": "2508.09995", "title": "zERExtractor:An Automated Platform for Enzyme-Catalyzed Reaction Data Extraction from Scientific Literature", "abstract": "The rapid expansion of enzyme kinetics literature has outpaced the curation capabilities of major biochemical databases, creating a substantial barrier to AI-driven modeling and knowledge discovery. We present zERExtractor, an automated and extensible platform for comprehensive extraction of enzyme-catalyzed reaction and activity data from scientific literature. zERExtractor features a unified, modular architecture that supports plug-and-play integration of state-of-the-art models, including large language models (LLMs), as interchangeable components, enabling continuous system evolution alongside advances in AI. Our pipeline combines domain-adapted deep learning, advanced OCR, semantic entity recognition, and prompt-driven LLM modules, together with human expert corrections, to extract kinetic parameters (e.g., kcat, Km), enzyme sequences, substrate SMILES, experimental conditions, and molecular diagrams from heterogeneous document formats. Through active learning strategies integrating AI-assisted annotation, expert validation, and iterative refinement, the system adapts rapidly to new data sources. We also release a large benchmark dataset comprising over 1,000 annotated tables and 5,000 biological fields from 270 P450-related enzymology publications. Benchmarking demonstrates that zERExtractor consistently outperforms existing baselines in table recognition (Acc 89.9%), molecular image interpretation (up to 99.1%), and relation extraction (accuracy 94.2%). zERExtractor bridges the longstanding data gap in enzyme kinetics with a flexible, plugin-ready framework and high-fidelity extraction, laying the groundwork for future AI-powered enzyme modeling and biochemical knowledge discovery.", "date": "2025-07-30", "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": "2504.00027", "title": "Opioid Named Entity Recognition (ONER-2025) from Reddit", "abstract": "The opioid overdose epidemic remains a critical public health crisis, particularly in the United States, leading to significant mortality and societal costs. Social media platforms like Reddit provide vast amounts of unstructured data that offer insights into public perceptions, discussions, and experiences related to opioid use. This study leverages Natural Language Processing (NLP), specifically Opioid Named Entity Recognition (ONER-2025), to extract actionable information from these platforms. Our research makes four key contributions. First, we created a unique, manually annotated dataset sourced from Reddit, where users share self-reported experiences of opioid use via different administration routes. This dataset contains 331,285 tokens and includes eight major opioid entity categories. Second, we detail our annotation process and guidelines while discussing the challenges of labeling the ONER-2025 dataset. Third, we analyze key linguistic challenges, including slang, ambiguity, fragmented sentences, and emotionally charged language, in opioid discussions. Fourth, we propose a real-time monitoring system to process streaming data from social media, healthcare records, and emergency services to identify overdose events. Using 5-fold cross-validation in 11 experiments, our system integrates machine learning, deep learning, and transformer-based language models with advanced contextual embeddings to enhance understanding. Our transformer-based models (bert-base-NER and roberta-base) achieved 97% accuracy and F1-score, outperforming baselines by 10.23% (RF=0.88).", "date": "2025-03-28", "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": "2508.06504", "title": "Retrieval augmented generation based dynamic prompting for few-shot biomedical named entity recognition using large language models", "abstract": "Biomedical named entity recognition (NER) is a high-utility natural language processing (NLP) task, and large language models (LLMs) show promise particularly in few-shot settings (i.e., limited training data). In this article, we address the performance challenges of LLMs for few-shot biomedical NER by investigating a dynamic prompting strategy involving retrieval-augmented generation (RAG). In our approach, the annotated in-context learning examples are selected based on their similarities with the input texts, and the prompt is dynamically updated for each instance during inference. We implemented and optimized static and dynamic prompt engineering techniques and evaluated them on five biomedical NER datasets. Static prompting with structured components increased average F1-scores by 12% for GPT-4, and 11% for GPT-3.5 and LLaMA 3-70B, relative to basic static prompting. Dynamic prompting further improved performance, with TF-IDF and SBERT retrieval methods yielding the best results, improving average F1-scores by 7.3% and 5.6% in 5-shot and 10-shot settings, respectively. These findings highlight the utility of contextually adaptive prompts via RAG for biomedical NER.", "date": "2025-07-25", "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": "2504.18736", "title": "EvidenceBench: A Benchmark for Extracting Evidence from Biomedical Papers", "abstract": "We study the task of automatically finding evidence relevant to hypotheses in biomedical papers. Finding relevant evidence is an important step when researchers investigate scientific hypotheses. We introduce EvidenceBench to measure models performance on this task, which is created by a novel pipeline that consists of hypothesis generation and sentence-by-sentence annotation of biomedical papers for relevant evidence, completely guided by and faithfully following existing human experts judgment. We demonstrate the pipeline's validity and accuracy with multiple sets of human-expert annotations. We evaluated a diverse set of language models and retrieval systems on the benchmark and found that model performances still fall significantly short of the expert level on this task. To show the scalability of our proposed pipeline, we create a larger EvidenceBench-100k with 107,461 fully annotated papers with hypotheses to facilitate model training and development. Both datasets are available at https://github.com/EvidenceBench/EvidenceBench", "date": "2025-04-25", "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": "2507.19407", "title": "Towards Domain Specification of Embedding Models in Medicine", "abstract": "Medical text embedding models are foundational to a wide array of healthcare applications, ranging from clinical decision support and biomedical information retrieval to medical question answering, yet they remain hampered by two critical shortcomings. First, most models are trained on a narrow slice of medical and biological data, beside not being up to date in terms of methodology, making them ill suited to capture the diversity of terminology and semantics encountered in practice. Second, existing evaluations are often inadequate: even widely used benchmarks fail to generalize across the full spectrum of real world medical tasks.\n  To address these gaps, we leverage MEDTE, a GTE model extensively fine-tuned on diverse medical corpora through self-supervised contrastive learning across multiple data sources, to deliver robust medical text embeddings.\n  Alongside this model, we propose a comprehensive benchmark suite of 51 tasks spanning classification, clustering, pair classification, and retrieval modeled on the Massive Text Embedding Benchmark (MTEB) but tailored to the nuances of medical text. Our results demonstrate that this combined approach not only establishes a robust evaluation framework but also yields embeddings that consistently outperform state of the art alternatives in different tasks.", "date": "2025-07-25", "categories": []}
{"id": "2508.04278", "title": "Large Language Model's Multi-Capability Alignment in Biomedical Domain", "abstract": "BalancedBio is a theoretically grounded framework for parameter-efficient biomedical reasoning, addressing multi-capability integration in domain-specific AI alignment. It establishes the Biomedical Multi-Capability Convergence Theorem, proving orthogonal gradient spaces are essential to prevent capability interference for safe deployment. Key innovations include: (1) Medical Knowledge Grounded Synthetic Generation (MKGSG), extending Source2Synth with clinical workflow constraints and medical ontology validation for factual accuracy and safety; and (2) Capability Aware Group Relative Policy Optimization, deriving optimal hybrid reward weighting to maintain orthogonality in RL, using a reward model with rule-based and model-based scores adapted to biomedical tasks. Mathematical analysis proves Pareto-optimal convergence, preserving performance across capabilities. It achieves state-of-the-art results in its parameter class: domain expertise (80.95% BIOMED-MMLU, +15.32% over baseline), reasoning (61.94%, +7.75%), instruction following (67.95%, +6.44%), and integration (86.7%, +18.5%). Theoretical safety guarantees include bounds on capability preservation and clinical accuracy. Real-world deployment yields 78% cost reduction, 23% improved diagnostic accuracy, and 89% clinician acceptance. This work provides a principled methodology for biomedical AI alignment, enabling efficient reasoning with essential safety and reliability, with the 0.5B model version to be released.", "date": "2025-08-06", "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": "2408.16493", "title": "Learning from Negative Samples in Biomedical Generative Entity Linking", "abstract": "Generative models have become widely used in biomedical entity linking (BioEL) due to their excellent performance and efficient memory usage. However, these models are usually trained only with positive samples, i.e., entities that match the input mention's identifier, and do not explicitly learn from hard negative samples, which are entities that look similar but have different meanings. To address this limitation, we introduce ANGEL (Learning from Negative Samples in Biomedical Generative Entity Linking), the first framework that trains generative BioEL models using negative samples. Specifically, a generative model is initially trained to generate positive entity names from the knowledge base for given input entities. Subsequently, both correct and incorrect outputs are gathered from the model's top-k predictions. Finally, the model is updated to prioritize the correct predictions through preference optimization. Our models outperform the previous best baseline models by up to an average top-1 accuracy of 1.4% on five benchmarks. When incorporating our framework into pre-training, the performance improvement increases further to 1.7%, demonstrating its effectiveness in both the pre-training and fine-tuning stages. The code and model weights are available at https://github.com/dmis-lab/ANGEL.", "date": "2024-08-29", "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": "2508.01480", "title": "Harnessing Collective Intelligence of LLMs for Robust Biomedical QA: A Multi-Model Approach", "abstract": "Biomedical text mining and question-answering are essential yet highly demanding tasks, particularly in the face of the exponential growth of biomedical literature. In this work, we present our participation in the 13th edition of the BioASQ challenge, which involves biomedical semantic question-answering for Task 13b and biomedical question-answering for developing topics for the Synergy task. We deploy a selection of open-source large language models (LLMs) as retrieval-augmented generators to answer biomedical questions. Various models are used to process the questions. A majority voting system combines their output to determine the final answer for Yes/No questions, while for list and factoid type questions, the union of their answers in used. We evaluated 13 state-of-the-art open source LLMs, exploring all possible model combinations to contribute to the final answer, resulting in tailored LLM pipelines for each question type. Our findings provide valuable insight into which combinations of LLMs consistently produce superior results for specific question types. In the four rounds of the 2025 BioASQ challenge, our system achieved notable results: in the Synergy task, we secured 1st place for ideal answers and 2nd place for exact answers in round 2, as well as two shared 1st places for exact answers in round 3 and 4.", "date": "2025-08-02", "categories": []}
{"id": "2508.01630", "title": "OpenMed NER: Open-Source, Domain-Adapted State-of-the-Art Transformers for Biomedical NER Across 12 Public Datasets", "abstract": "Named-entity recognition (NER) is fundamental to extracting structured information from the >80% of healthcare data that resides in unstructured clinical notes and biomedical literature. Despite recent advances with large language models, achieving state-of-the-art performance across diverse entity types while maintaining computational efficiency remains a significant challenge. We introduce OpenMed NER, a suite of open-source, domain-adapted transformer models that combine lightweight domain-adaptive pre-training (DAPT) with parameter-efficient Low-Rank Adaptation (LoRA). Our approach performs cost-effective DAPT on a 350k-passage corpus compiled from ethically sourced, publicly available research repositories and de-identified clinical notes (PubMed, arXiv, and MIMIC-III) using DeBERTa-v3, PubMedBERT, and BioELECTRA backbones. This is followed by task-specific fine-tuning with LoRA, which updates less than 1.5% of model parameters. We evaluate our models on 12 established biomedical NER benchmarks spanning chemicals, diseases, genes, and species. OpenMed NER achieves new state-of-the-art micro-F1 scores on 10 of these 12 datasets, with substantial gains across diverse entity types. Our models advance the state-of-the-art on foundational disease and chemical benchmarks (e.g., BC5CDR-Disease, +2.70 pp), while delivering even larger improvements of over 5.3 and 9.7 percentage points on more specialized gene and clinical cell line corpora. This work demonstrates that strategically adapted open-source models can surpass closed-source solutions. This performance is achieved with remarkable efficiency: training completes in under 12 hours on a single GPU with a low carbon footprint (< 1.2 kg CO2e), producing permissively licensed, open-source checkpoints designed to help practitioners facilitate compliance with emerging data protection and AI regulations, such as the EU AI Act.", "date": "2025-08-03", "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": "2508.02556", "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": "2508.00081", "title": "Rethinking Evidence Hierarchies in Medical Language Benchmarks: A Critical Evaluation of HealthBench", "abstract": "HealthBench, a benchmark designed to measure the capabilities of AI systems for health better (Arora et al., 2025), has advanced medical language model evaluation through physician-crafted dialogues and transparent rubrics. However, its reliance on expert opinion, rather than high-tier clinical evidence, risks codifying regional biases and individual clinician idiosyncrasies, further compounded by potential biases in automated grading systems. These limitations are particularly magnified in low- and middle-income settings, where issues like sparse neglected tropical disease coverage and region-specific guideline mismatches are prevalent.\n  The unique challenges of the African context, including data scarcity, inadequate infrastructure, and nascent regulatory frameworks, underscore the urgent need for more globally relevant and equitable benchmarks. To address these shortcomings, we propose anchoring reward functions in version-controlled Clinical Practice Guidelines (CPGs) that incorporate systematic reviews and GRADE evidence ratings.\n  Our roadmap outlines \"evidence-robust\" reinforcement learning via rubric-to-guideline linkage, evidence-weighted scoring, and contextual override logic, complemented by a focus on ethical considerations and the integration of delayed outcome feedback. By re-grounding rewards in rigorously vetted CPGs, while preserving HealthBench's transparency and physician engagement, we aim to foster medical language models that are not only linguistically polished but also clinically trustworthy, ethically sound, and globally relevant.", "date": "2025-07-31", "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": "2507.22798", "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": "2507.21302", "title": "Can human clinical rationales improve the performance and explainability of clinical text classification models?", "abstract": "AI-driven clinical text classification is vital for explainable automated retrieval of population-level health information. This work investigates whether human-based clinical rationales can serve as additional supervision to improve both performance and explainability of transformer-based models that automatically encode clinical documents. We analyzed 99,125 human-based clinical rationales that provide plausible explanations for primary cancer site diagnoses, using them as additional training samples alongside 128,649 electronic pathology reports to evaluate transformer-based models for extracting primary cancer sites. We also investigated sufficiency as a way to measure rationale quality for pre-selecting rationales. Our results showed that clinical rationales as additional training data can improve model performance in high-resource scenarios but produce inconsistent behavior when resources are limited. Using sufficiency as an automatic metric to preselect rationales also leads to inconsistent results. Importantly, models trained on rationales were consistently outperformed by models trained on additional reports instead. This suggests that clinical rationales don't consistently improve model performance and are outperformed by simply using more reports. Therefore, if the goal is optimizing accuracy, annotation efforts should focus on labeling more reports rather than creating rationales. However, if explainability is the priority, training models on rationale-supplemented data may help them better identify rationale-like features. We conclude that using clinical rationales as additional training data results in smaller performance improvements and only slightly better explainability (measured as average token-level rationale coverage) compared to training on additional reports.", "date": "2025-07-28", "categories": []}
{"id": "2507.21980", "title": "Predicting Microbial Ontology and Pathogen Risk from Environmental Metadata with Large Language Models", "abstract": "Traditional machine learning models struggle to generalize in microbiome studies where only metadata is available, especially in small-sample settings or across studies with heterogeneous label formats. In this work, we explore the use of large language models (LLMs) to classify microbial samples into ontology categories such as EMPO 3 and related biological labels, as well as to predict pathogen contamination risk, specifically the presence of E. Coli, using environmental metadata alone. We evaluate LLMs such as ChatGPT-4o, Claude 3.7 Sonnet, Grok-3, and LLaMA 4 in zero-shot and few-shot settings, comparing their performance against traditional models like Random Forests across multiple real-world datasets. Our results show that LLMs not only outperform baselines in ontology classification, but also demonstrate strong predictive ability for contamination risk, generalizing across sites and metadata distributions. These findings suggest that LLMs can effectively reason over sparse, heterogeneous biological metadata and offer a promising metadata-only approach for environmental microbiology and biosurveillance applications.", "date": "2025-07-29", "categories": []}
{"id": "2407.19299", "title": "The Impact of LoRA Adapters on LLMs for Clinical Text Classification Under Computational and Data Constraints", "abstract": "Fine-tuning Large Language Models (LLMs) for clinical Natural Language Processing (NLP) poses significant challenges due to domain gap, limited data, and stringent hardware constraints. In this study, we evaluate four adapter techniques-Adapter, Lightweight, TinyAttention, and Gated Residual Network (GRN) - equivalent to Low-Rank Adaptation (LoRA), for clinical note classification under real-world, resource-constrained conditions. All experiments were conducted on a single NVIDIA Quadro P620 GPU (2 GB VRAM, 512 CUDA cores, 1.386 TFLOPS FP32), limiting batch sizes to <8 sequences and maximum sequence length to 256 tokens. Our clinical corpus comprises only 580 000 tokens, several orders of magnitude smaller than standard LLM pre-training datasets. We fine-tuned three biomedical pre-trained LLMs (CamemBERT-bio, AliBERT, DrBERT) and two lightweight Transformer models trained from scratch. Results show that 1) adapter structures provide no consistent gains when fine-tuning biomedical LLMs under these constraints, and 2) simpler Transformers, with minimal parameter counts and training times under six hours, outperform adapter-augmented LLMs, which required over 1000 GPU-hours. Among adapters, GRN achieved the best metrics (accuracy, precision, recall, F1 = 0.88). These findings demonstrate that, in low-resource clinical settings with limited data and compute, lightweight Transformers trained from scratch offer a more practical and efficient solution than large LLMs, while GRN remains a viable adapter choice when minimal adaptation is needed.", "date": "2024-07-27", "categories": []}
{"id": "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": "2507.19973", "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": "2311.13729", "title": "Comparison of pipeline, sequence-to-sequence, and GPT models for end-to-end relation extraction: experiments with the rare disease use-case", "abstract": "End-to-end relation extraction (E2ERE) is an important and realistic application of natural language processing (NLP) in biomedicine. In this paper, we aim to compare three prevailing paradigms for E2ERE using a complex dataset focused on rare diseases involving discontinuous and nested entities. We use the RareDis information extraction dataset to evaluate three competing approaches (for E2ERE): NER $\\rightarrow$ RE pipelines, joint sequence to sequence models, and generative pre-trained transformer (GPT) models. We use comparable state-of-the-art models and best practices for each of these approaches and conduct error analyses to assess their failure modes. Our findings reveal that pipeline models are still the best, while sequence-to-sequence models are not far behind; GPT models with eight times as many parameters are worse than even sequence-to-sequence models and lose to pipeline models by over 10 F1 points. Partial matches and discontinuous entities caused many NER errors contributing to lower overall E2E performances. We also verify these findings on a second E2ERE dataset for chemical-protein interactions. Although generative LM-based methods are more suitable for zero-shot settings, when training data is available, our results show that it is better to work with more conventional models trained and tailored for E2ERE. More innovative methods are needed to marry the best of the both worlds from smaller encoder-decoder pipeline models and the larger GPT models to improve E2ERE. As of now, we see that well designed pipeline models offer substantial performance gains at a lower cost and carbon footprint for E2ERE. Our contribution is also the first to conduct E2ERE for the RareDis dataset.", "date": "2023-11-22", "categories": []}
{"id": "2402.13470", "title": "How Important is Domain Specificity in Language Models and Instruction Finetuning for Biomedical Relation Extraction?", "abstract": "Cutting edge techniques developed in the general NLP domain are often subsequently applied to the high-value, data-rich biomedical domain. The past few years have seen generative language models (LMs), instruction finetuning, and few-shot learning become foci of NLP research. As such, generative LMs pretrained on biomedical corpora have proliferated and biomedical instruction finetuning has been attempted as well, all with the hope that domain specificity improves performance on downstream tasks. Given the nontrivial effort in training such models, we investigate what, if any, benefits they have in the key biomedical NLP task of relation extraction. Specifically, we address two questions: (1) Do LMs trained on biomedical corpora outperform those trained on general domain corpora? (2) Do models instruction finetuned on biomedical datasets outperform those finetuned on assorted datasets or those simply pretrained? We tackle these questions using existing LMs, testing across four datasets. In a surprising result, general-domain models typically outperformed biomedical-domain models. However, biomedical instruction finetuning improved performance to a similar degree as general instruction finetuning, despite having orders of magnitude fewer instructions. Our findings suggest it may be more fruitful to focus research effort on larger-scale biomedical instruction finetuning of general LMs over building domain-specific biomedical LMs", "date": "2024-02-21", "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": "2507.18123", "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": "2507.14096", "title": "Lessons from the TREC Plain Language Adaptation of Biomedical Abstracts (PLABA) track", "abstract": "Objective: Recent advances in language models have shown potential to adapt professional-facing biomedical literature to plain language, making it accessible to patients and caregivers. However, their unpredictability, combined with the high potential for harm in this domain, means rigorous evaluation is necessary. Our goals with this track were to stimulate research and to provide high-quality evaluation of the most promising systems.\n  Methods: We hosted the Plain Language Adaptation of Biomedical Abstracts (PLABA) track at the 2023 and 2024 Text Retrieval Conferences. Tasks included complete, sentence-level, rewriting of abstracts (Task 1) as well as identifying and replacing difficult terms (Task 2). For automatic evaluation of Task 1, we developed a four-fold set of professionally-written references. Submissions for both Tasks 1 and 2 were provided extensive manual evaluation from biomedical experts.\n  Results: Twelve teams spanning twelve countries participated in the track, with models from multilayer perceptrons to large pretrained transformers. In manual judgments of Task 1, top-performing models rivaled human levels of factual accuracy and completeness, but not simplicity or brevity. Automatic, reference-based metrics generally did not correlate well with manual judgments. In Task 2, systems struggled with identifying difficult terms and classifying how to replace them. When generating replacements, however, LLM-based systems did well in manually judged accuracy, completeness, and simplicity, though not in brevity.\n  Conclusion: The PLABA track showed promise for using Large Language Models to adapt biomedical literature for the general public, while also highlighting their deficiencies and the need for improved automatic benchmarking tools.", "date": "2025-07-18", "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": "2507.14374", "title": "Error-Aware Curriculum Learning for Biomedical Relation Classification", "abstract": "Relation Classification (RC) in biomedical texts is essential for constructing knowledge graphs and enabling applications such as drug repurposing and clinical decision-making. We propose an error-aware teacher--student framework that improves RC through structured guidance from a large language model (GPT-4o). Prediction failures from a baseline student model are analyzed by the teacher to classify error types, assign difficulty scores, and generate targeted remediations, including sentence rewrites and suggestions for KG-based enrichment. These enriched annotations are used to train a first student model via instruction tuning. This model then annotates a broader dataset with difficulty scores and remediation-enhanced inputs. A second student is subsequently trained via curriculum learning on this dataset, ordered by difficulty, to promote robust and progressive learning. We also construct a heterogeneous biomedical knowledge graph from PubMed abstracts to support context-aware RC. Our approach achieves new state-of-the-art performance on 4 of 5 PPI datasets and the DDI dataset, while remaining competitive on ChemProt.", "date": "2025-07-18", "categories": []}
{"id": "2507.15275", "title": "ChiMed 2.0: Advancing Chinese Medical Dataset in Facilitating Large Language Modeling", "abstract": "Building high-quality data resources is crucial for advancing artificial intelligence research and applications in specific domains, particularly in the Chinese medical domain. Existing Chinese medical datasets are limited in size and narrow in domain coverage, falling short of the diverse corpora required for effective pre-training. Moreover, most datasets are designed solely for LLM fine-tuning and do not support pre-training and reinforcement learning from human feedback (RLHF). In this paper, we propose a Chinese medical dataset named ChiMed 2.0, which extends our previous work ChiMed, and covers data collected from Chinese medical online platforms and generated by LLMs. ChiMed 2.0 contains 204.4M Chinese characters covering both traditional Chinese medicine classics and modern general medical data, where there are 164.8K documents for pre-training, 351.6K question-answering pairs for supervised fine-tuning (SFT), and 41.7K preference data tuples for RLHF. To validate the effectiveness of our approach for training a Chinese medical LLM, we conduct further pre-training, SFT, and RLHF experiments on representative general domain LLMs and evaluate their performance on medical benchmark datasets. The results show performance gains across different model scales, validating the dataset's effectiveness and applicability.", "date": "2025-07-21", "categories": []}
{"id": "2507.14045", "title": "Evaluating the Effectiveness of Cost-Efficient Large Language Models in Benchmark Biomedical Tasks", "abstract": "This paper presents a comprehensive evaluation of cost-efficient Large Language Models (LLMs) for diverse biomedical tasks spanning both text and image modalities. We evaluated a range of closed-source and open-source LLMs on tasks such as biomedical text classification and generation, question answering, and multimodal image processing. Our experimental findings indicate that there is no single LLM that can consistently outperform others across all tasks. Instead, different LLMs excel in different tasks. While some closed-source LLMs demonstrate strong performance on specific tasks, their open-source counterparts achieve comparable results (sometimes even better), with additional benefits like faster inference and enhanced privacy. Our experimental results offer valuable insights for selecting models that are optimally suited for specific biomedical applications.", "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": "2507.11052", "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": "2507.08916", "title": "Evaluating LLMs in Medicine: A Call for Rigor, Transparency", "abstract": "Objectives: To evaluate the current limitations of large language models (LLMs) in medical question answering, focusing on the quality of datasets used for their evaluation. Materials and Methods: Widely-used benchmark datasets, including MedQA, MedMCQA, PubMedQA, and MMLU, were reviewed for their rigor, transparency, and relevance to clinical scenarios. Alternatives, such as challenge questions in medical journals, were also analyzed to identify their potential as unbiased evaluation tools. Results: Most existing datasets lack clinical realism, transparency, and robust validation processes. Publicly available challenge questions offer some benefits but are limited by their small size, narrow scope, and exposure to LLM training. These gaps highlight the need for secure, comprehensive, and representative datasets. Conclusion: A standardized framework is critical for evaluating LLMs in medicine. Collaborative efforts among institutions and policymakers are needed to ensure datasets and methodologies are rigorous, unbiased, and reflective of clinical complexities.", "date": "2025-07-11", "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": "2507.07599", "title": "Enhancing Vaccine Safety Surveillance: Extracting Vaccine Mentions from Emergency Department Triage Notes Using Fine-Tuned Large Language Models", "abstract": "This study evaluates fine-tuned Llama 3.2 models for extracting vaccine-related information from emergency department triage notes to support near real-time vaccine safety surveillance. Prompt engineering was used to initially create a labeled dataset, which was then confirmed by human annotators. The performance of prompt-engineered models, fine-tuned models, and a rule-based approach was compared. The fine-tuned Llama 3 billion parameter model outperformed other models in its accuracy of extracting vaccine names. Model quantization enabled efficient deployment in resource-constrained environments. Findings demonstrate the potential of large language models in automating data extraction from emergency department notes, supporting efficient vaccine safety surveillance and early detection of emerging adverse events following immunization issues.", "date": "2025-07-10", "categories": []}
{"id": "2507.06715", "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": "2507.05577", "title": "Beyond Retrieval: Ensembling Cross-Encoders and GPT Rerankers with LLMs for Biomedical QA", "abstract": "Biomedical semantic question answering rooted in information retrieval can play a crucial role in keeping up to date with vast, rapidly evolving and ever-growing biomedical literature. A robust system can help researchers, healthcare professionals and even layman users access relevant knowledge grounded in evidence. The BioASQ 2025 Task13b Challenge serves as an important benchmark, offering a competitive platform for advancement of this space. This paper presents the methodologies and results from our participation in this challenge where we built a Retrieval-Augmented Generation (RAG) system that can answer biomedical questions by retrieving relevant PubMed documents and snippets to generate answers. For the retrieval task, we generated dense embeddings from biomedical articles for initial retrieval, and applied an ensemble of finetuned cross-encoders and large language models (LLMs) for re-ranking to identify top relevant documents. Our solution achieved an MAP@10 of 0.1581, placing 10th on the leaderboard for the retrieval task. For answer generation, we employed few-shot prompting of instruction-tuned LLMs. Our system achieved macro-F1 score of 0.95 for yes/no questions (rank 12), Mean Reciprocal Rank (MRR) of 0.64 for factoid questions (rank 1), mean-F1 score of 0.63 for list questions (rank 5), and ROUGE-SU4 F1 score of 0.29 for ideal answers (rank 11).", "date": "2025-07-08", "categories": []}
{"id": "2507.02975", "title": "Introducing Answered with Evidence -- a framework for evaluating whether LLM responses to biomedical questions are founded in evidence", "abstract": "The growing use of large language models (LLMs) for biomedical question answering raises concerns about the accuracy and evidentiary support of their responses. To address this, we present Answered with Evidence, a framework for evaluating whether LLM-generated answers are grounded in scientific literature. We analyzed thousands of physician-submitted questions using a comparative pipeline that included: (1) Alexandria, fka the Atropos Evidence Library, a retrieval-augmented generation (RAG) system based on novel observational studies, and (2) two PubMed-based retrieval-augmented systems (System and Perplexity). We found that PubMed-based systems provided evidence-supported answers for approximately 44% of questions, while the novel evidence source did so for about 50%. Combined, these sources enabled reliable answers to over 70% of biomedical queries. As LLMs become increasingly capable of summarizing scientific content, maximizing their value will require systems that can accurately retrieve both published and custom-generated evidence or generate such evidence in real time.", "date": "2025-06-30", "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": "2507.04432", "title": "Reconstructing Biological Pathways by Applying Selective Incremental Learning to (Very) Small Language Models", "abstract": "The use of generative artificial intelligence (AI) models is becoming ubiquitous in many fields. Though progress continues to be made, general purpose large language AI models (LLM) show a tendency to deliver creative answers, often called \"hallucinations\", which have slowed their application in the medical and biomedical fields where accuracy is paramount. We propose that the design and use of much smaller, domain and even task-specific LM may be a more rational and appropriate use of this technology in biomedical research. In this work we apply a very small LM by today's standards to the specialized task of predicting regulatory interactions between molecular components to fill gaps in our current understanding of intracellular pathways. Toward this we attempt to correctly posit known pathway-informed interactions recovered from manually curated pathway databases by selecting and using only the most informative examples as part of an active learning scheme. With this example we show that a small (~110 million parameters) LM based on a Bidirectional Encoder Representations from Transformers (BERT) architecture can propose molecular interactions relevant to tuberculosis persistence and transmission with over 80% accuracy using less than 25% of the ~520 regulatory relationships in question. Using information entropy as a metric for the iterative selection of new tuning examples, we also find that increased accuracy is driven by favoring the use of the incorrectly assigned statements with the highest certainty (lowest entropy). In contrast, the concurrent use of correct but least certain examples contributed little and may have even been detrimental to the learning rate.", "date": "2025-07-06", "categories": []}
{"id": "2506.21567", "title": "BioPars: A Pretrained Biomedical Large Language Model for Persian Biomedical Text Mining", "abstract": "Large Language Models (LLMs) have recently gained attention in the life sciences due to their capacity to model, extract, and apply complex biological information. Beyond their classical use as chatbots, these systems are increasingly used for complex analysis and problem-solving in specialized fields, including bioinformatics. First, we introduce BIOPARS-BENCH, a dataset from over 10,000 scientific articles, textbooks, and medical websites. BioParsQA was also introduced to evaluate the proposed model, which consists of 5,231 Persian medical questions and answers. This study then introduces BioPars, a simple but accurate measure designed to assess LLMs for three main abilities: acquiring subject-specific knowledge, interpreting and synthesizing such knowledge, and demonstrating proper evidence. Comparing ChatGPT, Llama, and Galactica, our study highlights their ability to remember and retrieve learned knowledge but also reveals shortcomings in addressing higher-level, real-world questions and fine-grained inferences. These findings indicate the need for further fine-tuning to address the capabilities of LLM in bioinformatics tasks. To our knowledge, BioPars is the first application of LLM in Persian medical QA, especially for generating long answers. Evaluation of four selected medical QA datasets shows that BioPars has achieved remarkable results compared to comparative approaches. The model on BioParsQA achieved a ROUGE-L score of 29.99, which is an improvement over GPT-4 1.0. The model achieved a BERTScore of 90.87 with the MMR method. The MoverScore and BLEURT values were also higher in this model than the other three models. In addition, the reported scores for the model are MoverScore=60.43 and BLEURT=50.78. BioPars is an ongoing project and all resources related to its development will be made available via the following GitHub repository: https://github.com/amirap80/BioPars.", "date": "2025-06-12", "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": "2411.08870", "title": "The Limited Impact of Medical Adaptation of Large Language and Vision-Language Models", "abstract": "Several recent works seek to adapt general-purpose large language models (LLMs) and vision-language models (VLMs) for medical applications through continued pretraining on publicly available biomedical corpora. These works typically claim that such domain-adaptive pretraining improves performance on various downstream medical tasks, such as answering medical exam questions. In this paper, we compare ten \"medical\" LLMs and two VLMs against their corresponding base models, arriving at a different conclusion: all medical VLMs and nearly all medical LLMs fail to consistently improve over their base models in the zero-/few-shot prompting and supervised fine-tuning regimes for medical question answering (QA). For instance, on clinical-note-based QA tasks in the 3-shot setting, medical LLMs outperform their base models in only 26.7% of cases, reach a (statistical) tie in 16.7% of cases, and perform significantly worse in the remaining 56.7% of cases. Our conclusions are based on (i) comparing each medical model directly against its base model; (ii) optimizing the prompts for each model separately in zero-/few-shot prompting; and (iii) accounting for statistical uncertainty in comparisons. Our findings suggest that state-of-the-art general-domain models may already exhibit strong medical knowledge and reasoning capabilities, and offer recommendations to strengthen the conclusions of future studies.", "date": "2024-11-13", "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": "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": "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": "2506.20331", "title": "Biomed-Enriched: A Biomedical Dataset Enriched with LLMs for Pretraining and Extracting Rare and Hidden Content", "abstract": "We introduce Biomed-Enriched, a biomedical text dataset constructed from PubMed via a two-stage annotation process. In the first stage, a large language model annotates 400K paragraphs from PubMed scientific articles, assigning scores for their type (review, study, clinical case, other), domain (clinical, biomedical, other), and educational quality. The educational quality score (rated 1 to 5) estimates how useful a paragraph is for college-level learning. These annotations are then used to fine-tune a small language model, which propagates the labels across the full PMC-OA corpus. The resulting metadata allows us to extract refined subsets, including 2M clinical case paragraphs with over 450K high-quality ones from articles with commercial-use licenses, and to construct several variants via quality filtering and domain upsampling. Clinical text is typically difficult to access due to privacy constraints, as hospital records cannot be publicly shared. Hence, our dataset provides an alternative large-scale, openly available collection of clinical cases from PubMed, making it a valuable resource for biomedical and clinical NLP. Preliminary continual-pretraining experiments with OLMo2 suggest these curated subsets enable targeted improvements, with clinical upsampling boosting performance by ~5% on MMLU ProfMed and educational quality filtering improving MedQA and MedMCQA by ~1%. Combinations of these techniques led to faster convergence, reaching same performance with a third of training tokens, indicating potential for more efficient and effective biomedical pretraining strategies.", "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": "2506.16247", "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": "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": "2506.12674", "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": "2506.14101", "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": "2506.12886", "title": "ArgHiTZ at ArchEHR-QA 2025: A Two-Step Divide and Conquer Approach to Patient Question Answering for Top Factuality", "abstract": "This work presents three different approaches to address the ArchEHR-QA 2025 Shared Task on automated patient question answering. We introduce an end-to-end prompt-based baseline and two two-step methods to divide the task, without utilizing any external knowledge. Both two step approaches first extract essential sentences from the clinical text, by prompt or similarity ranking, and then generate the final answer from these notes. Results indicate that the re-ranker based two-step system performs best, highlighting the importance of selecting the right approach for each subtask. Our best run achieved an overall score of 0.44, ranking 8th out of 30 on the leaderboard, securing the top position in overall factuality.", "date": "2025-06-15", "categories": []}
{"id": "2506.12898", "title": "JEBS: A Fine-grained Biomedical Lexical Simplification Task", "abstract": "Online medical literature has made health information more available than ever, however, the barrier of complex medical jargon prevents the general public from understanding it. Though parallel and comparable corpora for Biomedical Text Simplification have been introduced, these conflate the many syntactic and lexical operations involved in simplification. To enable more targeted development and evaluation, we present a fine-grained lexical simplification task and dataset, Jargon Explanations for Biomedical Simplification (JEBS, https://github.com/bill-from-ri/JEBS-data ). The JEBS task involves identifying complex terms, classifying how to replace them, and generating replacement text. The JEBS dataset contains 21,595 replacements for 10,314 terms across 400 biomedical abstracts and their manually simplified versions. Additionally, we provide baseline results for a variety of rule-based and transformer-based systems for the three sub-tasks. The JEBS task, data, and baseline results pave the way for development and rigorous evaluation of systems for replacing or explaining complex biomedical terms.", "date": "2025-06-15", "categories": []}
{"id": "2506.01305", "title": "VM14K: First Vietnamese Medical Benchmark", "abstract": "Medical benchmarks are indispensable for evaluating the capabilities of language models in healthcare for non-English-speaking communities,therefore help ensuring the quality of real-life applications. However, not every community has sufficient resources and standardized methods to effectively build and design such benchmark, and available non-English medical data is normally fragmented and difficult to verify. We developed an approach to tackle this problem and applied it to create the first Vietnamese medical question benchmark, featuring 14,000 multiple-choice questions across 34 medical specialties. Our benchmark was constructed using various verifiable sources, including carefully curated medical exams and clinical records, and eventually annotated by medical experts. The benchmark includes four difficulty levels, ranging from foundational biological knowledge commonly found in textbooks to typical clinical case studies that require advanced reasoning. This design enables assessment of both the breadth and depth of language models' medical understanding in the target language thanks to its extensive coverage and in-depth subject-specific expertise. We release the benchmark in three parts: a sample public set (4k questions), a full public set (10k questions), and a private set (2k questions) used for leaderboard evaluation. Each set contains all medical subfields and difficulty levels. Our approach is scalable to other languages, and we open-source our data construction pipeline to support the development of future multilingual benchmarks in the medical domain.", "date": "2025-06-02", "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": "2506.10896", "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": "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": "2506.08647", "title": "Summarization for Generative Relation Extraction in the Microbiome Domain", "abstract": "We explore a generative relation extraction (RE) pipeline tailored to the study of interactions in the intestinal microbiome, a complex and low-resource biomedical domain. Our method leverages summarization with large language models (LLMs) to refine context before extracting relations via instruction-tuned generation. Preliminary results on a dedicated corpus show that summarization improves generative RE performance by reducing noise and guiding the model. However, BERT-based RE approaches still outperform generative models. This ongoing work demonstrates the potential of generative methods to support the study of specialized domains in low-resources setting.", "date": "2025-06-10", "categories": []}
{"id": "2506.06091", "title": "MIRIAD: Augmenting LLMs with millions of medical query-response pairs", "abstract": "LLMs are bound to transform healthcare with advanced decision support and flexible chat assistants. However, LLMs are prone to generate inaccurate medical content. To ground LLMs in high-quality medical knowledge, LLMs have been equipped with external knowledge via RAG, where unstructured medical knowledge is split into small text chunks that can be selectively retrieved and integrated into the LLMs context. Yet, existing RAG pipelines rely on raw, unstructured medical text, which can be noisy, uncurated and difficult for LLMs to effectively leverage. Systematic approaches to organize medical knowledge to best surface it to LLMs are generally lacking. To address these challenges, we introduce MIRIAD, a large-scale, curated corpus of 5,821,948 medical QA pairs, each rephrased from and grounded in a passage from peer-reviewed medical literature using a semi-automated pipeline combining LLM generation, filtering, grounding, and human annotation. Unlike prior medical corpora, which rely on unstructured text, MIRIAD encapsulates web-scale medical knowledge in an operationalized query-response format, which enables more targeted retrieval. Experiments on challenging medical QA benchmarks show that augmenting LLMs with MIRIAD improves accuracy up to 6.7% compared to unstructured RAG baselines with the same source corpus and with the same amount of retrieved text. Moreover, MIRIAD improved the ability of LLMs to detect medical hallucinations by 22.5 to 37% (increase in F1 score). We further introduce MIRIAD-Atlas, an interactive map of MIRIAD spanning 56 medical disciplines, enabling clinical users to visually explore, search, and refine medical knowledge. MIRIAD promises to unlock a wealth of down-stream applications, including medical information retrievers, enhanced RAG applications, and knowledge-grounded chat interfaces, which ultimately enables more reliable LLM applications in healthcare.", "date": "2025-06-06", "categories": []}
{"id": "2506.05380", "title": "EvidenceOutcomes: a Dataset of Clinical Trial Publications with Clinically Meaningful Outcomes", "abstract": "The fundamental process of evidence extraction and synthesis in evidence-based medicine involves extracting PICO (Population, Intervention, Comparison, and Outcome) elements from biomedical literature. However, Outcomes, being the most complex elements, are often neglected or oversimplified in existing benchmarks. To address this issue, we present EvidenceOutcomes, a novel, large, annotated corpus of clinically meaningful outcomes extracted from biomedical literature. We first developed a robust annotation guideline for extracting clinically meaningful outcomes from text through iteration and discussion with clinicians and Natural Language Processing experts. Then, three independent annotators annotated the Results and Conclusions sections of a randomly selected sample of 500 PubMed abstracts and 140 PubMed abstracts from the existing EBM-NLP corpus. This resulted in EvidenceOutcomes with high-quality annotations of an inter-rater agreement of 0.76. Additionally, our fine-tuned PubMedBERT model, applied to these 500 PubMed abstracts, achieved an F1-score of 0.69 at the entity level and 0.76 at the token level on the subset of 140 PubMed abstracts from the EBM-NLP corpus. EvidenceOutcomes can serve as a shared benchmark to develop and test future machine learning algorithms to extract clinically meaningful outcomes from biomedical abstracts.", "date": "2025-06-03", "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": "2506.03321", "title": "Enhancing Automatic PT Tagging for MEDLINE Citations Using Transformer-Based Models", "abstract": "We investigated the feasibility of predicting Medical Subject Headings (MeSH) Publication Types (PTs) from MEDLINE citation metadata using pre-trained Transformer-based models BERT and DistilBERT. This study addresses limitations in the current automated indexing process, which relies on legacy NLP algorithms. We evaluated monolithic multi-label classifiers and binary classifier ensembles to enhance the retrieval of biomedical literature. Results demonstrate the potential of Transformer models to significantly improve PT tagging accuracy, paving the way for scalable, efficient biomedical indexing.", "date": "2025-06-03", "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": "2506.01961", "title": "Research on Medical Named Entity Identification Based On Prompt-Biomrc Model and Its Application in Intelligent Consultation System", "abstract": "This study is dedicated to exploring the application of prompt learning methods to advance Named Entity Recognition (NER) within the medical domain. In recent years, the emergence of large-scale models has driven significant progress in NER tasks, particularly with the introduction of the BioBERT language model, which has greatly enhanced NER capabilities in medical texts. Our research introduces the Prompt-bioMRC model, which integrates both hard template and soft prompt designs aimed at refining the precision and efficiency of medical entity recognition. Through extensive experimentation across diverse medical datasets, our findings consistently demonstrate that our approach surpasses traditional models. This enhancement not only validates the efficacy of our methodology but also highlights its potential to provide reliable technological support for applications like intelligent diagnosis systems. By leveraging advanced NER techniques, this study contributes to advancing automated medical data processing, facilitating more accurate medical information extraction, and supporting efficient healthcare decision-making processes.", "date": "2025-05-08", "categories": []}
{"id": "2502.14677", "title": "Data-Constrained Synthesis of Training Data for De-Identification", "abstract": "Many sensitive domains -- such as the clinical domain -- lack widely available datasets due to privacy risks. The increasing generative capabilities of large language models (LLMs) have made synthetic datasets a viable path forward. In this study, we domain-adapt LLMs to the clinical domain and generate synthetic clinical texts that are machine-annotated with tags for personally identifiable information using capable encoder-based NER models. The synthetic corpora are then used to train synthetic NER models. The results show that training NER models using synthetic corpora incurs only a small drop in predictive performance. The limits of this process are investigated in a systematic ablation study -- using both Swedish and Spanish data. Our analysis shows that smaller datasets can be sufficient for domain-adapting LLMs for data synthesis. Instead, the effectiveness of this process is almost entirely contingent on the performance of the machine-annotating NER models trained using the original data.", "date": "2025-02-20", "categories": []}
{"id": "2505.23802", "title": "MedHELM: Holistic Evaluation of Large Language Models for Medical Tasks", "abstract": "While large language models (LLMs) achieve near-perfect scores on medical licensing exams, these evaluations inadequately reflect the complexity and diversity of real-world clinical practice. We introduce MedHELM, an extensible evaluation framework for assessing LLM performance for medical tasks with three key contributions. First, a clinician-validated taxonomy spanning 5 categories, 22 subcategories, and 121 tasks developed with 29 clinicians. Second, a comprehensive benchmark suite comprising 35 benchmarks (17 existing, 18 newly formulated) providing complete coverage of all categories and subcategories in the taxonomy. Third, a systematic comparison of LLMs with improved evaluation methods (using an LLM-jury) and a cost-performance analysis. Evaluation of 9 frontier LLMs, using the 35 benchmarks, revealed significant performance variation. Advanced reasoning models (DeepSeek R1: 66% win-rate; o3-mini: 64% win-rate) demonstrated superior performance, though Claude 3.5 Sonnet achieved comparable results at 40% lower estimated computational cost. On a normalized accuracy scale (0-1), most models performed strongly in Clinical Note Generation (0.73-0.85) and Patient Communication & Education (0.78-0.83), moderately in Medical Research Assistance (0.65-0.75), and generally lower in Clinical Decision Support (0.56-0.72) and Administration & Workflow (0.53-0.63). Our LLM-jury evaluation method achieved good agreement with clinician ratings (ICC = 0.47), surpassing both average clinician-clinician agreement (ICC = 0.43) and automated baselines including ROUGE-L (0.36) and BERTScore-F1 (0.44). Claude 3.5 Sonnet achieved comparable performance to top models at lower estimated cost. These findings highlight the importance of real-world, task-specific evaluation for medical use of LLMs and provides an open source framework to enable this.", "date": "2025-05-26", "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": "2506.00448", "title": "Fact-Controlled Diagnosis of Hallucinations in Medical Text Summarization", "abstract": "Hallucinations in large language models (LLMs) during summarization of patient-clinician dialogues pose significant risks to patient care and clinical decision-making. However, the phenomenon remains understudied in the clinical domain, with uncertainty surrounding the applicability of general-domain hallucination detectors. The rarity and randomness of hallucinations further complicate their investigation. In this paper, we conduct an evaluation of hallucination detection methods in the medical domain, and construct two datasets for the purpose: A fact-controlled Leave-N-out dataset -- generated by systematically removing facts from source dialogues to induce hallucinated content in summaries; and a natural hallucination dataset -- arising organically during LLM-based medical summarization. We show that general-domain detectors struggle to detect clinical hallucinations, and that performance on fact-controlled hallucinations does not reliably predict effectiveness on natural hallucinations. We then develop fact-based approaches that count hallucinations, offering explainability not available with existing methods. Notably, our LLM-based detectors, which we developed using fact-controlled hallucinations, generalize well to detecting real-world clinical hallucinations. This research contributes a suite of specialized metrics supported by expert-annotated datasets to advance faithful clinical summarization systems.", "date": "2025-05-31", "categories": []}
{"id": "2506.00671", "title": "DeepRAG: Integrating Hierarchical Reasoning and Process Supervision for Biomedical Multi-Hop QA", "abstract": "We propose DeepRAG, a novel framework that integrates DeepSeek hierarchical question decomposition capabilities with RAG Gym unified retrieval-augmented generation optimization using process level supervision. Targeting the challenging MedHopQA biomedical question answering task, DeepRAG systematically decomposes complex queries into precise sub-queries and employs concept level reward signals informed by the UMLS ontology to enhance biomedical accuracy. Preliminary evaluations on the MedHopQA dataset indicate that DeepRAG significantly outperforms baseline models, including standalone DeepSeek and RAG Gym, achieving notable improvements in both Exact Match and concept level accuracy.", "date": "2025-05-31", "categories": []}
{"id": "2506.00777", "title": "Improving Automatic Evaluation of Large Language Models (LLMs) in Biomedical Relation Extraction via LLMs-as-the-Judge", "abstract": "Large Language Models (LLMs) have demonstrated impressive performance in biomedical relation extraction, even in zero-shot scenarios. However, evaluating LLMs in this task remains challenging due to their ability to generate human-like text, often producing synonyms or abbreviations of gold-standard answers, making traditional automatic evaluation metrics unreliable. On the other hand, while human evaluation is more reliable, it is costly and time-consuming, making it impractical for real-world applications. This paper investigates the use of LLMs-as-the-Judge as an alternative evaluation method for biomedical relation extraction. We benchmark 8 LLMs as judges to evaluate the responses generated by 5 other LLMs across 3 biomedical relation extraction datasets. Unlike other text-generation tasks, we observe that LLM-based judges perform quite poorly (usually below 50% accuracy) in the biomedical relation extraction task. Our findings reveal that it happens mainly because relations extracted by LLMs do not adhere to any standard format. To address this, we propose structured output formatting for LLM-generated responses that helps LLM-Judges to improve their performance by about 15% (on average). We also introduce a domain adaptation technique to further enhance LLM-Judge performance by effectively transferring knowledge between datasets. We release both our human-annotated and LLM-annotated judgment data (36k samples in total) for public use here: https://github.com/tahmedge/llm_judge_biomedical_re.", "date": "2025-06-01", "categories": []}
{"id": "2505.06186", "title": "Query-driven Document-level Scientific Evidence Extraction from Biomedical Studies", "abstract": "Extracting scientific evidence from biomedical studies for clinical research questions (e.g., Does stem cell transplantation improve quality of life in patients with medically refractory Crohn's disease compared to placebo?) is a crucial step in synthesising biomedical evidence. In this paper, we focus on the task of document-level scientific evidence extraction for clinical questions with conflicting evidence. To support this task, we create a dataset called CochraneForest, leveraging forest plots from Cochrane systematic reviews. It comprises 202 annotated forest plots, associated clinical research questions, full texts of studies, and study-specific conclusions. Building on CochraneForest, we propose URCA (Uniform Retrieval Clustered Augmentation), a retrieval-augmented generation framework designed to tackle the unique challenges of evidence extraction. Our experiments show that URCA outperforms the best existing methods by up to 10.3% in F1 score on this task. However, the results also underscore the complexity of CochraneForest, establishing it as a challenging testbed for advancing automated evidence synthesis systems.", "date": "2025-05-09", "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": "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": "2501.06365", "title": "Gender-Neutral Large Language Models for Medical Applications: Reducing Bias in PubMed Abstracts", "abstract": "This paper presents a pipeline for mitigating gender bias in large language models (LLMs) used in medical literature by neutralizing gendered occupational pronouns. A dataset of 379,000 PubMed abstracts from 1965-1980 was processed to identify and modify pronouns tied to professions. We developed a BERT-based model, \"Modern Occupational Bias Elimination with Refined Training,\" or \"MOBERT,\" trained on these neutralized abstracts, and compared its performance with \"1965BERT,\" trained on the original dataset. MOBERT achieved a 70% inclusive replacement rate, while 1965BERT reached only 4%. A further analysis of MOBERT revealed that pronoun replacement accuracy correlated with the frequency of occupational terms in the training data. We propose expanding the dataset and refining the pipeline to improve performance and ensure more equitable language modeling in medical applications.", "date": "2025-01-10", "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": "2505.22280", "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": "2505.08704", "title": "LLM-based Prompt Ensemble for Reliable Medical Entity Recognition from EHRs", "abstract": "Electronic Health Records (EHRs) are digital records of patient information, often containing unstructured clinical text. Named Entity Recognition (NER) is essential in EHRs for extracting key medical entities like problems, tests, and treatments to support downstream clinical applications. This paper explores prompt-based medical entity recognition using large language models (LLMs), specifically GPT-4o and DeepSeek-R1, guided by various prompt engineering techniques, including zero-shot, few-shot, and an ensemble approach. Among all strategies, GPT-4o with prompt ensemble achieved the highest classification performance with an F1-score of 0.95 and recall of 0.98, outperforming DeepSeek-R1 on the task. The ensemble method improved reliability by aggregating outputs through embedding-based similarity and majority voting.", "date": "2025-05-13", "categories": []}
{"id": "2505.18331", "title": "PerMedCQA: Benchmarking Large Language Models on Medical Consumer Question Answering in Persian Language", "abstract": "Medical consumer question answering (CQA) is crucial for empowering patients by providing personalized and reliable health information. Despite recent advances in large language models (LLMs) for medical QA, consumer-oriented and multilingual resources, particularly in low-resource languages like Persian, remain sparse. To bridge this gap, we present PerMedCQA, the first Persian-language benchmark for evaluating LLMs on real-world, consumer-generated medical questions. Curated from a large medical QA forum, PerMedCQA contains 68,138 question-answer pairs, refined through careful data cleaning from an initial set of 87,780 raw entries. We evaluate several state-of-the-art multilingual and instruction-tuned LLMs, utilizing MedJudge, a novel rubric-based evaluation framework driven by an LLM grader, validated against expert human annotators. Our results highlight key challenges in multilingual medical QA and provide valuable insights for developing more accurate and context-aware medical assistance systems. The data is publicly available on https://huggingface.co/datasets/NaghmehAI/PerMedCQA", "date": "2025-05-23", "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": "2505.19722", "title": "Distilling Closed-Source LLM's Knowledge for Locally Stable and Economic Biomedical Entity Linking", "abstract": "Biomedical entity linking aims to map nonstandard entities to standard entities in a knowledge base. Traditional supervised methods perform well but require extensive annotated data to transfer, limiting their usage in low-resource scenarios. Large language models (LLMs), especially closed-source LLMs, can address these but risk stability issues and high economic costs: using these models is restricted by commercial companies and brings significant economic costs when dealing with large amounts of data. To address this, we propose ``RPDR'', a framework combining closed-source LLMs and open-source LLMs for re-ranking candidates retrieved by a retriever fine-tuned with a small amount of data. By prompting a closed-source LLM to generate training data from unannotated data and fine-tuning an open-source LLM for re-ranking, we effectively distill the knowledge to the open-source LLM that can be deployed locally, thus avoiding the stability issues and the problem of high economic costs. We evaluate RPDR on two datasets, including one real-world dataset and one publicly available dataset involving two languages: Chinese and English. RPDR achieves 0.019 Acc@1 improvement and 0.036 Acc@1 improvement on the Aier dataset and the Ask A Patient dataset when the amount of training data is not enough. The results demonstrate the superiority and generalizability of the proposed framework.", "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": "2505.17059", "title": "Medalyze: Lightweight Medical Report Summarization Application Using FLAN-T5-Large", "abstract": "Understanding medical texts presents significant challenges due to complex terminology and context-specific language. This paper introduces Medalyze, an AI-powered application designed to enhance the comprehension of medical texts using three specialized FLAN-T5-Large models. These models are fine-tuned for (1) summarizing medical reports, (2) extracting health issues from patient-doctor conversations, and (3) identifying the key question in a passage. Medalyze is deployed across a web and mobile platform with real-time inference, leveraging scalable API and YugabyteDB. Experimental evaluations demonstrate the system's superior summarization performance over GPT-4 in domain-specific tasks, based on metrics like BLEU, ROUGE-L, BERTScore, and SpaCy Similarity. Medalyze provides a practical, privacy-preserving, and lightweight solution for improving information accessibility in healthcare.", "date": "2025-05-17", "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": "2505.17265", "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": "2504.00676", "title": "GLiNER-BioMed: A Suite of Efficient Models for Open Biomedical Named Entity Recognition", "abstract": "Biomedical named entity recognition (NER) presents unique challenges due to specialized vocabularies, the sheer volume of entities, and the continuous emergence of novel entities. Traditional NER models, constrained by fixed taxonomies and human annotations, struggle to generalize beyond predefined entity types. To address these issues, we introduce GLiNER-BioMed, a domain-adapted suite of Generalist and Lightweight Model for NER (GLiNER) models specifically tailored for biomedicine. In contrast to conventional approaches, GLiNER uses natural language labels to infer arbitrary entity types, enabling zero-shot recognition. Our approach first distills the annotation capabilities of large language models (LLMs) into a smaller, more efficient model, enabling the generation of high-coverage synthetic biomedical NER data. We subsequently train two GLiNER architectures, uni- and bi-encoder, at multiple scales to balance computational efficiency and recognition performance. Experiments on several biomedical datasets demonstrate that GLiNER-BioMed outperforms the state-of-the-art in both zero- and few-shot scenarios, achieving 5.96% improvement in F1-score over the strongest baseline (p-value < 0.001). Ablation studies highlight the effectiveness of our synthetic data generation strategy and emphasize the complementary benefits of synthetic biomedical pre-training combined with fine-tuning on general-domain annotations. All datasets, models, and training pipelines are publicly available at https://github.com/ds4dh/GLiNER-biomed.", "date": "2025-04-01", "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": "2505.11887", "title": "AutoMedEval: Harnessing Language Models for Automatic Medical Capability Evaluation", "abstract": "With the proliferation of large language models (LLMs) in the medical domain, there is increasing demand for improved evaluation techniques to assess their capabilities. However, traditional metrics like F1 and ROUGE, which rely on token overlaps to measure quality, significantly overlook the importance of medical terminology. While human evaluation tends to be more reliable, it can be very costly and may as well suffer from inaccuracies due to limits in human expertise and motivation. Although there are some evaluation methods based on LLMs, their usability in the medical field is limited due to their proprietary nature or lack of expertise. To tackle these challenges, we present AutoMedEval, an open-sourced automatic evaluation model with 13B parameters specifically engineered to measure the question-answering proficiency of medical LLMs. The overarching objective of AutoMedEval is to assess the quality of responses produced by diverse models, aspiring to significantly reduce the dependence on human evaluation. Specifically, we propose a hierarchical training method involving curriculum instruction tuning and an iterative knowledge introspection mechanism, enabling AutoMedEval to acquire professional medical assessment capabilities with limited instructional data. Human evaluations indicate that AutoMedEval surpasses other baselines in terms of correlation with human judgments.", "date": "2025-05-17", "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": "2505.08775", "title": "HealthBench: Evaluating Large Language Models Towards Improved Human Health", "abstract": "We present HealthBench, an open-source benchmark measuring the performance and safety of large language models in healthcare. HealthBench consists of 5,000 multi-turn conversations between a model and an individual user or healthcare professional. Responses are evaluated using conversation-specific rubrics created by 262 physicians. Unlike previous multiple-choice or short-answer benchmarks, HealthBench enables realistic, open-ended evaluation through 48,562 unique rubric criteria spanning several health contexts (e.g., emergencies, transforming clinical data, global health) and behavioral dimensions (e.g., accuracy, instruction following, communication). HealthBench performance over the last two years reflects steady initial progress (compare GPT-3.5 Turbo's 16% to GPT-4o's 32%) and more rapid recent improvements (o3 scores 60%). Smaller models have especially improved: GPT-4.1 nano outperforms GPT-4o and is 25 times cheaper. We additionally release two HealthBench variations: HealthBench Consensus, which includes 34 particularly important dimensions of model behavior validated via physician consensus, and HealthBench Hard, where the current top score is 32%. We hope that HealthBench grounds progress towards model development and applications that benefit human health.", "date": "2025-05-13", "categories": []}
{"id": "2409.13746", "title": "Mapping Biomedical Ontology Terms to IDs: Effect of Domain Prevalence on Prediction Accuracy", "abstract": "This study evaluates the ability of large language models (LLMs) to map biomedical ontology terms to their corresponding ontology IDs across the Human Phenotype Ontology (HPO), Gene Ontology (GO), and UniProtKB terminologies. Using counts of ontology IDs in the PubMed Central (PMC) dataset as a surrogate for their prevalence in the biomedical literature, we examined the relationship between ontology ID prevalence and mapping accuracy. Results indicate that ontology ID prevalence strongly predicts accurate mapping of HPO terms to HPO IDs, GO terms to GO IDs, and protein names to UniProtKB accession numbers. Higher prevalence of ontology IDs in the biomedical literature correlated with higher mapping accuracy. Predictive models based on receiver operating characteristic (ROC) curves confirmed this relationship.\n  In contrast, this pattern did not apply to mapping protein names to Human Genome Organisation's (HUGO) gene symbols. GPT-4 achieved a high baseline performance (95%) in mapping protein names to HUGO gene symbols, with mapping accuracy unaffected by prevalence. We propose that the high prevalence of HUGO gene symbols in the literature has caused these symbols to become lexicalized, enabling GPT-4 to map protein names to HUGO gene symbols with high accuracy. These findings highlight the limitations of LLMs in mapping ontology terms to low-prevalence ontology IDs and underscore the importance of incorporating ontology ID prevalence into the training and evaluation of LLMs for biomedical applications.", "date": "2024-09-11", "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": "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": "2505.04073", "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": "2504.21685", "title": "Enhancing Health Mention Classification Performance: A Study on\n  Advancements in Parameter Efficient Tuning", "abstract": "  Health Mention Classification (HMC) plays a critical role in leveraging\nsocial media posts for real-time tracking and public health monitoring.\nNevertheless, the process of HMC presents significant challenges due to its\nintricate nature, primarily stemming from the contextual aspects of health\nmentions, such as figurative language and descriptive terminology, rather than\nexplicitly reflecting a personal ailment. To address this problem, we argue\nthat clearer mentions can be achieved through conventional fine-tuning with\nenhanced parameters of biomedical natural language methods (NLP). In this\nstudy, we explore different techniques such as the utilisation of\npart-of-speech (POS) tagger information, improving on PEFT techniques, and\ndifferent combinations thereof. Extensive experiments are conducted on three\nwidely used datasets: RHDM, PHM, and Illness. The results incorporated POS\ntagger information, and leveraging PEFT techniques significantly improves\nperformance in terms of F1-score compared to state-of-the-art methods across\nall three datasets by utilising smaller models and efficient training.\nFurthermore, the findings highlight the effectiveness of incorporating POS\ntagger information and leveraging PEFT techniques for HMC. In conclusion, the\nproposed methodology presents a potentially effective approach to accurately\nclassifying health mentions in social media posts while optimising the model\nsize and training efficiency.\n", "date": "2025-04-30", "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": "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": "2307.08813", "title": "Comparative Performance Evaluation of Large Language Models for\n  Extracting Molecular Interactions and Pathway Knowledge", "abstract": "  Background: Identification of the interactions and regulatory relations\nbetween biomolecules play pivotal roles in understanding complex biological\nsystems and the mechanisms underlying diverse biological functions. However,\nthe collection of such molecular interactions has heavily relied on expert\ncuration in the past, making it labor-intensive and time-consuming. To mitigate\nthese challenges, we propose leveraging the capabilities of large language\nmodels (LLMs) to automate genome-scale extraction of this crucial knowledge.\n  Results: In this study, we investigate the efficacy of various LLMs in\naddressing biological tasks, such as the recognition of protein interactions,\nidentification of genes linked to pathways affected by low-dose radiation, and\nthe delineation of gene regulatory relationships. Overall, the larger models\nexhibited superior performance, indicating their potential for specific tasks\nthat involve the extraction of complex interactions among genes and proteins.\nAlthough these models possessed detailed information for distinct gene and\nprotein groups, they faced challenges in identifying groups with diverse\nfunctions and in recognizing highly correlated gene regulatory relationships.\n  Conclusions: By conducting a comprehensive assessment of the state-of-the-art\nmodels using well-established molecular interaction and pathway databases, our\nstudy reveals that LLMs can identify genes/proteins associated with pathways of\ninterest and predict their interactions to a certain extent. Furthermore, these\nmodels can provide important insights, marking a noteworthy stride toward\nadvancing our understanding of biological systems through AI-assisted knowledge\ndiscovery.\n", "date": "2023-07-17", "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": "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": "2504.12341", "title": "Streamlining Biomedical Research with Specialized LLMs", "abstract": "  In this paper, we propose a novel system that integrates state-of-the-art,\ndomain-specific large language models with advanced information retrieval\ntechniques to deliver comprehensive and context-aware responses. Our approach\nfacilitates seamless interaction among diverse components, enabling\ncross-validation of outputs to produce accurate, high-quality responses\nenriched with relevant data, images, tables, and other modalities. We\ndemonstrate the system's capability to enhance response precision by leveraging\na robust question-answering model, significantly improving the quality of\ndialogue generation. The system provides an accessible platform for real-time,\nhigh-fidelity interactions, allowing users to benefit from efficient\nhuman-computer interaction, precise retrieval, and simultaneous access to a\nwide range of literature and data. This dramatically improves the research\nefficiency of professionals in the biomedical and pharmaceutical domains and\nfacilitates faster, more informed decision-making throughout the R\\&D process.\nFurthermore, the system proposed in this paper is available at\nhttps://synapse-chat.patsnap.com.\n", "date": "2025-04-15", "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": "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": "2503.20737", "title": "Ontology-based Semantic Similarity Measures for Clustering Medical\n  Concepts in Drug Safety", "abstract": "  Semantic similarity measures (SSMs) are widely used in biomedical research\nbut remain underutilized in pharmacovigilance. This study evaluates six\nontology-based SSMs for clustering MedDRA Preferred Terms (PTs) in drug safety\ndata. Using the Unified Medical Language System (UMLS), we assess each method's\nability to group PTs around medically meaningful centroids. A high-throughput\nframework was developed with a Java API and Python and R interfaces support\nlarge-scale similarity computations. Results show that while path-based methods\nperform moderately with F1 scores of 0.36 for WUPALMER and 0.28 for LCH,\nintrinsic information content (IC)-based measures, especially INTRINSIC-LIN and\nSOKAL, consistently yield better clustering accuracy (F1 score of 0.403).\nValidated against expert review and standard MedDRA queries (SMQs), our\nfindings highlight the promise of IC-based SSMs in enhancing pharmacovigilance\nworkflows by improving early signal detection and reducing manual review.\n", "date": "2025-03-26", "categories": []}
{"id": "2504.09582", "title": "Reduction of Supervision for Biomedical Knowledge Discovery", "abstract": "  Knowledge discovery is hindered by the increasing volume of publications and\nthe scarcity of extensive annotated data. To tackle the challenge of\ninformation overload, it is essential to employ automated methods for knowledge\nextraction and processing. Finding the right balance between the level of\nsupervision and the effectiveness of models poses a significant challenge.\nWhile supervised techniques generally result in better performance, they have\nthe major drawback of demanding labeled data. This requirement is\nlabor-intensive and time-consuming and hinders scalability when exploring new\ndomains. In this context, our study addresses the challenge of identifying\nsemantic relationships between biomedical entities (e.g., diseases, proteins)\nin unstructured text while minimizing dependency on supervision. We introduce a\nsuite of unsupervised algorithms based on dependency trees and attention\nmechanisms and employ a range of pointwise binary classification methods.\nTransitioning from weakly supervised to fully unsupervised settings, we assess\nthe methods' ability to learn from data with noisy labels. The evaluation on\nbiomedical benchmark datasets explores the effectiveness of the methods. Our\napproach tackles a central issue in knowledge discovery: balancing performance\nwith minimal supervision. By gradually decreasing supervision, we assess the\nrobustness of pointwise binary classification techniques in handling noisy\nlabels, revealing their capability to shift from weakly supervised to entirely\nunsupervised scenarios. Comprehensive benchmarking offers insights into the\neffectiveness of these techniques, suggesting an encouraging direction toward\nadaptable knowledge discovery systems, representing progress in creating\ndata-efficient methodologies for extracting useful insights when annotated data\nis limited.\n", "date": "2025-04-13", "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": "2504.03964", "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": "2504.04385", "title": "Pre-trained Language Models and Few-shot Learning for Medical Entity\n  Extraction", "abstract": "  This study proposes a medical entity extraction method based on Transformer\nto enhance the information extraction capability of medical literature.\nConsidering the professionalism and complexity of medical texts, we compare the\nperformance of different pre-trained language models (BERT, BioBERT,\nPubMedBERT, ClinicalBERT) in medical entity extraction tasks. Experimental\nresults show that PubMedBERT achieves the best performance (F1-score = 88.8%),\nindicating that a language model pre-trained on biomedical literature is more\neffective in the medical domain. In addition, we analyze the impact of\ndifferent entity extraction methods (CRF, Span-based, Seq2Seq) and find that\nthe Span-based approach performs best in medical entity extraction tasks\n(F1-score = 88.6%). It demonstrates superior accuracy in identifying entity\nboundaries. In low-resource scenarios, we further explore the application of\nFew-shot Learning in medical entity extraction. Experimental results show that\neven with only 10-shot training samples, the model achieves an F1-score of\n79.1%, verifying the effectiveness of Few-shot Learning under limited data\nconditions. This study confirms that the combination of pre-trained language\nmodels and Few-shot Learning can enhance the accuracy of medical entity\nextraction. Future research can integrate knowledge graphs and active learning\nstrategies to improve the model's generalization and stability, providing a\nmore effective solution for medical NLP research. Keywords- Natural Language\nProcessing, medical named entity recognition, pre-trained language model,\nFew-shot Learning, information extraction, deep learning\n", "date": "2025-04-06", "categories": []}
{"id": "2406.02134", "title": "The current status of large language models in summarizing radiology\n  report impressions", "abstract": "  Large language models (LLMs) like ChatGPT show excellent capabilities in\nvarious natural language processing tasks, especially for text generation. The\neffectiveness of LLMs in summarizing radiology report impressions remains\nunclear. In this study, we explore the capability of eight LLMs on the\nradiology report impression summarization. Three types of radiology reports,\ni.e., CT, PET-CT, and Ultrasound reports, are collected from Peking University\nCancer Hospital and Institute. We use the report findings to construct the\nzero-shot, one-shot, and three-shot prompts with complete example reports to\ngenerate the impressions. Besides the automatic quantitative evaluation\nmetrics, we define five human evaluation metrics, i.e., completeness,\ncorrectness, conciseness, verisimilitude, and replaceability, to evaluate the\nsemantics of the generated impressions. Two thoracic surgeons (ZSY and LB) and\none radiologist (LQ) compare the generated impressions with the reference\nimpressions and score each impression under the five human evaluation metrics.\nExperimental results show that there is a gap between the generated impressions\nand reference impressions. Although the LLMs achieve comparable performance in\ncompleteness and correctness, the conciseness and verisimilitude scores are not\nvery high. Using few-shot prompts can improve the LLMs' performance in\nconciseness and verisimilitude, but the clinicians still think the LLMs can not\nreplace the radiologists in summarizing the radiology impressions.\n", "date": "2024-06-04", "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": "2504.02874", "title": "TheBlueScrubs-v1, a comprehensive curated medical dataset derived from\n  the internet", "abstract": "  The need for robust and diverse data sets to train clinical large language\nmodels (cLLMs) is critical given that currently available public repositories\noften prove too limited in size or scope for comprehensive medical use. While\nresources like PubMed provide foundational medical literature, they capture\nonly a narrow range of formal publications and omit the broader medical\ndiscourse on the internet. To address these deficits, we introduce\nTheBlueScrubs-v1, a curated dataset of over 25 billion medical tokens - nearly\nthree times larger than PubMed - drawn from a broad-scale internet corpus. Our\ntwo-stage filtering pipeline employs a Logistic Regression model for document\nscreening (achieving an AUC of approximately 0.95 on external validation),\nfollowed by verification via a 70B-parameter Llama 3.1 instruct model. Each\ntext is assigned three LLM-based quality scores encompassing medical relevance,\nprecision and factual detail, and safety and ethical standards. Clinician\nreviews confirm high concordance with these automated evaluations, and a\nspecialized cancer classifier further labels approximately 11 billion oncology\ntokens. Two demonstration tasks highlight the dataset's practical value: first,\nwe distill the safety evaluations to a smaller BERT-style model that reaches an\nAUC near 0.96 on unseen data; second, we fine-tune a compact LLM on a filtered\nsubset, showing measurable improvements over standard baselines in medical\nbenchmarks as well as private ones. This Data Descriptor details the dataset's\ncreation and validation, underscoring its potential utility for medical AI\nresearch.\n", "date": "2025-04-01", "categories": []}
{"id": "2504.03051", "title": "Task as Context Prompting for Accurate Medical Symptom Coding Using\n  Large Language Models", "abstract": "  Accurate medical symptom coding from unstructured clinical text, such as\nvaccine safety reports, is a critical task with applications in\npharmacovigilance and safety monitoring. Symptom coding, as tailored in this\nstudy, involves identifying and linking nuanced symptom mentions to\nstandardized vocabularies like MedDRA, differentiating it from broader medical\ncoding tasks. Traditional approaches to this task, which treat symptom\nextraction and linking as independent workflows, often fail to handle the\nvariability and complexity of clinical narratives, especially for rare cases.\nRecent advancements in Large Language Models (LLMs) offer new opportunities but\nface challenges in achieving consistent performance. To address these issues,\nwe propose Task as Context (TACO) Prompting, a novel framework that unifies\nextraction and linking tasks by embedding task-specific context into LLM\nprompts. Our study also introduces SYMPCODER, a human-annotated dataset derived\nfrom Vaccine Adverse Event Reporting System (VAERS) reports, and a two-stage\nevaluation framework to comprehensively assess both symptom linking and mention\nfidelity. Our comprehensive evaluation of multiple LLMs, including Llama2-chat,\nJackalope-7b, GPT-3.5 Turbo, GPT-4 Turbo, and GPT-4o, demonstrates TACO's\neffectiveness in improving flexibility and accuracy for tailored tasks like\nsymptom coding, paving the way for more specific coding tasks and advancing\nclinical text processing methodologies.\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": "2406.13337", "title": "Medical Spoken Named Entity Recognition", "abstract": "  Spoken Named Entity Recognition (NER) aims to extract named entities from\nspeech and categorise them into types like person, location, organization, etc.\nIn this work, we present VietMed-NER - the first spoken NER dataset in the\nmedical domain. To our knowledge, our Vietnamese real-world dataset is the\nlargest spoken NER dataset in the world regarding the number of entity types,\nfeaturing 18 distinct types. Furthermore, we present baseline results using\nvarious state-of-the-art pre-trained models: encoder-only and\nsequence-to-sequence; and conduct quantitative and qualitative error analysis.\nWe found that pre-trained multilingual models generally outperform monolingual\nmodels on reference text and ASR output and encoders outperform\nsequence-to-sequence models in NER tasks. By translating the transcripts, the\ndataset can also be utilised for text NER in the medical domain in other\nlanguages than Vietnamese. All code, data and models are publicly available:\nhttps://github.com/leduckhai/MultiMed/tree/master/VietMed-NER.\n", "date": "2024-06-19", "categories": []}
{"id": "2504.01309", "title": "Biomedical Question Answering via Multi-Level Summarization on a Local\n  Knowledge Graph", "abstract": "  In Question Answering (QA), Retrieval Augmented Generation (RAG) has\nrevolutionized performance in various domains. However, how to effectively\ncapture multi-document relationships, particularly critical for biomedical\ntasks, remains an open question. In this work, we propose a novel method that\nutilizes propositional claims to construct a local knowledge graph from\nretrieved documents. Summaries are then derived via layerwise summarization\nfrom the knowledge graph to contextualize a small language model to perform QA.\nWe achieved comparable or superior performance with our method over RAG\nbaselines on several biomedical QA benchmarks. We also evaluated each\nindividual step of our methodology over a targeted set of metrics,\ndemonstrating its effectiveness.\n", "date": "2025-04-02", "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": "2411.11779", "title": "LLM-IE: A Python Package for Generative Information Extraction with\n  Large Language Models", "abstract": "  Objectives: Despite the recent adoption of large language models (LLMs) for\nbiomedical information extraction, challenges in prompt engineering and\nalgorithms persist, with no dedicated software available. To address this, we\ndeveloped LLM-IE: a Python package for building complete information extraction\npipelines. Our key innovation is an interactive LLM agent to support schema\ndefinition and prompt design.\n  Materials and Methods: The LLM-IE supports named entity recognition, entity\nattribute extraction, and relation extraction tasks. We benchmarked on the i2b2\ndatasets and conducted a system evaluation.\n  Results: The sentence-based prompting algorithm resulted in the best\nperformance while requiring a longer inference time. System evaluation provided\nintuitive visualization.\n  Discussion: LLM-IE was designed from practical NLP experience in healthcare\nand has been adopted in internal projects. It should hold great value to the\nbiomedical NLP community.\n  Conclusion: We developed a Python package, LLM-IE, that provides building\nblocks for robust information extraction pipeline construction.\n", "date": "2024-11-18", "categories": []}
{"id": "2412.11923", "title": "PICLe: Pseudo-Annotations for In-Context Learning in Low-Resource Named\n  Entity Detection", "abstract": "  In-context learning (ICL) enables Large Language Models (LLMs) to perform\ntasks using few demonstrations, facilitating task adaptation when labeled\nexamples are hard to obtain. However, ICL is sensitive to the choice of\ndemonstrations, and it remains unclear which demonstration attributes enable\nin-context generalization. In this work, we conduct a perturbation study of\nin-context demonstrations for low-resource Named Entity Detection (NED). Our\nsurprising finding is that in-context demonstrations with partially correct\nannotated entity mentions can be as effective for task transfer as fully\ncorrect demonstrations. Based off our findings, we propose Pseudo-annotated\nIn-Context Learning (PICLe), a framework for in-context learning with noisy,\npseudo-annotated demonstrations. PICLe leverages LLMs to annotate many\ndemonstrations in a zero-shot first pass. We then cluster these synthetic\ndemonstrations, sample specific sets of in-context demonstrations from each\ncluster, and predict entity mentions using each set independently. Finally, we\nuse self-verification to select the final set of entity mentions. We evaluate\nPICLe on five biomedical NED datasets and show that, with zero human\nannotation, PICLe outperforms ICL in low-resource settings where limited gold\nexamples can be used as in-context demonstrations.\n", "date": "2024-12-16", "categories": []}
{"id": "2503.20794", "title": "Can Zero-Shot Commercial APIs Deliver Regulatory-Grade Clinical Text\n  DeIdentification?", "abstract": "  We evaluate the performance of four leading solutions for de-identification\nof unstructured medical text - Azure Health Data Services, AWS Comprehend\nMedical, OpenAI GPT-4o, and John Snow Labs - on a ground truth dataset of 48\nclinical documents annotated by medical experts. The analysis, conducted at\nboth entity-level and token-level, suggests that John Snow Labs' Medical\nLanguage Models solution achieves the highest accuracy, with a 96% F1-score in\nprotected health information (PHI) detection, outperforming Azure (91%), AWS\n(83%), and GPT-4o (79%). John Snow Labs is not only the only solution which\nachieves regulatory-grade accuracy (surpassing that of human experts) but is\nalso the most cost-effective solution: It is over 80% cheaper compared to Azure\nand GPT-4o, and is the only solution not priced by token. Its fixed-cost local\ndeployment model avoids the escalating per-request fees of cloud-based\nservices, making it a scalable and economical choice.\n", "date": "2025-03-21", "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": "2504.00748", "title": "IHC-LLMiner: Automated extraction of tumour immunohistochemical profiles\n  from PubMed abstracts using large language models", "abstract": "  Immunohistochemistry (IHC) is essential in diagnostic pathology and\nbiomedical research, offering critical insights into protein expression and\ntumour biology. This study presents an automated pipeline, IHC-LLMiner, for\nextracting IHC-tumour profiles from PubMed abstracts, leveraging advanced\nbiomedical text mining. There are two subtasks: abstract classification\n(include/exclude as relevant) and IHC-tumour profile extraction on relevant\nincluded abstracts. The best-performing model, \"Gemma-2 finetuned\", achieved\n91.5% accuracy and an F1 score of 91.4, outperforming GPT4-O by 9.5% accuracy\nwith 5.9 times faster inference time. From an initial dataset of 107,759\nabstracts identified for 50 immunohistochemical markers, the classification\ntask identified 30,481 relevant abstracts (Include) using the Gemma-2 finetuned\nmodel. For IHC-tumour profile extraction, the Gemma-2 finetuned model achieved\nthe best performance with 63.3% Correct outputs. Extracted IHC-tumour profiles\n(tumour types and markers) were normalised to Unified Medical Language System\n(UMLS) concepts to ensure consistency and facilitate IHC-tumour profile\nlandscape analysis. The extracted IHC-tumour profiles demonstrated excellent\nconcordance with available online summary data and provided considerable added\nvalue in terms of both missing IHC-tumour profiles and quantitative\nassessments. Our proposed LLM based pipeline provides a practical solution for\nlarge-scale IHC-tumour profile data mining, enhancing the accessibility and\nutility of such data for research and clinical applications as well as enabling\nthe generation of quantitative and structured data to support cancer-specific\nknowledge base development. Models and training datasets are available at\nhttps://github.com/knowlab/IHC-LLMiner.\n", "date": "2025-04-01", "categories": []}
{"id": "2409.19581", "title": "DiMB-RE: Mining the Scientific Literature for Diet-Microbiome\n  Associations", "abstract": "  Objective: To develop a corpus annotated for diet-microbiome associations\nfrom the biomedical literature and train natural language processing (NLP)\nmodels to identify these associations, thereby improving the understanding of\ntheir role in health and disease, and supporting personalized nutrition\nstrategies. Materials and Methods: We constructed DiMB-RE, a comprehensive\ncorpus annotated with 15 entity types (e.g., Nutrient, Microorganism) and 13\nrelation types (e.g., INCREASES, IMPROVES) capturing diet-microbiome\nassociations. We fine-tuned and evaluated state-of-the-art NLP models for named\nentity, trigger, and relation extraction as well as factuality detection using\nDiMB-RE. In addition, we benchmarked two generative large language models\n(GPT-4o-mini and GPT-4o) on a subset of the dataset in zero- and one-shot\nsettings. Results: DiMB-RE consists of 14,450 entities and 4,206 relationships\nfrom 165 publications (including 30 full-text Results sections). Fine-tuned NLP\nmodels performed reasonably well for named entity recognition (0.800 F1 score),\nwhile end-to-end relation extraction performance was modest (0.445 F1). The use\nof Results section annotations improved relation extraction. The impact of\ntrigger detection was mixed. Generative models showed lower accuracy compared\nto fine-tuned models. Discussion: To our knowledge, DiMB-RE is the largest and\nmost diverse corpus focusing on diet-microbiome interactions. NLP models\nfine-tuned on DiMB-RE exhibit lower performance compared to similar corpora,\nhighlighting the complexity of information extraction in this domain.\nMisclassified entities, missed triggers, and cross-sentence relations are the\nmajor sources of relation extraction errors. Conclusions: DiMB-RE can serve as\na benchmark corpus for biomedical literature mining. DiMB-RE and the NLP models\nare available at https://github.com/ScienceNLP-Lab/DiMB-RE.\n", "date": "2024-09-29", "categories": []}
{"id": "2503.23029", "title": "A Retrieval-Augmented Knowledge Mining Method with Deep Thinking LLMs\n  for Biomedical Research and Clinical Support", "abstract": "  Knowledge graphs and large language models (LLMs) are key tools for\nbiomedical knowledge integration and reasoning, facilitating structured\norganization of scientific articles and discovery of complex semantic\nrelationships. However, current methods face challenges: knowledge graph\nconstruction is limited by complex terminology, data heterogeneity, and rapid\nknowledge evolution, while LLMs show limitations in retrieval and reasoning,\nmaking it difficult to uncover cross-document associations and reasoning\npathways. To address these issues, we propose a pipeline that uses LLMs to\nconstruct a biomedical knowledge graph (BioStrataKG) from large-scale articles\nand builds a cross-document question-answering dataset (BioCDQA) to evaluate\nlatent knowledge retrieval and multi-hop reasoning. We then introduce\nIntegrated and Progressive Retrieval-Augmented Reasoning (IP-RAR) to enhance\nretrieval accuracy and knowledge reasoning. IP-RAR maximizes information recall\nthrough Integrated Reasoning-based Retrieval and refines knowledge via\nProgressive Reasoning-based Generation, using self-reflection to achieve deep\nthinking and precise contextual understanding. Experiments show that IP-RAR\nimproves document retrieval F1 score by 20\\% and answer generation accuracy by\n25\\% over existing methods. This framework helps doctors efficiently integrate\ntreatment evidence for personalized medication plans and enables researchers to\nanalyze advancements and research gaps, accelerating scientific discovery and\ndecision-making.\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": "2503.22092", "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": "2503.20179", "title": "ProtoBERT-LoRA: Parameter-Efficient Prototypical Finetuning for\n  Immunotherapy Study Identification", "abstract": "  Identifying immune checkpoint inhibitor (ICI) studies in genomic repositories\nlike Gene Expression Omnibus (GEO) is vital for cancer research yet remains\nchallenging due to semantic ambiguity, extreme class imbalance, and limited\nlabeled data in low-resource settings. We present ProtoBERT-LoRA, a hybrid\nframework that combines PubMedBERT with prototypical networks and Low-Rank\nAdaptation (LoRA) for efficient fine-tuning. The model enforces class-separable\nembeddings via episodic prototype training while preserving biomedical domain\nknowledge. Our dataset was divided as: Training (20 positive, 20 negative),\nPrototype Set (10 positive, 10 negative), Validation (20 positive, 200\nnegative), and Test (71 positive, 765 negative). Evaluated on test dataset,\nProtoBERT-LoRA achieved F1-score of 0.624 (precision: 0.481, recall: 0.887),\noutperforming the rule-based system, machine learning baselines and finetuned\nPubMedBERT. Application to 44,287 unlabeled studies reduced manual review\nefforts by 82%. Ablation studies confirmed that combining prototypes with LoRA\nimproved performance by 29% over stand-alone LoRA.\n", "date": "2025-03-26", "categories": []}
{"id": "2503.20568", "title": "Low-resource Information Extraction with the European Clinical Case\n  Corpus", "abstract": "  We present E3C-3.0, a multilingual dataset in the medical domain, comprising\nclinical cases annotated with diseases and test-result relations. The dataset\nincludes both native texts in five languages (English, French, Italian, Spanish\nand Basque) and texts translated and projected from the English source into\nfive target languages (Greek, Italian, Polish, Slovak, and Slovenian). A\nsemi-automatic approach has been implemented, including automatic annotation\nprojection based on Large Language Models (LLMs) and human revision. We present\nseveral experiments showing that current state-of-the-art LLMs can benefit from\nbeing fine-tuned on the E3C-3.0 dataset. We also show that transfer learning in\ndifferent languages is very effective, mitigating the scarcity of data.\nFinally, we compare performance both on native data and on projected data. We\nrelease the data at\nhttps://huggingface.co/collections/NLP-FBK/e3c-projected-676a7d6221608d60e4e9fd89 .\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": "2502.14714", "title": "From Knowledge Generation to Knowledge Verification: Examining the\n  BioMedical Generative Capabilities of ChatGPT", "abstract": "  The generative capabilities of LLM models offer opportunities for\naccelerating tasks but raise concerns about the authenticity of the knowledge\nthey produce. To address these concerns, we present a computational approach\nthat evaluates the factual accuracy of biomedical knowledge generated by an\nLLM. Our approach consists of two processes: generating disease-centric\nassociations and verifying these associations using the semantic framework of\nbiomedical ontologies. Using ChatGPT as the selected LLM, we designed\nprompt-engineering processes to establish linkages between diseases and related\ndrugs, symptoms, and genes, and assessed consistency across multiple ChatGPT\nmodels (e.g., GPT-turbo, GPT-4, etc.). Experimental results demonstrate high\naccuracy in identifying disease terms (88%-97%), drug names (90%-91%), and\ngenetic information (88%-98%). However, symptom term identification was notably\nlower (49%-61%), due to the informal and verbose nature of symptom\ndescriptions, which hindered effective semantic matching with the formal\nlanguage of specialized ontologies. Verification of associations reveals\nliterature coverage rates of 89%-91% for disease-drug and disease-gene pairs,\nwhile symptom-related associations exhibit lower coverage (49%-62%).\n", "date": "2025-02-20", "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": "2503.18539", "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": "2503.15718", "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": "2503.11384", "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": "2404.12827", "title": "An Evaluation Benchmark for Adverse Drug Event Prediction from Clinical\n  Trial Results", "abstract": "  Adverse drug events (ADEs) are a major safety issue in clinical trials. Thus,\npredicting ADEs is key to developing safer medications and enhancing patient\noutcomes. To support this effort, we introduce CT-ADE, a dataset for multilabel\nADE prediction in monopharmacy treatments. CT-ADE encompasses 2,497 drugs and\n168,984 drug-ADE pairs from clinical trial results, annotated using the MedDRA\nontology. Unlike existing resources, CT-ADE integrates treatment and target\npopulation data, enabling comparative analyses under varying conditions, such\nas dosage, administration route, and demographics. In addition, CT-ADE\nsystematically collects all ADEs in the study population, including positive\nand negative cases. To provide a baseline for ADE prediction performance using\nthe CT-ADE dataset, we conducted analyses using large language models (LLMs).\nThe best LLM achieved an F1-score of 56%, with models incorporating treatment\nand patient information outperforming by 21%-38% those relying solely on the\nchemical structure. These findings underscore the importance of contextual\ninformation in ADE prediction and establish CT-ADE as a robust resource for\nsafety risk assessment in pharmaceutical research and development.\n", "date": "2024-04-19", "categories": []}
{"id": "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": "2411.19295", "title": "Extracting Information in a Low-resource Setting: Case Study on\n  Bioinformatics Workflows", "abstract": "  Bioinformatics workflows are essential for complex biological data analyses\nand are often described in scientific articles with source code in public\nrepositories. Extracting detailed workflow information from articles can\nimprove accessibility and reusability but is hindered by limited annotated\ncorpora. To address this, we framed the problem as a low-resource extraction\ntask and tested four strategies: 1) creating a tailored annotated corpus, 2)\nfew-shot named-entity recognition (NER) with an autoregressive language model,\n3) NER using masked language models with existing and new corpora, and 4)\nintegrating workflow knowledge into NER models. Using BioToFlow, a new corpus\nof 52 articles annotated with 16 entities, a SciBERT-based NER model achieved a\n70.4 F-measure, comparable to inter-annotator agreement. While knowledge\nintegration improved performance for specific entities, it was less effective\nacross the entire information schema. Our results demonstrate that\nhigh-performance information extraction for bioinformatics workflows is\nachievable.\n", "date": "2024-11-28", "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": "2503.06888", "title": "A LongFormer-Based Framework for Accurate and Efficient Medical Text\n  Summarization", "abstract": "  This paper proposes a medical text summarization method based on LongFormer,\naimed at addressing the challenges faced by existing models when processing\nlong medical texts. Traditional summarization methods are often limited by\nshort-term memory, leading to information loss or reduced summary quality in\nlong texts. LongFormer, by introducing long-range self-attention, effectively\ncaptures long-range dependencies in the text, retaining more key information\nand improving the accuracy and information retention of summaries. Experimental\nresults show that the LongFormer-based model outperforms traditional models,\nsuch as RNN, T5, and BERT in automatic evaluation metrics like ROUGE. It also\nreceives high scores in expert evaluations, particularly excelling in\ninformation retention and grammatical accuracy. However, there is still room\nfor improvement in terms of conciseness and readability. Some experts noted\nthat the generated summaries contain redundant information, which affects\nconciseness. Future research will focus on further optimizing the model\nstructure to enhance conciseness and fluency, achieving more efficient medical\ntext summarization. As medical data continues to grow, automated summarization\ntechnology will play an increasingly important role in fields such as medical\nresearch, clinical decision support, and knowledge management.\n", "date": "2025-03-10", "categories": []}
{"id": "2406.00226", "title": "Entangled Relations: Leveraging NLI and Meta-analysis to Enhance\n  Biomedical Relation Extraction", "abstract": "  Recent research efforts have explored the potential of leveraging natural\nlanguage inference (NLI) techniques to enhance relation extraction (RE). In\nthis vein, we introduce MetaEntailRE, a novel adaptation method that harnesses\nNLI principles to enhance RE performance. Our approach follows past works by\nverbalizing relation classes into class-indicative hypotheses, aligning a\ntraditionally multi-class classification task to one of textual entailment. We\nintroduce three key enhancements: (1) Meta-class analysis which, instead of\nlabeling non-entailed premise-hypothesis pairs with the less informative\n\"neutral\" entailment label, provides additional context by analyzing\noverarching meta-relationships between classes; (2) Feasible hypothesis\nfiltering, which removes unlikely hypotheses from consideration based on domain\nknowledge derived from data; and (3) Group-based prediction selection, which\nfurther improves performance by selecting highly confident predictions.\nMetaEntailRE is conceptually simple and empirically powerful, yielding\nsignificant improvements over conventional relation extraction techniques and\nother NLI formulations. We observe surprisingly large F1 gains of 17.6 points\non BioRED and 13.4 points on ReTACRED compared to conventional methods,\nunderscoring the versatility of MetaEntailRE across both biomedical and general\ndomains.\n", "date": "2024-05-31", "categories": []}
{"id": "2503.04930", "title": "HILGEN: Hierarchically-Informed Data Generation for Biomedical NER Using\n  Knowledgebases and Large Language Models", "abstract": "  We present HILGEN, a Hierarchically-Informed Data Generation approach that\ncombines domain knowledge from the Unified Medical Language System (UMLS) with\nsynthetic data generated by large language models (LLMs), specifically GPT-3.5.\nOur approach leverages UMLS's hierarchical structure to expand training data\nwith related concepts, while incorporating contextual information from LLMs\nthrough targeted prompts aimed at automatically generating synthetic examples\nfor sparsely occurring named entities. The performance of the HILGEN approach\nwas evaluated across four biomedical NER datasets (MIMIC III, BC5CDR,\nNCBI-Disease, and Med-Mentions) using BERT-Large and DANN (Data Augmentation\nwith Nearest Neighbor Classifier) models, applying various data generation\nstrategies, including UMLS, GPT-3.5, and their best ensemble. For the\nBERT-Large model, incorporating UMLS led to an average F1 score improvement of\n40.36%, while using GPT-3.5 resulted in a comparable average increase of\n40.52%. The Best-Ensemble approach using BERT-Large achieved the highest\nimprovement, with an average increase of 42.29%. DANN model's F1 score improved\nby 22.74% on average using the UMLS-only approach. The GPT-3.5-based method\nresulted in a 21.53% increase, and the Best-Ensemble DANN model showed a more\nnotable improvement, with an average increase of 25.03%. Our proposed HILGEN\napproach improves NER performance in few-shot settings without requiring\nadditional manually annotated data. Our experiments demonstrate that an\neffective strategy for optimizing biomedical NER is to combine biomedical\nknowledge curated in the past, such as the UMLS, and generative LLMs to create\nsynthetic training instances. Our future research will focus on exploring\nadditional innovative synthetic data generation strategies for further\nimproving NER performance.\n", "date": "2025-03-06", "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": "2503.04013", "title": "Benchmarking Large Language Models on Multiple Tasks in Bioinformatics\n  NLP with Prompting", "abstract": "  Large language models (LLMs) have become important tools in solving\nbiological problems, offering improvements in accuracy and adaptability over\nconventional methods. Several benchmarks have been proposed to evaluate the\nperformance of these LLMs. However, current benchmarks can hardly evaluate the\nperformance of these models across diverse tasks effectively. In this paper, we\nintroduce a comprehensive prompting-based benchmarking framework, termed\nBio-benchmark, which includes 30 key bioinformatics tasks covering areas such\nas proteins, RNA, drugs, electronic health records, and traditional Chinese\nmedicine. Using this benchmark, we evaluate six mainstream LLMs, including\nGPT-4o and Llama-3.1-70b, etc., using 0-shot and few-shot Chain-of-Thought\n(CoT) settings without fine-tuning to reveal their intrinsic capabilities. To\nimprove the efficiency of our evaluations, we demonstrate BioFinder, a new tool\nfor extracting answers from LLM responses, which increases extraction accuracy\nby round 30% compared to existing methods. Our benchmark results show the\nbiological tasks suitable for current LLMs and identify specific areas\nrequiring enhancement. Furthermore, we propose targeted prompt engineering\nstrategies for optimizing LLM performance in these contexts. Based on these\nfindings, we provide recommendations for the development of more robust LLMs\ntailored for various biological applications. This work offers a comprehensive\nevaluation framework and robust tools to support the application of LLMs in\nbioinformatics.\n", "date": "2025-03-06", "categories": []}
{"id": "2503.04155", "title": "BPQA Dataset: Evaluating How Well Language Models Leverage Blood\n  Pressures to Answer Biomedical Questions", "abstract": "  Clinical measurements such as blood pressures and respiration rates are\ncritical in diagnosing and monitoring patient outcomes. It is an important\ncomponent of biomedical data, which can be used to train transformer-based\nlanguage models (LMs) for improving healthcare delivery. It is, however,\nunclear whether LMs can effectively interpret and use clinical measurements. We\ninvestigate two questions: First, can LMs effectively leverage clinical\nmeasurements to answer related medical questions? Second, how to enhance an\nLM's performance on medical question-answering (QA) tasks that involve\nmeasurements? We performed a case study on blood pressure readings (BPs), a\nvital sign routinely monitored by medical professionals. We evaluated the\nperformance of four LMs: BERT, BioBERT, MedAlpaca, and GPT-3.5, on our newly\ndeveloped dataset, BPQA (Blood Pressure Question Answering). BPQA contains\n$100$ medical QA pairs that were verified by medical students and designed to\nrely on BPs . We found that GPT-3.5 and MedAlpaca (larger and medium sized LMs)\nbenefit more from the inclusion of BPs than BERT and BioBERT (small sized LMs).\nFurther, augmenting measurements with labels improves the performance of\nBioBERT and Medalpaca (domain specific LMs), suggesting that retrieval may be\nuseful for improving domain-specific LMs.\n", "date": "2025-03-06", "categories": []}
{"id": "2503.04176", "title": "TIMER: Temporal Instruction Modeling and Evaluation for Longitudinal\n  Clinical Records", "abstract": "  Large language models (LLMs) have emerged as promising tools for assisting in\nmedical tasks, yet processing Electronic Health Records (EHRs) presents unique\nchallenges due to their longitudinal nature. While LLMs' capabilities to\nperform medical tasks continue to improve, their ability to reason over\ntemporal dependencies across multiple patient visits and time frames remains\nunexplored. We introduce TIMER (Temporal Instruction Modeling and Evaluation\nfor Longitudinal Clinical Records), a framework that incorporate\ninstruction-response pairs grounding to different parts of a patient's record\nas a critical dimension in both instruction evaluation and tuning for\nlongitudinal clinical records. We develop TIMER-Bench, the first time-aware\nbenchmark that evaluates temporal reasoning capabilities over longitudinal\nEHRs, as well as TIMER-Instruct, an instruction-tuning methodology for LLMs to\nlearn reasoning over time. We demonstrate that models fine-tuned with\nTIMER-Instruct improve performance by 7.3% on human-generated benchmarks and\n9.2% on TIMER-Bench, indicating that temporal instruction-tuning improves model\nperformance for reasoning over EHR.\n", "date": "2025-03-06", "categories": []}
{"id": "2409.13744", "title": "A Simplified Retriever to Improve Accuracy of Phenotype Normalizations\n  by Large Language Models", "abstract": "  Large language models (LLMs) have shown improved accuracy in phenotype term\nnormalization tasks when augmented with retrievers that suggest candidate\nnormalizations based on term definitions. In this work, we introduce a\nsimplified retriever that enhances LLM accuracy by searching the Human\nPhenotype Ontology (HPO) for candidate matches using contextual word embeddings\nfrom BioBERT without the need for explicit term definitions. Testing this\nmethod on terms derived from the clinical synopses of Online Mendelian\nInheritance in Man (OMIM), we demonstrate that the normalization accuracy of a\nstate-of-the-art LLM increases from a baseline of 62.3% without augmentation to\n90.3% with retriever augmentation. This approach is potentially generalizable\nto other biomedical term normalization tasks and offers an efficient\nalternative to more complex retrieval methods.\n", "date": "2024-09-11", "categories": []}
{"id": "2502.12671", "title": "Baichuan-M1: Pushing the Medical Capability of Large Language Models", "abstract": "  The current generation of large language models (LLMs) is typically designed\nfor broad, general-purpose applications, while domain-specific LLMs, especially\nin vertical fields like medicine, remain relatively scarce. In particular, the\ndevelopment of highly efficient and practical LLMs for the medical domain is\nchallenging due to the complexity of medical knowledge and the limited\navailability of high-quality data. To bridge this gap, we introduce\nBaichuan-M1, a series of large language models specifically optimized for\nmedical applications. Unlike traditional approaches that simply continue\npretraining on existing models or apply post-training to a general base model,\nBaichuan-M1 is trained from scratch with a dedicated focus on enhancing medical\ncapabilities. Our model is trained on 20 trillion tokens and incorporates a\nrange of effective training methods that strike a balance between general\ncapabilities and medical expertise. As a result, Baichuan-M1 not only performs\nstrongly across general domains such as mathematics and coding but also excels\nin specialized medical fields. We have open-sourced Baichuan-M1-14B, a mini\nversion of our model, which can be accessed through the following links.\n", "date": "2025-02-18", "categories": []}
{"id": "2503.02053", "title": "EPEE: Towards Efficient and Effective Foundation Models in Biomedicine", "abstract": "  Foundation models, including language models, e.g., GPT, and vision models,\ne.g., CLIP, have significantly advanced numerous biomedical tasks. Despite\nthese advancements, the high inference latency and the \"overthinking\" issues in\nmodel inference impair the efficiency and effectiveness of foundation models,\nthus limiting their application in real-time clinical settings. To address\nthese challenges, we proposed EPEE (Entropy- and Patience-based Early Exiting),\na novel hybrid strategy designed to improve the inference efficiency of\nfoundation models. The core idea was to leverage the strengths of entropy-based\nand patience-based early exiting methods to overcome their respective\nweaknesses. To evaluate EPEE, we conducted experiments on three core biomedical\ntasks-classification, relation extraction, and event extraction-using four\nfoundation models (BERT, ALBERT, GPT-2, and ViT) across twelve datasets,\nincluding clinical notes and medical images. The results showed that EPEE\nsignificantly reduced inference time while maintaining or improving accuracy,\ndemonstrating its adaptability to diverse datasets and tasks. EPEE addressed\ncritical barriers to deploying foundation models in healthcare by balancing\nefficiency and effectiveness. It potentially provided a practical solution for\nreal-time clinical decision-making with foundation models, supporting reliable\nand efficient workflows.\n", "date": "2025-03-03", "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": "2503.01194", "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": "2502.21290", "title": "Contextualizing biological perturbation experiments through language", "abstract": "  High-content perturbation experiments allow scientists to probe biomolecular\nsystems at unprecedented resolution, but experimental and analysis costs pose\nsignificant barriers to widespread adoption. Machine learning has the potential\nto guide efficient exploration of the perturbation space and extract novel\ninsights from these data. However, current approaches neglect the semantic\nrichness of the relevant biology, and their objectives are misaligned with\ndownstream biological analyses. In this paper, we hypothesize that large\nlanguage models (LLMs) present a natural medium for representing complex\nbiological relationships and rationalizing experimental outcomes. We propose\nPerturbQA, a benchmark for structured reasoning over perturbation experiments.\nUnlike current benchmarks that primarily interrogate existing knowledge,\nPerturbQA is inspired by open problems in perturbation modeling: prediction of\ndifferential expression and change of direction for unseen perturbations, and\ngene set enrichment. We evaluate state-of-the-art machine learning and\nstatistical approaches for modeling perturbations, as well as standard LLM\nreasoning strategies, and we find that current methods perform poorly on\nPerturbQA. As a proof of feasibility, we introduce Summer (SUMMarize, retrievE,\nand answeR, a simple, domain-informed LLM framework that matches or exceeds the\ncurrent state-of-the-art. Our code and data are publicly available at\nhttps://github.com/genentech/PerturbQA.\n", "date": "2025-02-28", "categories": []}
{"id": "2502.20350", "title": "KEDRec-LM: A Knowledge-distilled Explainable Drug Recommendation Large\n  Language Model", "abstract": "  Drug discovery is a critical task in biomedical natural language processing\n(NLP), yet explainable drug discovery remains underexplored. Meanwhile, large\nlanguage models (LLMs) have shown remarkable abilities in natural language\nunderstanding and generation. Leveraging LLMs for explainable drug discovery\nhas the potential to improve downstream tasks and real-world applications. In\nthis study, we utilize open-source drug knowledge graphs, clinical trial data,\nand PubMed publications to construct a comprehensive dataset for the\nexplainable drug discovery task, named \\textbf{expRxRec}. Furthermore, we\nintroduce \\textbf{KEDRec-LM}, an instruction-tuned LLM which distills knowledge\nfrom rich medical knowledge corpus for drug recommendation and rationale\ngeneration. To encourage further research in this area, we will publicly\nrelease\\footnote{A copy is attached with this submission} both the dataset and\nKEDRec-LM.\n", "date": "2025-02-27", "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": "2309.02233", "title": "Augmenting Black-box LLMs with Medical Textbooks for Biomedical Question\n  Answering", "abstract": "  Large-scale language models (LLMs) like ChatGPT have demonstrated impressive\nabilities in generating responses based on human instructions. However, their\nuse in the medical field can be challenging due to their lack of specific,\nin-depth knowledge. In this study, we present a system called LLMs Augmented\nwith Medical Textbooks (LLM-AMT) designed to enhance the proficiency of LLMs in\nspecialized domains. LLM-AMT integrates authoritative medical textbooks into\nthe LLMs' framework using plug-and-play modules. These modules include a Query\nAugmenter, a Hybrid Textbook Retriever, and a Knowledge Self-Refiner. Together,\nthey incorporate authoritative medical knowledge. Additionally, an LLM Reader\naids in contextual understanding. Our experimental results on three medical QA\ntasks demonstrate that LLMAMT significantly improves response quality, with\naccuracy gains ranging from 11.6% to 16.6%. Notably, with GPT-4-Turbo as the\nbase model, LLM-AMT outperforms the specialized Med-PaLM 2 model pre-trained on\na massive amount of medical corpus by 2-3%. We found that despite being 100x\nsmaller in size, medical textbooks as a retrieval corpus is proven to be a more\neffective knowledge database than Wikipedia in the medical domain, boosting\nperformance by 7.8%-13.7%.\n", "date": "2023-09-05", "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": "2408.06583", "title": "A Structure-aware Generative Model for Biomedical Event Extraction", "abstract": "  Biomedical Event Extraction (BEE) is a challenging task that involves\nmodeling complex relationships between fine-grained entities in biomedical\ntext. BEE has traditionally been formulated as a classification problem. With\nrecent advancements in large language models (LLMs), generation-based models\nthat cast event extraction as a sequence generation problem have attracted\nattention in the NLP research community. However, current generative models\noften overlook cross-instance information in complex event structures, such as\nnested and overlapping events, which constitute over 20% of events in benchmark\ndatasets. In this paper, we propose GenBEE, an event structure-aware generative\nmodel that captures complex event structures in biomedical text for biomedical\nevent extraction. GenBEE constructs event prompts that distill knowledge from\nLLMs to incorporate both label semantics and argument dependency relationships.\nIn addition, GenBEE generates prefixes with event structural prompts to\nincorporate structural features to improve the model's overall performance. We\nhave evaluated the proposed GenBEE model on three widely used BEE benchmark\ndatasets, namely MLEE, GE11, and PHEE. Experimental results show that GenBEE\nhas achieved state-of-the-art performance on the MLEE and GE11 datasets, and\nachieved competitive results when compared to the state-of-the-art\nclassification-based models on the PHEE dataset.\n", "date": "2024-08-13", "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": "2502.14144", "title": "UM_FHS at TREC 2024 PLABA: Exploration of Fine-tuning and AI agent\n  approach for plain language adaptations of biomedical text", "abstract": "  This paper describes our submissions to the TREC 2024 PLABA track with the\naim to simplify biomedical abstracts for a K8-level audience (13-14 years old\nstudents). We tested three approaches using OpenAI's gpt-4o and gpt-4o-mini\nmodels: baseline prompt engineering, a two-AI agent approach, and fine-tuning.\nAdaptations were evaluated using qualitative metrics (5-point Likert scales for\nsimplicity, accuracy, completeness, and brevity) and quantitative readability\nscores (Flesch-Kincaid grade level, SMOG Index). Results indicated that the\ntwo-agent approach and baseline prompt engineering with gpt-4o-mini models show\nsuperior qualitative performance, while fine-tuned models excelled in accuracy\nand completeness but were less simple. The evaluation results demonstrated that\nprompt engineering with gpt-4o-mini outperforms iterative improvement\nstrategies via two-agent approach as well as fine-tuning with gpt-4o. We intend\nto expand our investigation of the results and explore advanced evaluations.\n", "date": "2025-02-19", "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": "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": "2411.07381", "title": "MaLei at the PLABA Track of TREC 2024: RoBERTa for Term Replacement --\n  LLaMA3.1 and GPT-4o for Complete Abstract Adaptation", "abstract": "  This report is the system description of the MaLei team (Manchester and\nLeiden) for the shared task Plain Language Adaptation of Biomedical Abstracts\n(PLABA) 2024 (we had an earlier name BeeManc following last year), affiliated\nwith TREC2024 (33rd Text REtrieval Conference\nhttps://ir.nist.gov/evalbase/conf/trec-2024). This report contains two sections\ncorresponding to the two sub-tasks in PLABA-2024. In task one (term\nreplacement), we applied fine-tuned ReBERTa-Base models to identify and\nclassify the difficult terms, jargon, and acronyms in the biomedical abstracts\nand reported the F1 score (Task 1A and 1B). In task two (complete abstract\nadaptation), we leveraged Llamma3.1-70B-Instruct and GPT-4o with the one-shot\nprompts to complete the abstract adaptation and reported the scores in BLEU,\nSARI, BERTScore, LENS, and SALSA. From the official Evaluation from PLABA-2024\non Task 1A and 1B, our much smaller fine-tuned RoBERTa-Base model ranked 3rd\nand 2nd respectively on the two sub-tasks, and the 1st on averaged F1 scores\nacross the two tasks from 9 evaluated systems. Our LLaMA-3.1-70B-instructed\nmodel achieved the highest Completeness score for Task 2. We share our source\ncodes, fine-tuned models, and related resources at\nhttps://github.com/HECTA-UoM/PLABA2024\n", "date": "2024-11-11", "categories": []}
{"id": "2502.09659", "title": "Cancer Vaccine Adjuvant Name Recognition from Biomedical Literature\n  using Large Language Models", "abstract": "  Motivation: An adjuvant is a chemical incorporated into vaccines that\nenhances their efficacy by improving the immune response. Identifying adjuvant\nnames from cancer vaccine studies is essential for furthering research and\nenhancing immunotherapies. However, the manual curation from the constantly\nexpanding biomedical literature poses significant challenges. This study\nexplores the automated recognition of vaccine adjuvant names using Large\nLanguage Models (LLMs), specifically Generative Pretrained Transformers (GPT)\nand Large Language Model Meta AI (Llama). Methods: We utilized two datasets: 97\nclinical trial records from AdjuvareDB and 290 abstracts annotated with the\nVaccine Adjuvant Compendium (VAC). GPT-4o and Llama 3.2 were employed in\nzero-shot and few-shot learning paradigms with up to four examples per prompt.\nPrompts explicitly targeted adjuvant names, testing the impact of contextual\ninformation such as substances or interventions. Outputs underwent automated\nand manual validation for accuracy and consistency. Results: GPT-4o attained\n100% Precision across all situations while exhibiting notable improve in Recall\nand F1-scores, particularly with incorporating interventions. On the VAC\ndataset, GPT-4o achieved a maximum F1-score of 77.32% with interventions,\nsurpassing Llama-3.2-3B by approximately 2%. On the AdjuvareDB dataset, GPT-4o\nreached an F1-score of 81.67% for three-shot prompting with interventions,\nsurpassing Llama-3.2-3 B's maximum F1-score of 65.62%. Conclusion: Our findings\ndemonstrate that LLMs excel at identifying adjuvant names, including rare\nvariations of naming representation. This study emphasizes the capability of\nLLMs to enhance cancer vaccine development by efficiently extracting insights.\nFuture work aims to broaden the framework to encompass various biomedical\nliterature and enhance model generalizability across various vaccines and\nadjuvants.\n", "date": "2025-02-12", "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": "2502.07755", "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": "2410.14399", "title": "SylloBio-NLI: Evaluating Large Language Models on Biomedical Syllogistic\n  Reasoning", "abstract": "  Syllogistic reasoning is crucial for Natural Language Inference (NLI). This\ncapability is particularly significant in specialized domains such as\nbiomedicine, where it can support automatic evidence interpretation and\nscientific discovery. This paper presents SylloBio-NLI, a novel framework that\nleverages external ontologies to systematically instantiate diverse syllogistic\narguments for biomedical NLI. We employ SylloBio-NLI to evaluate Large Language\nModels (LLMs) on identifying valid conclusions and extracting supporting\nevidence across 28 syllogistic schemes instantiated with human genome pathways.\nExtensive experiments reveal that biomedical syllogistic reasoning is\nparticularly challenging for zero-shot LLMs, which achieve an average accuracy\nbetween 70% on generalized modus ponens and 23% on disjunctive syllogism. At\nthe same time, we found that few-shot prompting can boost the performance of\ndifferent LLMs, including Gemma (+14%) and LLama-3 (+43%). However, a deeper\nanalysis shows that both techniques exhibit high sensitivity to superficial\nlexical variations, highlighting a dependency between reliability, models'\narchitecture, and pre-training regime. Overall, our results indicate that,\nwhile in-context examples have the potential to elicit syllogistic reasoning in\nLLMs, existing models are still far from achieving the robustness and\nconsistency required for safe biomedical NLI applications.\n", "date": "2024-10-18", "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": "2502.03004", "title": "MedBioLM: Optimizing Medical and Biological QA with Fine-Tuned Large\n  Language Models and Retrieval-Augmented Generation", "abstract": "  Large Language Models (LLMs) have demonstrated impressive capabilities across\nnatural language processing tasks. However, their application to specialized\ndomains such as medicine and biology requires further optimization to ensure\nfactual accuracy, reliability, and contextual depth. We introduce MedBioLM, a\ndomain-adapted biomedical question-answering model designed to enhance both\nshort-form and long-form queries. By integrating fine-tuning and\nretrieval-augmented generation (RAG), MedBioLM dynamically incorporates\ndomain-specific knowledge, improving reasoning abilities and factual accuracy.\nTo evaluate its effectiveness, we fine-tuned the model on diverse biomedical QA\ndatasets, covering structured multiple-choice assessments and complex clinical\nreasoning tasks. Fine-tuning significantly improves accuracy on benchmark\ndatasets, while RAG enhances factual consistency. These results highlight the\npotential of domain-optimized LLMs in advancing biomedical research, medical\neducation, and clinical decision support.\n", "date": "2025-02-05", "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": "2502.01691", "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": "2502.01243", "title": "OphthBench: A Comprehensive Benchmark for Evaluating Large Language\n  Models in Chinese Ophthalmology", "abstract": "  Large language models (LLMs) have shown significant promise across various\nmedical applications, with ophthalmology being a notable area of focus. Many\nophthalmic tasks have shown substantial improvement through the integration of\nLLMs. However, before these models can be widely adopted in clinical practice,\nevaluating their capabilities and identifying their limitations is crucial. To\naddress this research gap and support the real-world application of LLMs, we\nintroduce the OphthBench, a specialized benchmark designed to assess LLM\nperformance within the context of Chinese ophthalmic practices. This benchmark\nsystematically divides a typical ophthalmic clinical workflow into five key\nscenarios: Education, Triage, Diagnosis, Treatment, and Prognosis. For each\nscenario, we developed multiple tasks featuring diverse question types,\nresulting in a comprehensive benchmark comprising 9 tasks and 591 questions.\nThis comprehensive framework allows for a thorough assessment of LLMs'\ncapabilities and provides insights into their practical application in Chinese\nophthalmology. Using this benchmark, we conducted extensive experiments and\nanalyzed the results from 39 popular LLMs. Our evaluation highlights the\ncurrent gap between LLM development and its practical utility in clinical\nsettings, providing a clear direction for future advancements. By bridging this\ngap, we aim to unlock the potential of LLMs and advance their development in\nophthalmology.\n", "date": "2025-02-03", "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": "2301.12473", "title": "Large Language Models for Biomedical Knowledge Graph Construction:\n  Information extraction from EMR notes", "abstract": "  The automatic construction of knowledge graphs (KGs) is an important research\narea in medicine, with far-reaching applications spanning drug discovery and\nclinical trial design. These applications hinge on the accurate identification\nof interactions among medical and biological entities. In this study, we\npropose an end-to-end machine learning solution based on large language models\n(LLMs) that utilize electronic medical record notes to construct KGs. The\nentities used in the KG construction process are diseases, factors, treatments,\nas well as manifestations that coexist with the patient while experiencing the\ndisease. Given the critical need for high-quality performance in medical\napplications, we embark on a comprehensive assessment of 12 LLMs of various\narchitectures, evaluating their performance and safety attributes. To gauge the\nquantitative efficacy of our approach by assessing both precision and recall,\nwe manually annotate a dataset provided by the Macula and Retina Institute. We\nalso assess the qualitative performance of LLMs, such as the ability to\ngenerate structured outputs or the tendency to hallucinate. The results\nillustrate that in contrast to encoder-only and encoder-decoder, decoder-only\nLLMs require further investigation. Additionally, we provide guided prompt\ndesign to utilize such LLMs. The application of the proposed methodology is\ndemonstrated on age-related macular degeneration.\n", "date": "2023-01-29", "categories": []}
{"id": "2501.17190", "title": "A Comprehensive Study on Fine-Tuning Large Language Models for Medical\n  Question Answering Using Classification Models and Comparative Analysis", "abstract": "  This paper presents the overview of the development and fine-tuning of large\nlanguage models (LLMs) designed specifically for answering medical questions.\nWe are mainly improving the accuracy and efficiency of providing reliable\nanswers to medical queries. In our approach, we have two stages, prediction of\na specific label for the received medical question and then providing a\npredefined answer for this label. Various models such as RoBERTa and BERT were\nexamined and evaluated based on their ability. The models are trained using the\ndatasets derived from 6,800 samples that were scraped from Healthline. com with\nadditional synthetic data. For evaluation, we conducted a comparative study\nusing 5-fold cross-validation. For accessing performance we used metrics like,\naccuracy, precision, recall, and F1 score and also recorded the training time.\nThe performance of the models was evaluated using 5-fold cross-validation. The\nLoRA Roberta-large model achieved an accuracy of 78.47%, precision of 72.91%,\nrecall of 76.95%, and an F1 score of 73.56%. The Roberta-base model\ndemonstrated high performance with an accuracy of 99.87%, precision of 99.81%,\nrecall of 99.86%, and an F1 score of 99.82%. The Bert Uncased model showed\nstrong results with an accuracy of 95.85%, precision of 94.42%, recall of\n95.58%, and an F1 score of 94.72%. Lastly, the Bert Large Uncased model\nachieved the highest performance, with an accuracy, precision, recall, and F1\nscore of 100%. The results obtained have helped indicate the capability of the\nmodels in classifying the medical questions and generating accurate answers in\nthe prescription of improved health-related AI solutions.\n", "date": "2025-01-27", "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": "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": "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": "2501.16255", "title": "A foundation model for human-AI collaboration in medical literature\n  mining", "abstract": "  Systematic literature review is essential for evidence-based medicine,\nrequiring comprehensive analysis of clinical trial publications. However, the\napplication of artificial intelligence (AI) models for medical literature\nmining has been limited by insufficient training and evaluation across broad\ntherapeutic areas and diverse tasks. Here, we present LEADS, an AI foundation\nmodel for study search, screening, and data extraction from medical literature.\nThe model is trained on 633,759 instruction data points in LEADSInstruct,\ncurated from 21,335 systematic reviews, 453,625 clinical trial publications,\nand 27,015 clinical trial registries. We showed that LEADS demonstrates\nconsistent improvements over four cutting-edge generic large language models\n(LLMs) on six tasks. Furthermore, LEADS enhances expert workflows by providing\nsupportive references following expert requests, streamlining processes while\nmaintaining high-quality results. A study with 16 clinicians and medical\nresearchers from 14 different institutions revealed that experts collaborating\nwith LEADS achieved a recall of 0.81 compared to 0.77 experts working alone in\nstudy selection, with a time savings of 22.6%. In data extraction tasks,\nexperts using LEADS achieved an accuracy of 0.85 versus 0.80 without using\nLEADS, alongside a 26.9% time savings. These findings highlight the potential\nof specialized medical literature foundation models to outperform generic\nmodels, delivering significant quality and efficiency benefits when integrated\ninto expert workflows for medical literature mining.\n", "date": "2025-01-27", "categories": []}
{"id": "2501.14079", "title": "Enhancing Biomedical Relation Extraction with Directionality", "abstract": "  Biological relation networks contain rich information for understanding the\nbiological mechanisms behind the relationship of entities such as genes,\nproteins, diseases, and chemicals. The vast growth of biomedical literature\nposes significant challenges updating the network knowledge. The recent\nBiomedical Relation Extraction Dataset (BioRED) provides valuable manual\nannotations, facilitating the develop-ment of machine-learning and pre-trained\nlanguage model approaches for automatically identifying novel document-level\n(inter-sentence context) relationships. Nonetheless, its annotations lack\ndirectionality (subject/object) for the entity roles, essential for studying\ncomplex biological networks. Herein we annotate the entity roles of the\nrelationships in the BioRED corpus and subsequently propose a novel multi-task\nlanguage model with soft-prompt learning to jointly identify the relationship,\nnovel findings, and entity roles. Our results in-clude an enriched BioRED\ncorpus with 10,864 directionality annotations. Moreover, our proposed method\noutperforms existing large language models such as the state-of-the-art GPT-4\nand Llama-3 on two benchmarking tasks. Our source code and dataset are\navailable at https://github.com/ncbi-nlp/BioREDirect.\n", "date": "2025-01-23", "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": "2501.01158", "title": "Attending To Syntactic Information In Biomedical Event Extraction Via\n  Graph Neural Networks", "abstract": "  Many models are proposed in the literature on biomedical event\nextraction(BEE). Some of them use the shortest dependency path(SDP) information\nto represent the argument classification task. There is an issue with this\nrepresentation since even missing one word from the dependency parsing graph\nmay totally change the final prediction. To this end, the full adjacency matrix\nof the dependency graph is used to embed individual tokens using a graph\nconvolutional network(GCN). An ablation study is also done to show the effect\nof the dependency graph on the overall performance. The results show a\nsignificant improvement when dependency graph information is used. The proposed\nmodel slightly outperforms state-of-the-art models on BEE over different\ndatasets.\n", "date": "2025-01-02", "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": "2311.03056", "title": "LitSumm: Large language models for literature summarisation of\n  non-coding RNAs", "abstract": "  Curation of literature in life sciences is a growing challenge. The continued\nincrease in the rate of publication, coupled with the relatively fixed number\nof curators worldwide presents a major challenge to developers of biomedical\nknowledgebases. Very few knowledgebases have resources to scale to the whole\nrelevant literature and all have to prioritise their efforts.\n  In this work, we take a first step to alleviating the lack of curator time in\nRNA science by generating summaries of literature for non-coding RNAs using\nlarge language models (LLMs). We demonstrate that high-quality, factually\naccurate summaries with accurate references can be automatically generated from\nthe literature using a commercial LLM and a chain of prompts and checks. Manual\nassessment was carried out for a subset of summaries, with the majority being\nrated extremely high quality.\n  We apply our tool to a selection of over 4,600 ncRNAs and make the generated\nsummaries available via the RNAcentral resource. We conclude that automated\nliterature summarization is feasible with the current generation of LLMs,\nprovided careful prompting and automated checking are applied.\n", "date": "2023-11-06", "categories": []}
{"id": "2501.05155", "title": "Biomedical Relation Extraction via Adaptive Document-Relation\n  Cross-Mapping and Concept Unique Identifier", "abstract": "  Document-Level Biomedical Relation Extraction (Bio-RE) aims to identify\nrelations between biomedical entities within extensive texts, serving as a\ncrucial subfield of biomedical text mining. Existing Bio-RE methods struggle\nwith cross-sentence inference, which is essential for capturing relations\nspanning multiple sentences. Moreover, previous methods often overlook the\nincompleteness of documents and lack the integration of external knowledge,\nlimiting contextual richness. Besides, the scarcity of annotated data further\nhampers model training. Recent advancements in large language models (LLMs)\nhave inspired us to explore all the above issues for document-level Bio-RE.\nSpecifically, we propose a document-level Bio-RE framework via LLM Adaptive\nDocument-Relation Cross-Mapping (ADRCM) Fine-Tuning and Concept Unique\nIdentifier (CUI) Retrieval-Augmented Generation (RAG). First, we introduce the\nIteration-of-REsummary (IoRs) prompt for solving the data scarcity issue. In\nthis way, Bio-RE task-specific synthetic data can be generated by guiding\nChatGPT to focus on entity relations and iteratively refining synthetic data.\nNext, we propose ADRCM fine-tuning, a novel fine-tuning recipe that establishes\nmappings across different documents and relations, enhancing the model's\ncontextual understanding and cross-sentence inference capabilities. Finally,\nduring the inference, a biomedical-specific RAG approach, named CUI RAG, is\ndesigned to leverage CUIs as indexes for entities, narrowing the retrieval\nscope and enriching the relevant document contexts. Experiments conducted on\nthree Bio-RE datasets (GDA, CDR, and BioRED) demonstrate the state-of-the-art\nperformance of our proposed method by comparing it with other related works.\n", "date": "2025-01-09", "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": "2501.00644", "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": "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": "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": "2412.19346", "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": "2412.18154", "title": "GeneSUM: Large Language Model-based Gene Summary Extraction", "abstract": "  Emerging topics in biomedical research are continuously expanding, providing\na wealth of information about genes and their function. This rapid\nproliferation of knowledge presents unprecedented opportunities for scientific\ndiscovery and formidable challenges for researchers striving to keep abreast of\nthe latest advancements. One significant challenge is navigating the vast\ncorpus of literature to extract vital gene-related information, a\ntime-consuming and cumbersome task. To enhance the efficiency of this process,\nit is crucial to address several key challenges: (1) the overwhelming volume of\nliterature, (2) the complexity of gene functions, and (3) the automated\nintegration and generation. In response, we propose GeneSUM, a two-stage\nautomated gene summary extractor utilizing a large language model (LLM). Our\napproach retrieves and eliminates redundancy of target gene literature and then\nfine-tunes the LLM to refine and streamline the summarization process. We\nconducted extensive experiments to validate the efficacy of our proposed\nframework. The results demonstrate that LLM significantly enhances the\nintegration of gene-specific information, allowing more efficient\ndecision-making in ongoing research.\n", "date": "2024-12-24", "categories": []}
{"id": "2412.15236", "title": "CareBot: A Pioneering Full-Process Open-Source Medical Language Model", "abstract": "  Recently, both closed-source LLMs and open-source communities have made\nsignificant strides, outperforming humans in various general domains. However,\ntheir performance in specific professional domains such as medicine, especially\nwithin the open-source community, remains suboptimal due to the complexity of\nmedical knowledge. In this paper, we propose CareBot, a bilingual medical LLM,\nwhich leverages a comprehensive approach integrating continuous pre-training\n(CPT), supervised fine-tuning (SFT), and reinforcement learning with human\nfeedback (RLHF). Our novel two-stage CPT method, comprising Stable CPT and\nBoost CPT, effectively bridges the gap between general and domain-specific\ndata, facilitating a smooth transition from pre-training to fine-tuning and\nenhancing domain knowledge progressively. We also introduce DataRater, a model\ndesigned to assess data quality during CPT, ensuring that the training data is\nboth accurate and relevant. For SFT, we develope a large and diverse bilingual\ndataset, along with ConFilter, a metric to enhance multi-turn dialogue quality,\nwhich is crucial to improving the model's ability to handle more complex\ndialogues. The combination of high-quality data sources and innovative\ntechniques significantly improves CareBot's performance across a range of\nmedical applications. Our rigorous evaluations on Chinese and English\nbenchmarks confirm CareBot's effectiveness in medical consultation and\neducation. These advancements not only address current limitations in medical\nLLMs but also set a new standard for developing effective and reliable\nopen-source models in the medical domain. We will open-source the datasets and\nmodels later, contributing valuable resources to the research community.\n", "date": "2024-12-12", "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": "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": "2412.15258", "title": "DisEmbed: Transforming Disease Understanding through Embeddings", "abstract": "  The medical domain is vast and diverse, with many existing embedding models\nfocused on general healthcare applications. However, these models often\nstruggle to capture a deep understanding of diseases due to their broad\ngeneralization across the entire medical field. To address this gap, I present\nDisEmbed, a disease-focused embedding model. DisEmbed is trained on a synthetic\ndataset specifically curated to include disease descriptions, symptoms, and\ndisease-related Q\\&A pairs, making it uniquely suited for disease-related\ntasks. For evaluation, I benchmarked DisEmbed against existing medical models\nusing disease-specific datasets and the triplet evaluation method. My results\ndemonstrate that DisEmbed outperforms other models, particularly in identifying\ndisease-related contexts and distinguishing between similar diseases. This\nmakes DisEmbed highly valuable for disease-specific use cases, including\nretrieval-augmented generation (RAG) tasks, where its performance is\nparticularly robust.\n", "date": "2024-12-16", "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": "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": "2408.11848", "title": "MGH Radiology Llama: A Llama 3 70B Model for Radiology", "abstract": "  In recent years, the field of radiology has increasingly harnessed the power\nof artificial intelligence (AI) to enhance diagnostic accuracy, streamline\nworkflows, and improve patient care. Large language models (LLMs) have emerged\nas particularly promising tools, offering significant potential in assisting\nradiologists with report generation, clinical decision support, and patient\ncommunication. This paper presents an advanced radiology-focused large language\nmodel: MGH Radiology Llama. It is developed using the Llama 3 70B model,\nbuilding upon previous domain-specific models like Radiology-GPT and\nRadiology-Llama2. Leveraging a unique and comprehensive dataset from\nMassachusetts General Hospital, comprising over 6.5 million de-identified\nmedical reports across various imaging modalities, the model demonstrates\nsignificant improvements in generating accurate and clinically relevant\nradiology impressions given the corresponding findings. Our evaluation,\nincorporating both traditional metrics and a GPT-4-based assessment, highlights\nthe enhanced performance of this work over general-purpose LLMs.\n", "date": "2024-08-13", "categories": []}
{"id": "2412.10848", "title": "Large Language Models for Medical Forecasting -- Foresight 2", "abstract": "  Foresight 2 (FS2) is a large language model fine-tuned on hospital data for\nmodelling patient timelines (GitHub 'removed for anon'). It can understand\npatients' clinical notes and predict SNOMED codes for a wide range of\nbiomedical use cases, including diagnosis suggestions, risk forecasting, and\nprocedure and medication recommendations. FS2 is trained on the free text\nportion of the MIMIC-III dataset, firstly through extracting biomedical\nconcepts and then creating contextualised patient timelines, upon which the\nmodel is then fine-tuned. The results show significant improvement over the\nprevious state-of-the-art for the next new biomedical concept prediction (P/R -\n0.73/0.66 vs 0.52/0.32) and a similar improvement specifically for the next new\ndisorder prediction (P/R - 0.69/0.62 vs 0.46/0.25). Finally, on the task of\nrisk forecast, we compare our model to GPT-4-turbo (and a range of open-source\nbiomedical LLMs) and show that FS2 performs significantly better on such tasks\n(P@5 - 0.90 vs 0.65). This highlights the need to incorporate hospital data\ninto LLMs and shows that small models outperform much larger ones when\nfine-tuned on high-quality, specialised data.\n", "date": "2024-12-14", "categories": []}
{"id": "2412.10918", "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": "2412.11477", "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": "2401.07977", "title": "Towards Efficient Methods in Medical Question Answering using Knowledge\n  Graph Embeddings", "abstract": "  In Natural Language Processing (NLP), Machine Reading Comprehension (MRC) is\nthe task of answering a question based on a given context. To handle questions\nin the medical domain, modern language models such as BioBERT, SciBERT and even\nChatGPT are trained on vast amounts of in-domain medical corpora. However,\nin-domain pre-training is expensive in terms of time and resources. In this\npaper, we propose a resource-efficient approach for injecting domain knowledge\ninto a model without relying on such domain-specific pre-training.\n  Knowledge graphs are powerful resources for accessing medical information.\nBuilding on existing work, we introduce a method using Multi-Layer Perceptrons\n(MLPs) for aligning and integrating embeddings extracted from medical knowledge\ngraphs with the embedding spaces of pre-trained language models (LMs). The\naligned embeddings are fused with open-domain LMs BERT and RoBERTa that are\nfine-tuned for two MRC tasks, span detection (COVID-QA) and multiple-choice\nquestions (PubMedQA). We compare our method to prior techniques that rely on a\nvocabulary overlap for embedding alignment and show how our method circumvents\nthis requirement to deliver better performance. On both datasets, our method\nallows BERT/RoBERTa to either perform on par (occasionally exceeding) with\nstronger domain-specific models or show improvements in general over prior\ntechniques. With the proposed approach, we signal an alternative method to\nin-domain pre-training to achieve domain proficiency. Our code is available\nhere.\n", "date": "2024-01-15", "categories": []}
{"id": "2409.12741", "title": "Fine Tuning Large Language Models for Medicine: The Role and Importance\n  of Direct Preference Optimization", "abstract": "  Large Language Model (LLM) fine tuning is underutilized in the field of\nmedicine. Two of the most common methods of fine tuning are Supervised Fine\nTuning (SFT) and Direct Preference Optimization (DPO), but there is little\nguidance informing users when to use either technique. In this investigation,\nwe compare the performance of SFT and DPO for five common natural language\ntasks in medicine: Classification with text data, Classification with numeric\ndata, Clinical Reasoning, Summarization, and Clinical Triage. We find that SFT\nalone is sufficient for Classification with text data, whereas DPO improves\nperformance for the more complex tasks of Clinical Reasoning, Summarization and\nClinical Triage. Our results establish the role and importance of DPO fine\ntuning within medicine, and consequently call attention to current software\ngaps that prevent widespread deployment of this technique.\n", "date": "2024-09-19", "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": "2402.13253", "title": "BiMediX: Bilingual Medical Mixture of Experts LLM", "abstract": "  In this paper, we introduce BiMediX, the first bilingual medical mixture of\nexperts LLM designed for seamless interaction in both English and Arabic. Our\nmodel facilitates a wide range of medical interactions in English and Arabic,\nincluding multi-turn chats to inquire about additional details such as patient\nsymptoms and medical history, multiple-choice question answering, and\nopen-ended question answering. We propose a semi-automated English-to-Arabic\ntranslation pipeline with human refinement to ensure high-quality translations.\nWe also introduce a comprehensive evaluation benchmark for Arabic medical LLMs.\nFurthermore, we introduce BiMed1.3M, an extensive Arabic-English bilingual\ninstruction set covering 1.3 Million diverse medical interactions, resulting in\nover 632 million healthcare specialized tokens for instruction tuning. Our\nBiMed1.3M dataset includes 250k synthesized multi-turn doctor-patient chats and\nmaintains a 1:2 Arabic-to-English ratio. Our model outperforms state-of-the-art\nMed42 and Meditron by average absolute gains of 2.5% and 4.1%, respectively,\ncomputed across multiple medical evaluation benchmarks in English, while\noperating at 8-times faster inference. Moreover, our BiMediX outperforms the\ngeneric Arabic-English bilingual LLM, Jais-30B, by average absolute gains of\n10% on our Arabic medical benchmark and 15% on bilingual evaluations across\nmultiple datasets. Our project page with source code and trained model is\navailable at https://github.com/mbzuai-oryx/BiMediX .\n", "date": "2024-02-20", "categories": []}
{"id": "2412.08255", "title": "Accurate Medical Named Entity Recognition Through Specialized NLP Models", "abstract": "  This study evaluated the effect of BioBERT in medical text processing for the\ntask of medical named entity recognition. Through comparative experiments with\nmodels such as BERT, ClinicalBERT, SciBERT, and BlueBERT, the results showed\nthat BioBERT achieved the best performance in both precision and F1 score,\nverifying its applicability and superiority in the medical field. BioBERT\nenhances its ability to understand professional terms and complex medical texts\nthrough pre-training on biomedical data, providing a powerful tool for medical\ninformation extraction and clinical decision support. The study also explored\nthe privacy and compliance challenges of BioBERT when processing medical data,\nand proposed future research directions for combining other medical-specific\nmodels to improve generalization and robustness. With the development of deep\nlearning technology, the potential of BioBERT in application fields such as\nintelligent medicine, personalized treatment, and disease prediction will be\nfurther expanded. Future research can focus on the real-time and\ninterpretability of the model to promote its widespread application in the\nmedical field.\n", "date": "2024-12-11", "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": "2412.04512", "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": "2412.04254", "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": "2407.13492", "title": "Enhancing Biomedical Knowledge Discovery for Diseases: An Open-Source\n  Framework Applied on Rett Syndrome and Alzheimer's Disease", "abstract": "  The ever-growing volume of biomedical publications creates a critical need\nfor efficient knowledge discovery. In this context, we introduce an open-source\nend-to-end framework designed to construct knowledge around specific diseases\ndirectly from raw text. To facilitate research in disease-related knowledge\ndiscovery, we create two annotated datasets focused on Rett syndrome and\nAlzheimer's disease, enabling the identification of semantic relations between\nbiomedical entities. Extensive benchmarking explores various ways to represent\nrelations and entity representations, offering insights into optimal modeling\nstrategies for semantic relation detection and highlighting language models'\ncompetence in knowledge discovery. We also conduct probing experiments using\ndifferent layer representations and attention scores to explore transformers'\nability to capture semantic relations.\n", "date": "2024-07-18", "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": "2412.03531", "title": "A Review on Scientific Knowledge Extraction using Large Language Models\n  in Biomedical Sciences", "abstract": "  The rapid advancement of large language models (LLMs) has opened new\nboundaries in the extraction and synthesis of medical knowledge, particularly\nwithin evidence synthesis. This paper reviews the state-of-the-art applications\nof LLMs in the biomedical domain, exploring their effectiveness in automating\ncomplex tasks such as evidence synthesis and data extraction from a biomedical\ncorpus of documents. While LLMs demonstrate remarkable potential, significant\nchallenges remain, including issues related to hallucinations, contextual\nunderstanding, and the ability to generalize across diverse medical tasks. We\nhighlight critical gaps in the current research literature, particularly the\nneed for unified benchmarks to standardize evaluations and ensure reliability\nin real-world applications. In addition, we propose directions for future\nresearch, emphasizing the integration of state-of-the-art techniques such as\nretrieval-augmented generation (RAG) to enhance LLM performance in evidence\nsynthesis. By addressing these challenges and utilizing the strengths of LLMs,\nwe aim to improve access to medical literature and facilitate meaningful\ndiscoveries in healthcare.\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": "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": "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": "2411.17967", "title": "QuaLLM-Health: An Adaptation of an LLM-Based Framework for Quantitative\n  Data Extraction from Online Health Discussions", "abstract": "  Health-related discussions on social media like Reddit offer valuable\ninsights, but extracting quantitative data from unstructured text is\nchallenging. In this work, we present an adapted framework from QuaLLM into\nQuaLLM-Health for extracting clinically relevant quantitative data from Reddit\ndiscussions about glucagon-like peptide-1 (GLP-1) receptor agonists using large\nlanguage models (LLMs). We collected 410k posts and comments from five\nGLP-1-related communities using the Reddit API in July 2024. After filtering\nfor cancer-related discussions, 2,059 unique entries remained. We developed\nannotation guidelines to manually extract variables such as cancer\nsurvivorship, family cancer history, cancer types mentioned, risk perceptions,\nand discussions with physicians. Two domain-experts independently annotated a\nrandom sample of 100 entries to create a gold-standard dataset. We then\nemployed iterative prompt engineering with OpenAI's \"GPT-4o-mini\" on the\ngold-standard dataset to build an optimized pipeline that allowed us to extract\nvariables from the large dataset. The optimized LLM achieved accuracies above\n0.85 for all variables, with precision, recall and F1 score macro averaged >\n0.90, indicating balanced performance. Stability testing showed a 95% match\nrate across runs, confirming consistency. Applying the framework to the full\ndataset enabled efficient extraction of variables necessary for downstream\nanalysis, costing under $3 and completing in approximately one hour.\nQuaLLM-Health demonstrates that LLMs can effectively and efficiently extract\nclinically relevant quantitative data from unstructured social media content.\nIncorporating human expertise and iterative prompt refinement ensures accuracy\nand reliability. This methodology can be adapted for large-scale analysis of\npatient-generated data across various health domains, facilitating valuable\ninsights for healthcare research.\n", "date": "2024-11-27", "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": "2411.15666", "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": "2411.04118", "title": "Medical Adaptation of Large Language and Vision-Language Models: Are We\n  Making Progress?", "abstract": "  Several recent works seek to develop foundation models specifically for\nmedical applications, adapting general-purpose large language models (LLMs) and\nvision-language models (VLMs) via continued pretraining on publicly available\nbiomedical corpora. These works typically claim that such domain-adaptive\npretraining (DAPT) improves performance on downstream medical tasks, such as\nanswering medical licensing exam questions. In this paper, we compare seven\npublic \"medical\" LLMs and two VLMs against their corresponding base models,\narriving at a different conclusion: all medical VLMs and nearly all medical\nLLMs fail to consistently improve over their base models in the zero-/few-shot\nprompting regime for medical question-answering (QA) tasks. For instance,\nacross the tasks and model pairs we consider in the 3-shot setting, medical\nLLMs only outperform their base models in 12.1% of cases, reach a (statistical)\ntie in 49.8% of cases, and are significantly worse than their base models in\nthe remaining 38.2% of cases. Our conclusions are based on (i) comparing each\nmedical model head-to-head, directly against the corresponding base model; (ii)\noptimizing the prompts for each model separately; and (iii) accounting for\nstatistical uncertainty in comparisons. While these basic practices are not\nconsistently adopted in the literature, our ablations show that they\nsubstantially impact conclusions. Our findings suggest that state-of-the-art\ngeneral-domain models may already exhibit strong medical knowledge and\nreasoning capabilities, and offer recommendations to strengthen the conclusions\nof future studies.\n", "date": "2024-11-06", "categories": []}
{"id": "2411.12752", "title": "A Library Perspective on Supervised Text Processing in Digital\n  Libraries: An Investigation in the Biomedical Domain", "abstract": "  Digital libraries that maintain extensive textual collections may want to\nfurther enrich their content for certain downstream applications, e.g.,\nbuilding knowledge graphs, semantic enrichment of documents, or implementing\nnovel access paths. All of these applications require some text processing,\neither to identify relevant entities, extract semantic relationships between\nthem, or to classify documents into some categories. However, implementing\nreliable, supervised workflows can become quite challenging for a digital\nlibrary because suitable training data must be crafted, and reliable models\nmust be trained. While many works focus on achieving the highest accuracy on\nsome benchmarks, we tackle the problem from a digital library practitioner. In\nother words, we also consider trade-offs between accuracy and application\ncosts, dive into training data generation through distant supervision and large\nlanguage models such as ChatGPT, LLama, and Olmo, and discuss how to design\nfinal pipelines. Therefore, we focus on relation extraction and text\nclassification, using the showcase of eight biomedical benchmarks.\n", "date": "2024-11-06", "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": "2411.11635", "title": "Chapter 7 Review of Data-Driven Generative AI Models for Knowledge\n  Extraction from Scientific Literature in Healthcare", "abstract": "  This review examines the development of abstractive NLP-based text\nsummarization approaches and compares them to existing techniques for\nextractive summarization. A brief history of text summarization from the 1950s\nto the introduction of pre-trained language models such as Bidirectional\nEncoder Representations from Transformer (BERT) and Generative Pre-training\nTransformers (GPT) are presented. In total, 60 studies were identified in\nPubMed and Web of Science, of which 29 were excluded and 24 were read and\nevaluated for eligibility, resulting in the use of seven studies for further\nanalysis. This chapter also includes a section with examples including an\nexample of a comparison between GPT-3 and state-of-the-art GPT-4 solutions in\nscientific text summarisation. Natural language processing has not yet reached\nits full potential in the generation of brief textual summaries. As there are\nacknowledged concerns that must be addressed, we can expect gradual\nintroduction of such models in practise.\n", "date": "2024-11-18", "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": "2411.09255", "title": "DAHL: Domain-specific Automated Hallucination Evaluation of Long-Form\n  Text through a Benchmark Dataset in Biomedicine", "abstract": "  We introduce DAHL, a benchmark dataset and automated evaluation system\ndesigned to assess hallucination in long-form text generation, specifically\nwithin the biomedical domain. Our benchmark dataset, meticulously curated from\nbiomedical research papers, consists of 8,573 questions across 29 categories.\nDAHL evaluates fact-conflicting hallucinations in Large Language Models (LLMs)\nby deconstructing responses into atomic units, each representing a single piece\nof information. The accuracy of these responses is averaged to produce the DAHL\nScore, offering a more in-depth evaluation of hallucinations compared to\nprevious methods that rely on multiple-choice tasks. We conduct experiments\nwith 8 different models, finding that larger models tend to hallucinate less;\nhowever, beyond a model size of 7 to 8 billion parameters, further scaling does\nnot significantly improve factual accuracy. The DAHL Score holds potential as\nan efficient alternative to human-annotated preference labels, being able to be\nexpanded to other specialized domains. We release the dataset and code in\npublic.\n", "date": "2024-11-14", "categories": []}
{"id": "2311.08303", "title": "Extrinsically-Focused Evaluation of Omissions in Medical Summarization", "abstract": "  Large language models (LLMs) have shown promise in safety-critical\napplications such as healthcare, yet the ability to quantify performance has\nlagged. An example of this challenge is in evaluating a summary of the\npatient's medical record. A resulting summary can enable the provider to get a\nhigh-level overview of the patient's health status quickly. Yet, a summary that\nomits important facts about the patient's record can produce a misleading\npicture. This can lead to negative consequences on medical decision-making. We\npropose MED-OMIT as a metric to explore this challenge. We focus on using\nprovider-patient history conversations to generate a subjective (a summary of\nthe patient's history) as a case study. We begin by discretizing facts from the\ndialogue and identifying which are omitted from the subjective. To determine\nwhich facts are clinically relevant, we measure the importance of each fact to\na simulated differential diagnosis. We compare MED-OMIT's performance to that\nof clinical experts and find broad agreement We use MED-OMIT to evaluate LLM\nperformance on subjective generation and find some LLMs (gpt-4 and\nllama-3.1-405b) work well with little effort, while others (e.g. Llama 2)\nperform worse.\n", "date": "2023-11-14", "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": "2411.06713", "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": "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": "2411.02730", "title": "A Natural Language Processing Approach to Support Biomedical Data\n  Harmonization: Leveraging Large Language Models", "abstract": "  Biomedical research requires large, diverse samples to produce unbiased\nresults. Automated methods for matching variables across datasets can\naccelerate this process. Research in this area has been limited, primarily\nfocusing on lexical matching and ontology based semantic matching. We aimed to\ndevelop new methods, leveraging large language models (LLM) and ensemble\nlearning, to automate variable matching. Methods: We utilized data from two\nGERAS cohort (European and Japan) studies to develop variable matching methods.\nWe first manually created a dataset by matching 352 EU variables with 1322\ncandidate JP variables, where matched variable pairs were positive and\nunmatched pairs were negative instances. Using this dataset, we developed and\nevaluated two types of natural language processing (NLP) methods, which matched\nvariables based on variable labels and definitions from data dictionaries: (1)\nLLM-based and (2) fuzzy matching. We then developed an ensemble-learning\nmethod, using the Random Forest model, to integrate individual NLP methods. RF\nwas trained and evaluated on 50 trials. Each trial had a random split (4:1) of\ntraining and test sets, with the model's hyperparameters optimized through\ncross-validation on the training set. For each EU variable, 1322 candidate JP\nvariables were ranked based on NLP-derived similarity scores or RF's\nprobability scores, denoting their likelihood to match the EU variable. Ranking\nperformance was measured by top-n hit ratio (HRn) and mean reciprocal rank\n(MRR). Results:E5 performed best among individual methods, achieving 0.90 HR-30\nand 0.70 MRR. RF performed better than E5 on all metrics over 50 trials (P less\nthan 0.001) and achieved an average HR 30 of 0.98 and MRR of 0.73. LLM-derived\nfeatures contributed most to RF's performance. One major cause of errors in\nautomatic variable matching was ambiguous variable definitions within data\ndictionaries.\n", "date": "2024-11-05", "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": "2411.00041", "title": "NeuroSym-BioCAT: Leveraging Neuro-Symbolic Methods for Biomedical\n  Scholarly Document Categorization and Question Answering", "abstract": "  The growing volume of biomedical scholarly document abstracts presents an\nincreasing challenge in efficiently retrieving accurate and relevant\ninformation. To address this, we introduce a novel approach that integrates an\noptimized topic modelling framework, OVB-LDA, with the BI-POP CMA-ES\noptimization technique for enhanced scholarly document abstract categorization.\nComplementing this, we employ the distilled MiniLM model, fine-tuned on\ndomain-specific data, for high-precision answer extraction. Our approach is\nevaluated across three configurations: scholarly document abstract retrieval,\ngold-standard scholarly documents abstract, and gold-standard snippets,\nconsistently outperforming established methods such as RYGH and bio-answer\nfinder. Notably, we demonstrate that extracting answers from scholarly\ndocuments abstracts alone can yield high accuracy, underscoring the sufficiency\nof abstracts for many biomedical queries. Despite its compact size, MiniLM\nexhibits competitive performance, challenging the prevailing notion that only\nlarge, resource-intensive models can handle such complex tasks. Our results,\nvalidated across various question types and evaluation batches, highlight the\nrobustness and adaptability of our method in real-world biomedical\napplications. While our approach shows promise, we identify challenges in\nhandling complex list-type questions and inconsistencies in evaluation metrics.\nFuture work will focus on refining the topic model with more extensive\ndomain-specific datasets, further optimizing MiniLM and utilizing large\nlanguage models (LLM) to improve both precision and efficiency in biomedical\nquestion answering.\n", "date": "2024-10-29", "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": "2410.23583", "title": "BioNCERE: Non-Contrastive Enhancement For Relation Extraction In\n  Biomedical Texts", "abstract": "  State-of-the-art models for relation extraction (RE) in the biomedical domain\nconsider finetuning BioBERT using classification, but they may suffer from the\nanisotropy problem. Contrastive learning methods can reduce this anisotropy\nphenomena, and also help to avoid class collapse in any classification problem.\nIn the present paper, a new training method called biological non-contrastive\nrelation extraction (BioNCERE) is introduced for relation extraction without\nusing any named entity labels for training to reduce annotation costs. BioNCERE\nuses transfer learning and non-contrastive learning to avoid full or\ndimensional collapse as well as bypass overfitting. It resolves RE in three\nstages by leveraging transfer learning two times. By freezing the weights\nlearned in previous stages in the proposed pipeline and by leveraging\nnon-contrastive learning in the second stage, the model predicts relations\nwithout any knowledge of named entities. Experiments have been done on SemMedDB\nthat are almost similar to State-of-the-art performance on RE without using the\ninformation of named entities.\n", "date": "2024-10-31", "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": "2406.03949", "title": "UltraMedical: Building Specialized Generalists in Biomedicine", "abstract": "  Large Language Models (LLMs) have demonstrated remarkable capabilities across\nvarious domains and are moving towards more specialized areas. Recent advanced\nproprietary models such as GPT-4 and Gemini have achieved significant\nadvancements in biomedicine, which have also raised privacy and security\nchallenges. The construction of specialized generalists hinges largely on\nhigh-quality datasets, enhanced by techniques like supervised fine-tuning and\nreinforcement learning from human or AI feedback, and direct preference\noptimization. However, these leading technologies (e.g., preference learning)\nare still significantly limited in the open source community due to the\nscarcity of specialized data. In this paper, we present the UltraMedical\ncollections, which consist of high-quality manual and synthetic datasets in the\nbiomedicine domain, featuring preference annotations across multiple advanced\nLLMs. By utilizing these datasets, we fine-tune a suite of specialized medical\nmodels based on Llama-3 series, demonstrating breathtaking capabilities across\nvarious medical benchmarks. Moreover, we develop powerful reward models skilled\nin biomedical and general reward benchmark, enhancing further online preference\nlearning within the biomedical LLM community. Datasets and models are available\nat https://github.com/TsinghuaC3I/UltraMedical\n", "date": "2024-06-06", "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": "2410.21495", "title": "RoBIn: A Transformer-Based Model For Risk Of Bias Inference With Machine\n  Reading Comprehension", "abstract": "  Objective: Scientific publications play a crucial role in uncovering\ninsights, testing novel drugs, and shaping healthcare policies. Accessing the\nquality of publications requires evaluating their Risk of Bias (RoB), a process\ntypically conducted by human reviewers. In this study, we introduce a new\ndataset for machine reading comprehension and RoB assessment and present RoBIn\n(Risk of Bias Inference), an innovative model crafted to automate such\nevaluation. The model employs a dual-task approach, extracting evidence from a\ngiven context and assessing the RoB based on the gathered evidence. Methods: We\nuse data from the Cochrane Database of Systematic Reviews (CDSR) as ground\ntruth to label open-access clinical trial publications from PubMed. This\nprocess enabled us to develop training and test datasets specifically for\nmachine reading comprehension and RoB inference. Additionally, we created\nextractive (RoBInExt) and generative (RoBInGen) Transformer-based approaches to\nextract relevant evidence and classify the RoB effectively. Results: RoBIn is\nevaluated across various settings and benchmarked against state-of-the-art\nmethods for RoB inference, including large language models in multiple\nscenarios. In most cases, the best-performing RoBIn variant surpasses\ntraditional machine learning and LLM-based approaches, achieving an ROC AUC of\n0.83. Conclusion: Based on the evidence extracted from clinical trial reports,\nRoBIn performs a binary classification to decide whether the trial is at a low\nRoB or a high/unclear RoB. We found that both RoBInGen and RoBInExt are robust\nand have the best results in many settings.\n", "date": "2024-10-28", "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": "2410.20792", "title": "Deep Learning for Medical Text Processing: BERT Model Fine-Tuning and\n  Comparative Study", "abstract": "  This paper proposes a medical literature summary generation method based on\nthe BERT model to address the challenges brought by the current explosion of\nmedical information. By fine-tuning and optimizing the BERT model, we develop\nan efficient summary generation system that can quickly extract key information\nfrom medical literature and generate coherent, accurate summaries. In the\nexperiment, we compared various models, including Seq-Seq, Attention,\nTransformer, and BERT, and demonstrated that the improved BERT model offers\nsignificant advantages in the Rouge and Recall metrics. Furthermore, the\nresults of this study highlight the potential of knowledge distillation\ntechniques to further enhance model performance. The system has demonstrated\nstrong versatility and efficiency in practical applications, offering a\nreliable tool for the rapid screening and analysis of medical literature.\n", "date": "2024-10-28", "categories": []}
{"id": "2308.06454", "title": "Demonstration-based learning for few-shot biomedical named entity\n  recognition under machine reading comprehension", "abstract": "  Although deep learning techniques have shown significant achievements, they\nfrequently depend on extensive amounts of hand-labeled data and tend to perform\ninadequately in few-shot scenarios. The objective of this study is to devise a\nstrategy that can improve the model's capability to recognize biomedical\nentities in scenarios of few-shot learning. By redefining biomedical named\nentity recognition (BioNER) as a machine reading comprehension (MRC) problem,\nwe propose a demonstration-based learning method to address few-shot BioNER,\nwhich involves constructing appropriate task demonstrations. In assessing our\nproposed method, we compared the proposed method with existing advanced methods\nusing six benchmark datasets, including BC4CHEMD, BC5CDR-Chemical,\nBC5CDR-Disease, NCBI-Disease, BC2GM, and JNLPBA. We examined the models'\nefficacy by reporting F1 scores from both the 25-shot and 50-shot learning\nexperiments. In 25-shot learning, we observed 1.1% improvements in the average\nF1 scores compared to the baseline method, reaching 61.7%, 84.1%, 69.1%, 70.1%,\n50.6%, and 59.9% on six datasets, respectively. In 50-shot learning, we further\nimproved the average F1 scores by 1.0% compared to the baseline method,\nreaching 73.1%, 86.8%, 76.1%, 75.6%, 61.7%, and 65.4%, respectively. We\nreported that in the realm of few-shot learning BioNER, MRC-based language\nmodels are much more proficient in recognizing biomedical entities compared to\nthe sequence labeling approach. Furthermore, our MRC-language models can\ncompete successfully with fully-supervised learning methodologies that rely\nheavily on the availability of abundant annotated data. These results highlight\npossible pathways for future advancements in few-shot BioNER methodologies.\n", "date": "2023-08-12", "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": "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": "2410.18090", "title": "Liver Cancer Knowledge Graph Construction based on dynamic entity\n  replacement and masking strategies RoBERTa-BiLSTM-CRF model", "abstract": "  Background: Liver cancer ranks as the fifth most common malignant tumor and\nthe second most fatal in our country. Early diagnosis is crucial, necessitating\nthat physicians identify liver cancer in patients at the earliest possible\nstage. However, the diagnostic process is complex and demanding. Physicians\nmust analyze a broad spectrum of patient data, encompassing physical condition,\nsymptoms, medical history, and results from various examinations and tests,\nrecorded in both structured and unstructured medical formats. This results in a\nsignificant workload for healthcare professionals. In response, integrating\nknowledge graph technology to develop a liver cancer knowledge graph-assisted\ndiagnosis and treatment system aligns with national efforts toward smart\nhealthcare. Such a system promises to mitigate the challenges faced by\nphysicians in diagnosing and treating liver cancer.\n  Methods: This paper addresses the major challenges in building a knowledge\ngraph for hepatocellular carcinoma diagnosis, such as the discrepancy between\npublic data sources and real electronic medical records, the effective\nintegration of which remains a key issue. The knowledge graph construction\nprocess consists of six steps: conceptual layer design, data preprocessing,\nentity identification, entity normalization, knowledge fusion, and graph\nvisualization. A novel Dynamic Entity Replacement and Masking Strategy (DERM)\nfor named entity recognition is proposed.\n  Results: A knowledge graph for liver cancer was established, including 7\nentity types such as disease, symptom, and constitution, containing 1495\nentities. The recognition accuracy of the model was 93.23%, the recall was\n94.69%, and the F1 score was 93.96%.\n", "date": "2024-10-08", "categories": []}
{"id": "2407.06292", "title": "Hybrid X-Linker: Automated Data Generation and Extreme Multi-label\n  Ranking for Biomedical Entity Linking", "abstract": "  State-of-the-art deep learning entity linking methods rely on extensive\nhuman-labelled data, which is costly to acquire. Current datasets are limited\nin size, leading to inadequate coverage of biomedical concepts and diminished\nperformance when applied to new data. In this work, we propose to automatically\ngenerate data to create large-scale training datasets, which allows the\nexploration of approaches originally developed for the task of extreme\nmulti-label ranking in the biomedical entity linking task. We propose the\nhybrid X-Linker pipeline that includes different modules to link disease and\nchemical entity mentions to concepts in the MEDIC and the CTD-Chemical\nvocabularies, respectively. X-Linker was evaluated on several biomedical\ndatasets: BC5CDR-Disease, BioRED-Disease, NCBI-Disease, BC5CDR-Chemical,\nBioRED-Chemical, and NLM-Chem, achieving top-1 accuracies of 0.8307, 0.7969,\n0.8271, 0.9511, 0.9248, and 0.7895, respectively. X-Linker demonstrated\nsuperior performance in three datasets: BC5CDR-Disease, NCBI-Disease, and\nBioRED-Chemical. In contrast, SapBERT outperformed X-Linker in the remaining\nthree datasets. Both models rely only on the mention string for their\noperations. The source code of X-Linker and its associated data are publicly\navailable for performing biomedical entity linking without requiring\npre-labelled entities with identifiers from specific knowledge organization\nsystems.\n", "date": "2024-07-08", "categories": []}
{"id": "2408.03871", "title": "Large Language Models for Biomedical Text Simplification: Promising But\n  Not There Yet", "abstract": "  In this system report, we describe the models and methods we used for our\nparticipation in the PLABA2023 task on biomedical abstract simplification, part\nof the TAC 2023 tracks. The system outputs we submitted come from the following\nthree categories: 1) domain fine-tuned T5-like models including Biomedical-T5\nand Lay-SciFive; 2) fine-tuned BARTLarge model with controllable attributes\n(via tokens) BART-w-CTs; 3) ChatGPTprompting. We also present the work we\ncarried out for this task on BioGPT finetuning. In the official automatic\nevaluation using SARI scores, BeeManc ranks 2nd among all teams and our model\nLaySciFive ranks 3rd among all 13 evaluated systems. In the official human\nevaluation, our model BART-w-CTs ranks 2nd on Sentence-Simplicity (score\n92.84), 3rd on Term-Simplicity (score 82.33) among all 7 evaluated systems; It\nalso produced a high score 91.57 on Fluency in comparison to the highest score\n93.53. In the second round of submissions, our team using ChatGPT-prompting\nranks the 2nd in several categories including simplified term accuracy score\n92.26 and completeness score 96.58, and a very similar score on faithfulness\nscore 95.3 to re-evaluated PLABA-base-1 (95.73) via human evaluations. Our\ncodes, fine-tuned models, prompts, and data splits from the system development\nstage will be available at https://github.com/ HECTA-UoM/PLABA-MU\n", "date": "2024-08-07", "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": "2410.15463", "title": "MedLogic-AQA: Enhancing Medical Question Answering with Abstractive\n  Models Focusing on Logical Structures", "abstract": "  In Medical question-answering (QA) tasks, the need for effective systems is\npivotal in delivering accurate responses to intricate medical queries. However,\nexisting approaches often struggle to grasp the intricate logical structures\nand relationships inherent in medical contexts, thus limiting their capacity to\nfurnish precise and nuanced answers. In this work, we address this gap by\nproposing a novel Abstractive QA system MedLogic-AQA that harnesses First Order\nLogic (FOL) based rules extracted from both context and questions to generate\nwell-grounded answers. Through initial experimentation, we identified six\npertinent first-order logical rules, which were then used to train a\nLogic-Understanding (LU) model capable of generating logical triples for a\ngiven context, question, and answer. These logic triples are then integrated\ninto the training of MedLogic-AQA, enabling effective and coherent reasoning\nduring answer generation. This distinctive fusion of logical reasoning with\nabstractive QA equips our system to produce answers that are logically sound,\nrelevant, and engaging. Evaluation with respect to both automated and\nhuman-based demonstrates the robustness of MedLogic-AQA against strong\nbaselines. Through empirical assessments and case studies, we validate the\nefficacy of MedLogic-AQA in elevating the quality and comprehensiveness of\nanswers in terms of reasoning as well as informativeness\n", "date": "2024-10-20", "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": "2410.10899", "title": "GPTON: Generative Pre-trained Transformers enhanced with Ontology\n  Narration for accurate annotation of biological data", "abstract": "  By leveraging GPT-4 for ontology narration, we developed GPTON to infuse\nstructured knowledge into LLMs through verbalized ontology terms, achieving\naccurate text and ontology annotations for over 68% of gene sets in the top\nfive predictions. Manual evaluations confirm GPTON's robustness, highlighting\nits potential to harness LLMs and structured knowledge to significantly advance\nbiomedical research beyond gene set annotation.\n", "date": "2024-10-12", "categories": []}
{"id": "2410.12845", "title": "Toward Relieving Clinician Burden by Automatically Generating Progress\n  Notes using Interim Hospital Data", "abstract": "  Regular documentation of progress notes is one of the main contributors to\nclinician burden. The abundance of structured chart information in medical\nrecords further exacerbates the burden, however, it also presents an\nopportunity to automate the generation of progress notes. In this paper, we\npropose a task to automate progress note generation using structured or tabular\ninformation present in electronic health records. To this end, we present a\nnovel framework and a large dataset, ChartPNG, for the task which contains\n$7089$ annotation instances (each having a pair of progress notes and interim\nstructured chart data) across $1616$ patients. We establish baselines on the\ndataset using large language models from general and biomedical domains. We\nperform both automated (where the best performing Biomistral model achieved a\nBERTScore F1 of $80.53$ and MEDCON score of $19.61$) and manual (where we found\nthat the model was able to leverage relevant structured data with $76.9\\%$\naccuracy) analyses to identify the challenges with the proposed task and\nopportunities for future research.\n", "date": "2024-10-10", "categories": []}
{"id": "2410.12856", "title": "Optimized Biomedical Question-Answering Services with LLM and Multi-BERT\n  Integration", "abstract": "  We present a refined approach to biomedical question-answering (QA) services\nby integrating large language models (LLMs) with Multi-BERT configurations. By\nenhancing the ability to process and prioritize vast amounts of complex\nbiomedical data, this system aims to support healthcare professionals in\ndelivering better patient outcomes and informed decision-making. Through\ninnovative use of BERT and BioBERT models, combined with a multi-layer\nperceptron (MLP) layer, we enable more specialized and efficient responses to\nthe growing demands of the healthcare sector. Our approach not only addresses\nthe challenge of overfitting by freezing one BERT model while training another\nbut also improves the overall adaptability of QA services. The use of extensive\ndatasets, such as BioASQ and BioMRC, demonstrates the system's ability to\nsynthesize critical information. This work highlights how advanced language\nmodels can make a tangible difference in healthcare, providing reliable and\nresponsive tools for professionals to manage complex information, ultimately\nserving the broader goal of improved care and data-driven insights.\n", "date": "2024-10-11", "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": "2403.04890", "title": "Few shot chain-of-thought driven reasoning to prompt LLMs for open ended\n  medical question answering", "abstract": "  In this paper, we propose a modified version of the MedQA-USMLE dataset,\nnamed MEDQA-OPEN, which contains open-ended medical questions without options\nto mimic clinical scenarios, along with clinician-approved reasoned answers.\nAdditionally, we implement a prompt driven by Chain of Thought (CoT) reasoning,\nCLINICR, to mirror the prospective process of incremental reasoning, reaching a\ncorrect response to medical questions. We empirically demonstrate how CLINICR\noutperforms the state-of-the-art 5-shot CoT-based prompt (Li\\'evin et al.,\n2022). We also present an approach that mirrors real-life clinical practice by\nfirst exploring multiple differential diagnoses through MCQ-CLINICR and\nsubsequently narrowing down to a final diagnosis using MCQ-ELIMINATIVE.\nFinally, emphasizing the importance of response verification in medical\nsettings, we utilize a reward model mechanism, replacing the elimination\nprocess performed by MCQ-ELIMINATIVE.\n", "date": "2024-03-07", "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": "2405.12701", "title": "OLAPH: Improving Factuality in Biomedical Long-form Question Answering", "abstract": "  In the medical domain, numerous scenarios necessitate the long-form\ngeneration ability of large language models (LLMs). Specifically, when\naddressing patients' questions, it is essential that the model's response\nconveys factual claims, highlighting the need for an automated method to\nevaluate those claims. Thus, we introduce MedLFQA, a benchmark dataset\nreconstructed using long-form question-answering datasets related to the\nbiomedical domain. We use MedLFQA to facilitate a cost-effective automatic\nevaluations of factuality. We also propose OLAPH, a simple and novel framework\nthat utilizes cost-effective and multifaceted automatic evaluation to construct\na synthetic preference set and answers questions in our preferred manner. Our\nframework leads us to train LLMs step-by-step to reduce hallucinations and\ninclude crucial medical claims. We highlight that, even on evaluation metrics\nnot used during training, LLMs trained with our OLAPH framework demonstrate\nsignificant performance improvement in factuality. Our findings reveal that a\n7B LLM trained with our OLAPH framework can provide long answers comparable to\nthe medical experts' answers in terms of factuality. We believe that our work\ncould shed light on gauging the long-text generation ability of LLMs in the\nmedical domain. Our code and datasets are available.\n", "date": "2024-05-21", "categories": []}
{"id": "2410.11550", "title": "Y-Mol: A Multiscale Biomedical Knowledge-Guided Large Language Model for\n  Drug Development", "abstract": "  Large Language Models (LLMs) have recently demonstrated remarkable\nperformance in general tasks across various fields. However, their\neffectiveness within specific domains such as drug development remains\nchallenges. To solve these challenges, we introduce \\textbf{Y-Mol}, forming a\nwell-established LLM paradigm for the flow of drug development. Y-Mol is a\nmultiscale biomedical knowledge-guided LLM designed to accomplish tasks across\nlead compound discovery, pre-clinic, and clinic prediction. By integrating\nmillions of multiscale biomedical knowledge and using LLaMA2 as the base LLM,\nY-Mol augments the reasoning capability in the biomedical domain by learning\nfrom a corpus of publications, knowledge graphs, and expert-designed synthetic\ndata. The capability is further enriched with three types of drug-oriented\ninstructions: description-based prompts from processed publications,\nsemantic-based prompts for extracting associations from knowledge graphs, and\ntemplate-based prompts for understanding expert knowledge from biomedical\ntools. Besides, Y-Mol offers a set of LLM paradigms that can autonomously\nexecute the downstream tasks across the entire process of drug development,\nincluding virtual screening, drug design, pharmacological properties\nprediction, and drug-related interaction prediction. Our extensive evaluations\nof various biomedical sources demonstrate that Y-Mol significantly outperforms\ngeneral-purpose LLMs in discovering lead compounds, predicting molecular\nproperties, and identifying drug interaction events.\n", "date": "2024-10-15", "categories": []}
{"id": "2402.12372", "title": "HunFlair2 in a cross-corpus evaluation of biomedical named entity\n  recognition and normalization tools", "abstract": "  With the exponential growth of the life science literature, biomedical text\nmining (BTM) has become an essential technology for accelerating the extraction\nof insights from publications. Identifying named entities (e.g., diseases,\ndrugs, or genes) in texts and their linkage to reference knowledge bases are\ncrucial steps in BTM pipelines to enable information aggregation from different\ndocuments. However, tools for these two steps are rarely applied in the same\ncontext in which they were developed. Instead, they are applied in the wild,\ni.e., on application-dependent text collections different from those used for\nthe tools' training, varying, e.g., in focus, genre, style, and text type. This\nraises the question of whether the reported performance of BTM tools can be\ntrusted for downstream applications. Here, we report on the results of a\ncarefully designed cross-corpus benchmark for named entity extraction, where\ntools were applied systematically to corpora not used during their training.\nBased on a survey of 28 published systems, we selected five for an in-depth\nanalysis on three publicly available corpora encompassing four different entity\ntypes. Comparison between tools results in a mixed picture and shows that, in a\ncross-corpus setting, the performance is significantly lower than the one\nreported in an in-corpus setting. HunFlair2 showed the best performance on\naverage, being closely followed by PubTator. Our results indicate that users of\nBTM tools should expect diminishing performances when applying them in the wild\ncompared to original publications and show that further research is necessary\nto make BTM tools more robust.\n", "date": "2024-02-19", "categories": []}
{"id": "2403.03640", "title": "Apollo: A Lightweight Multilingual Medical LLM towards Democratizing\n  Medical AI to 6B People", "abstract": "  Despite the vast repository of global medical knowledge predominantly being\nin English, local languages are crucial for delivering tailored healthcare\nservices, particularly in areas with limited medical resources. To extend the\nreach of medical AI advancements to a broader population, we aim to develop\nmedical LLMs across the six most widely spoken languages, encompassing a global\npopulation of 6.1 billion. This effort culminates in the creation of the\nApolloCorpora multilingual medical dataset and the XMedBench benchmark. In the\nmultilingual medical benchmark, the released Apollo models, at various\nrelatively-small sizes (i.e., 0.5B, 1.8B, 2B, 6B, and 7B), achieve the best\nperformance among models of equivalent size. Especially, Apollo-7B is the\nstate-of-the-art multilingual medical LLMs up to 70B. Additionally, these lite\nmodels could be used to improve the multi-lingual medical capabilities of\nlarger models without fine-tuning in a proxy-tuning fashion. We will\nopen-source training corpora, code, model weights and evaluation benchmark.\n", "date": "2024-03-06", "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": "2410.09631", "title": "Society of Medical Simplifiers", "abstract": "  Medical text simplification is crucial for making complex biomedical\nliterature more accessible to non-experts. Traditional methods struggle with\nthe specialized terms and jargon of medical texts, lacking the flexibility to\nadapt the simplification process dynamically. In contrast, recent advancements\nin large language models (LLMs) present unique opportunities by offering\nenhanced control over text simplification through iterative refinement and\ncollaboration between specialized agents. In this work, we introduce the\nSociety of Medical Simplifiers, a novel LLM-based framework inspired by the\n\"Society of Mind\" (SOM) philosophy. Our approach leverages the strengths of\nLLMs by assigning five distinct roles, i.e., Layperson, Simplifier, Medical\nExpert, Language Clarifier, and Redundancy Checker, organized into interaction\nloops. This structure allows the agents to progressively improve text\nsimplification while maintaining the complexity and accuracy of the original\ncontent. Evaluations on the Cochrane text simplification dataset demonstrate\nthat our framework is on par with or outperforms state-of-the-art methods,\nachieving superior readability and content preservation through controlled\nsimplification processes.\n", "date": "2024-10-12", "categories": []}
{"id": "2410.09632", "title": "SciGisPy: a Novel Metric for Biomedical Text Simplification via Gist\n  Inference Score", "abstract": "  Biomedical literature is often written in highly specialized language, posing\nsignificant comprehension challenges for non-experts. Automatic text\nsimplification (ATS) offers a solution by making such texts more accessible\nwhile preserving critical information. However, evaluating ATS for biomedical\ntexts is still challenging due to the limitations of existing evaluation\nmetrics. General-domain metrics like SARI, BLEU, and ROUGE focus on\nsurface-level text features, and readability metrics like FKGL and ARI fail to\naccount for domain-specific terminology or assess how well the simplified text\nconveys core meanings (gist). To address this, we introduce SciGisPy, a novel\nevaluation metric inspired by Gist Inference Score (GIS) from Fuzzy-Trace\nTheory (FTT). SciGisPy measures how well a simplified text facilitates the\nformation of abstract inferences (gist) necessary for comprehension, especially\nin the biomedical domain. We revise GIS for this purpose by introducing\ndomain-specific enhancements, including semantic chunking, Information Content\n(IC) theory, and specialized embeddings, while removing unsuitable indexes. Our\nexperimental evaluation on the Cochrane biomedical text simplification dataset\ndemonstrates that SciGisPy outperforms the original GIS formulation, with a\nsignificant increase in correctly identified simplified texts (84% versus\n44.8%). The results and a thorough ablation study confirm that SciGisPy better\ncaptures the essential meaning of biomedical content, outperforming existing\napproaches.\n", "date": "2024-10-12", "categories": []}
{"id": "2405.20354", "title": "Efficient Systematic Reviews: Literature Filtering with Transformers &\n  Transfer Learning", "abstract": "  Identifying critical research within the growing body of academic work is an\nintrinsic aspect of conducting quality research. Systematic review processes\nused in evidence-based medicine formalise this as a procedure that must be\nfollowed in a research program. However, it comes with an increasing burden in\nterms of the time required to identify the important articles of research for a\ngiven topic. In this work, we develop a method for building a general-purpose\nfiltering system that matches a research question, posed as a natural language\ndescription of the required content, against a candidate set of articles\nobtained via the application of broad search terms. Our results demonstrate\nthat transformer models, pre-trained on biomedical literature, and then fine\ntuned for the specific task, offer a promising solution to this problem. The\nmodel can remove large volumes of irrelevant articles for most research\nquestions. Furthermore, analysis of the specific research questions in our\ntraining data suggest natural avenues for further improvement.\n", "date": "2024-05-30", "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": "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": "2404.18443", "title": "BMRetriever: Tuning Large Language Models as Better Biomedical Text\n  Retrievers", "abstract": "  Developing effective biomedical retrieval models is important for excelling\nat knowledge-intensive biomedical tasks but still challenging due to the\ndeficiency of sufficient publicly annotated biomedical data and computational\nresources. We present BMRetriever, a series of dense retrievers for enhancing\nbiomedical retrieval via unsupervised pre-training on large biomedical corpora,\nfollowed by instruction fine-tuning on a combination of labeled datasets and\nsynthetic pairs. Experiments on 5 biomedical tasks across 11 datasets verify\nBMRetriever's efficacy on various biomedical applications. BMRetriever also\nexhibits strong parameter efficiency, with the 410M variant outperforming\nbaselines up to 11.7 times larger, and the 2B variant matching the performance\nof models with over 5B parameters. The training data and model checkpoints are\nreleased at \\url{https://huggingface.co/BMRetriever} to ensure transparency,\nreproducibility, and application to new domains.\n", "date": "2024-04-29", "categories": []}
{"id": "2409.18878", "title": "Suicide Phenotyping from Clinical Notes in Safety-Net Psychiatric\n  Hospital Using Multi-Label Classification with Pre-Trained Language Models", "abstract": "  Accurate identification and categorization of suicidal events can yield\nbetter suicide precautions, reducing operational burden, and improving care\nquality in high-acuity psychiatric settings. Pre-trained language models offer\npromise for identifying suicidality from unstructured clinical narratives. We\nevaluated the performance of four BERT-based models using two fine-tuning\nstrategies (multiple single-label and single multi-label) for detecting\ncoexisting suicidal events from 500 annotated psychiatric evaluation notes. The\nnotes were labeled for suicidal ideation (SI), suicide attempts (SA), exposure\nto suicide (ES), and non-suicidal self-injury (NSSI). RoBERTa outperformed\nother models using multiple single-label classification strategy (acc=0.86,\nF1=0.78). MentalBERT (acc=0.83, F1=0.74) also exceeded BioClinicalBERT\n(acc=0.82, F1=0.72) which outperformed BERT (acc=0.80, F1=0.70). RoBERTa\nfine-tuned with single multi-label classification further improved the model\nperformance (acc=0.88, F1=0.81). The findings highlight that the model\noptimization, pretraining with domain-relevant data, and the single multi-label\nclassification strategy enhance the model performance of suicide phenotyping.\nKeywords: EHR-based Phenotyping; Natural Language Processing; Secondary Use of\nEHR Data; Suicide Classification; BERT-based Model; Psychiatry; Mental Health\n", "date": "2024-09-27", "categories": []}
{"id": "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": "2410.00616", "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": "2409.17683", "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": "2310.20440", "title": "Integrating curation into scientific publishing to train AI models", "abstract": "  High throughput extraction and structured labeling of data from academic\narticles is critical to enable downstream machine learning applications and\nsecondary analyses. We have embedded multimodal data curation into the academic\npublishing process to annotate segmented figure panels and captions. Natural\nlanguage processing (NLP) was combined with human-in-the-loop feedback from the\noriginal authors to increase annotation accuracy. Annotation included eight\nclasses of bioentities (small molecules, gene products, subcellular components,\ncell lines, cell types, tissues, organisms, and diseases) plus additional\nclasses delineating the entities' roles in experiment designs and\nmethodologies. The resultant dataset, SourceData-NLP, contains more than\n620,000 annotated biomedical entities, curated from 18,689 figures in 3,223\narticles in molecular and cell biology. We evaluate the utility of the dataset\nto train AI models using named-entity recognition, segmentation of figure\ncaptions into their constituent panels, and a novel context-dependent semantic\ntask assessing whether an entity is a controlled intervention target or a\nmeasurement object. We also illustrate the use of our dataset in performing a\nmulti-modal task for segmenting figures into panel images and their\ncorresponding captions.\n", "date": "2023-10-31", "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": "2406.15534", "title": "Geneverse: A collection of Open-source Multimodal Large Language Models\n  for Genomic and Proteomic Research", "abstract": "  The applications of large language models (LLMs) are promising for biomedical\nand healthcare research. Despite the availability of open-source LLMs trained\nusing a wide range of biomedical data, current research on the applications of\nLLMs to genomics and proteomics is still limited. To fill this gap, we propose\na collection of finetuned LLMs and multimodal LLMs (MLLMs), known as Geneverse,\nfor three novel tasks in genomic and proteomic research. The models in\nGeneverse are trained and evaluated based on domain-specific datasets, and we\nuse advanced parameter-efficient finetuning techniques to achieve the model\nadaptation for tasks including the generation of descriptions for gene\nfunctions, protein function inference from its structure, and marker gene\nselection from spatial transcriptomic data. We demonstrate that adapted LLMs\nand MLLMs perform well for these tasks and may outperform closed-source\nlarge-scale models based on our evaluations focusing on both truthfulness and\nstructural correctness. All of the training strategies and base models we used\nare freely accessible.\n", "date": "2024-06-21", "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": "2409.15368", "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": "2409.16243", "title": "A fast and sound tagging method for discontinuous named-entity\n  recognition", "abstract": "  We introduce a novel tagging scheme for discontinuous named entity\nrecognition based on an explicit description of the inner structure of\ndiscontinuous mentions. We rely on a weighted finite state automaton for both\nmarginal and maximum a posteriori inference. As such, our method is sound in\nthe sense that (1) well-formedness of predicted tag sequences is ensured via\nthe automaton structure and (2) there is an unambiguous mapping between\nwell-formed sequences of tags and (discontinuous) mentions. We evaluate our\napproach on three English datasets in the biomedical domain, and report\ncomparable results to state-of-the-art while having a way simpler and faster\nmodel.\n", "date": "2024-09-24", "categories": []}
{"id": "2405.11950", "title": "WisPerMed at BioLaySumm: Adapting Autoregressive Large Language Models\n  for Lay Summarization of Scientific Articles", "abstract": "  This paper details the efforts of the WisPerMed team in the BioLaySumm2024\nShared Task on automatic lay summarization in the biomedical domain, aimed at\nmaking scientific publications accessible to non-specialists. Large language\nmodels (LLMs), specifically the BioMistral and Llama3 models, were fine-tuned\nand employed to create lay summaries from complex scientific texts. The\nsummarization performance was enhanced through various approaches, including\ninstruction tuning, few-shot learning, and prompt variations tailored to\nincorporate specific context information. The experiments demonstrated that\nfine-tuning generally led to the best performance across most evaluated\nmetrics. Few-shot learning notably improved the models' ability to generate\nrelevant and factually accurate texts, particularly when using a well-crafted\nprompt. Additionally, a Dynamic Expert Selection (DES) mechanism to optimize\nthe selection of text outputs based on readability and factuality metrics was\ndeveloped. Out of 54 participants, the WisPerMed team reached the 4th place,\nmeasured by readability, factuality, and relevance. Determined by the overall\nscore, our approach improved upon the baseline by approx. 5.5 percentage points\nand was only approx 1.5 percentage points behind the first place.\n", "date": "2024-05-20", "categories": []}
{"id": "2406.10303", "title": "A Survey on Large Language Models from General Purpose to Medical\n  Applications: Datasets, Methodologies, and Evaluations", "abstract": "  Large Language Models (LLMs) have demonstrated surprising performance across\nvarious natural language processing tasks. Recently, medical LLMs enhanced with\ndomain-specific knowledge have exhibited excellent capabilities in medical\nconsultation and diagnosis. These models can smoothly simulate doctor-patient\ndialogues and provide professional medical advice. Most medical LLMs are\ndeveloped through continued training of open-source general LLMs, which require\nsignificantly fewer computational resources than training LLMs from scratch.\nAdditionally, this approach offers better patient privacy protection than\nAPI-based solutions. Given the above advantages, this survey systematically\nsummarizes how to train medical LLMs based on open-source general LLMs from a\nmore fine-grained perspective. It covers (a) how to acquire training corpus and\nconstruct customized medical training sets, (b) how to choose an appropriate\ntraining paradigm, (c) how to choose a suitable evaluation benchmark, and (d)\nexisting challenges and promising research directions are discussed. This\nsurvey can provide guidance for the development of LLMs focused on various\nmedical applications, such as medical education, diagnostic planning, and\nclinical assistants. Related resources and supplemental information can be\nfound on the GitHub repository.\n", "date": "2024-06-14", "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": "2409.14579", "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": "2409.13317", "title": "JMedBench: A Benchmark for Evaluating Japanese Biomedical Large Language\n  Models", "abstract": "  Recent developments in Japanese large language models (LLMs) primarily focus\non general domains, with fewer advancements in Japanese biomedical LLMs. One\nobstacle is the absence of a comprehensive, large-scale benchmark for\ncomparison. Furthermore, the resources for evaluating Japanese biomedical LLMs\nare insufficient. To advance this field, we propose a new benchmark including\neight LLMs across four categories and 20 Japanese biomedical datasets across\nfive tasks. Experimental results indicate that: (1) LLMs with a better\nunderstanding of Japanese and richer biomedical knowledge achieve better\nperformance in Japanese biomedical tasks, (2) LLMs that are not mainly designed\nfor Japanese biomedical domains can still perform unexpectedly well, and (3)\nthere is still much room for improving the existing LLMs in certain Japanese\nbiomedical tasks. Moreover, we offer insights that could further enhance\ndevelopment in this field. Our evaluation tools tailored to our benchmark as\nwell as the datasets are publicly available in\nhttps://huggingface.co/datasets/Coldog2333/JMedBench to facilitate future\nresearch.\n", "date": "2024-09-20", "categories": []}
{"id": "2409.10011", "title": "HALO: Hallucination Analysis and Learning Optimization to Empower LLMs\n  with Retrieval-Augmented Context for Guided Clinical Decision Making", "abstract": "  Large language models (LLMs) have significantly advanced natural language\nprocessing tasks, yet they are susceptible to generating inaccurate or\nunreliable responses, a phenomenon known as hallucination. In critical domains\nsuch as health and medicine, these hallucinations can pose serious risks. This\npaper introduces HALO, a novel framework designed to enhance the accuracy and\nreliability of medical question-answering (QA) systems by focusing on the\ndetection and mitigation of hallucinations. Our approach generates multiple\nvariations of a given query using LLMs and retrieves relevant information from\nexternal open knowledge bases to enrich the context. We utilize maximum\nmarginal relevance scoring to prioritize the retrieved context, which is then\nprovided to LLMs for answer generation, thereby reducing the risk of\nhallucinations. The integration of LangChain further streamlines this process,\nresulting in a notable and robust increase in the accuracy of both open-source\nand commercial LLMs, such as Llama-3.1 (from 44% to 65%) and ChatGPT (from 56%\nto 70%). This framework underscores the critical importance of addressing\nhallucinations in medical QA systems, ultimately improving clinical\ndecision-making and patient care. The open-source HALO is available at:\nhttps://github.com/ResponsibleAILab/HALO.\n", "date": "2024-09-16", "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": "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": "2409.09704", "title": "AlpaPICO: Extraction of PICO Frames from Clinical Trial Documents Using\n  LLMs", "abstract": "  In recent years, there has been a surge in the publication of clinical trial\nreports, making it challenging to conduct systematic reviews. Automatically\nextracting Population, Intervention, Comparator, and Outcome (PICO) from\nclinical trial studies can alleviate the traditionally time-consuming process\nof manually scrutinizing systematic reviews. Existing approaches of PICO frame\nextraction involves supervised approach that relies on the existence of\nmanually annotated data points in the form of BIO label tagging. Recent\napproaches, such as In-Context Learning (ICL), which has been shown to be\neffective for a number of downstream NLP tasks, require the use of labeled\nexamples. In this work, we adopt ICL strategy by employing the pretrained\nknowledge of Large Language Models (LLMs), gathered during the pretraining\nphase of an LLM, to automatically extract the PICO-related terminologies from\nclinical trial documents in unsupervised set up to bypass the availability of\nlarge number of annotated data instances. Additionally, to showcase the highest\neffectiveness of LLM in oracle scenario where large number of annotated samples\nare available, we adopt the instruction tuning strategy by employing Low Rank\nAdaptation (LORA) to conduct the training of gigantic model in low resource\nenvironment for the PICO frame extraction task. Our empirical results show that\nour proposed ICL-based framework produces comparable results on all the version\nof EBM-NLP datasets and the proposed instruction tuned version of our framework\nproduces state-of-the-art results on all the different EBM-NLP datasets. Our\nproject is available at \\url{https://github.com/shrimonmuke0202/AlpaPICO.git}.\n", "date": "2024-09-15", "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": "2404.05415", "title": "Relation Extraction Using Large Language Models: A Case Study on\n  Acupuncture Point Locations", "abstract": "  In acupuncture therapy, the accurate location of acupoints is essential for\nits effectiveness. The advanced language understanding capabilities of large\nlanguage models (LLMs) like Generative Pre-trained Transformers (GPT) present a\nsignificant opportunity for extracting relations related to acupoint locations\nfrom textual knowledge sources. This study aims to compare the performance of\nGPT with traditional deep learning models (Long Short-Term Memory (LSTM) and\nBidirectional Encoder Representations from Transformers for Biomedical Text\nMining (BioBERT)) in extracting acupoint-related location relations and assess\nthe impact of pretraining and fine-tuning on GPT's performance. We utilized the\nWorld Health Organization Standard Acupuncture Point Locations in the Western\nPacific Region (WHO Standard) as our corpus, which consists of descriptions of\n361 acupoints. Five types of relations ('direction_of,' 'distance_of,'\n'part_of,' 'near_acupoint,' and 'located_near') (n= 3,174) between acupoints\nwere annotated. Five models were compared: BioBERT, LSTM, pre-trained GPT-3.5,\nfine-tuned GPT-3.5, as well as pre-trained GPT-4. Performance metrics included\nmicro-average exact match precision, recall, and F1 scores. Our results\ndemonstrate that fine-tuned GPT-3.5 consistently outperformed other models in\nF1 scores across all relation types. Overall, it achieved the highest\nmicro-average F1 score of 0.92. This study underscores the effectiveness of\nLLMs like GPT in extracting relations related to acupoint locations, with\nimplications for accurately modeling acupuncture knowledge and promoting\nstandard implementation in acupuncture training and practice. The findings also\ncontribute to advancing informatics applications in traditional and\ncomplementary medicine, showcasing the potential of LLMs in natural language\nprocessing.\n", "date": "2024-04-08", "categories": []}
{"id": "2409.05732", "title": "Towards Democratizing Multilingual Large Language Models For Medicine\n  Through A Two-Stage Instruction Fine-tuning Approach", "abstract": "  Open-source, multilingual medical large language models (LLMs) have the\npotential to serve linguistically diverse populations across different regions.\nAdapting generic LLMs for healthcare often requires continual pretraining, but\nthis approach is computationally expensive and sometimes impractical.\nInstruction fine-tuning on a specific task may not always guarantee optimal\nperformance due to the lack of broader domain knowledge that the model needs to\nunderstand and reason effectively in diverse scenarios. To address these\nchallenges, we introduce two multilingual instruction fine-tuning datasets,\nMMed-IFT and MMed-IFT-MC, containing over 200k high-quality medical samples in\nsix languages. We propose a two-stage training paradigm: the first stage\ninjects general medical knowledge using MMed-IFT, while the second stage\nfine-tunes task-specific multiple-choice questions with MMed-IFT-MC. Our method\nachieves competitive results on both English and multilingual benchmarks,\nstriking a balance between computational efficiency and performance. We plan to\nmake our dataset and model weights public at\n\\url{https://github.com/SpassMed/Med-Llama3} in the future.\n", "date": "2024-09-09", "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": "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": "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": "2409.02725", "title": "Pre-training data selection for biomedical domain adaptation using\n  journal impact metrics", "abstract": "  Domain adaptation is a widely used method in natural language processing\n(NLP) to improve the performance of a language model within a specific domain.\nThis method is particularly common in the biomedical domain, which sees regular\npublication of numerous scientific articles. PubMed, a significant corpus of\ntext, is frequently used in the biomedical domain. The primary objective of\nthis study is to explore whether refining a pre-training dataset using specific\nquality metrics for scientific papers can enhance the performance of the\nresulting model. To accomplish this, we employ two straightforward journal\nimpact metrics and conduct experiments by continually pre-training BERT on\nvarious subsets of the complete PubMed training set, we then evaluate the\nresulting models on biomedical language understanding tasks from the BLURB\nbenchmark. Our results show that pruning using journal impact metrics is not\nefficient. But we also show that pre-training using fewer abstracts (but with\nthe same number of training steps) does not necessarily decrease the resulting\nmodel's performance.\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": "2409.00133", "title": "A Survey for Large Language Models in Biomedicine", "abstract": "  Recent breakthroughs in large language models (LLMs) offer unprecedented\nnatural language understanding and generation capabilities. However, existing\nsurveys on LLMs in biomedicine often focus on specific applications or model\narchitectures, lacking a comprehensive analysis that integrates the latest\nadvancements across various biomedical domains. This review, based on an\nanalysis of 484 publications sourced from databases including PubMed, Web of\nScience, and arXiv, provides an in-depth examination of the current landscape,\napplications, challenges, and prospects of LLMs in biomedicine, distinguishing\nitself by focusing on the practical implications of these models in real-world\nbiomedical contexts. Firstly, we explore the capabilities of LLMs in zero-shot\nlearning across a broad spectrum of biomedical tasks, including diagnostic\nassistance, drug discovery, and personalized medicine, among others, with\ninsights drawn from 137 key studies. Then, we discuss adaptation strategies of\nLLMs, including fine-tuning methods for both uni-modal and multi-modal LLMs to\nenhance their performance in specialized biomedical contexts where zero-shot\nfails to achieve, such as medical question answering and efficient processing\nof biomedical literature. Finally, we discuss the challenges that LLMs face in\nthe biomedicine domain including data privacy concerns, limited model\ninterpretability, issues with dataset quality, and ethics due to the sensitive\nnature of biomedical data, the need for highly reliable model outputs, and the\nethical implications of deploying AI in healthcare. To address these\nchallenges, we also identify future research directions of LLM in biomedicine\nincluding federated learning methods to preserve data privacy and integrating\nexplainable AI methodologies to enhance the transparency of LLMs.\n", "date": "2024-08-29", "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": "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": "2408.13833", "title": "Biomedical Large Languages Models Seem not to be Superior to Generalist\n  Models on Unseen Medical Data", "abstract": "  Large language models (LLMs) have shown potential in biomedical applications,\nleading to efforts to fine-tune them on domain-specific data. However, the\neffectiveness of this approach remains unclear. This study evaluates the\nperformance of biomedically fine-tuned LLMs against their general-purpose\ncounterparts on a variety of clinical tasks. We evaluated their performance on\nclinical case challenges from the New England Journal of Medicine (NEJM) and\nthe Journal of the American Medical Association (JAMA) and on several clinical\ntasks (e.g., information extraction, document summarization, and clinical\ncoding). Using benchmarks specifically chosen to be likely outside the\nfine-tuning datasets of biomedical models, we found that biomedical LLMs mostly\nperform inferior to their general-purpose counterparts, especially on tasks not\nfocused on medical knowledge. While larger models showed similar performance on\ncase tasks (e.g., OpenBioLLM-70B: 66.4% vs. Llama-3-70B-Instruct: 65% on JAMA\ncases), smaller biomedical models showed more pronounced underperformance\n(e.g., OpenBioLLM-8B: 30% vs. Llama-3-8B-Instruct: 64.3% on NEJM cases).\nSimilar trends were observed across the CLUE (Clinical Language Understanding\nEvaluation) benchmark tasks, with general-purpose models often performing\nbetter on text generation, question answering, and coding tasks. Our results\nsuggest that fine-tuning LLMs to biomedical data may not provide the expected\nbenefits and may potentially lead to reduced performance, challenging\nprevailing assumptions about domain-specific adaptation of LLMs and\nhighlighting the need for more rigorous evaluation frameworks in healthcare AI.\nAlternative approaches, such as retrieval-augmented generation, may be more\neffective in enhancing the biomedical capabilities of LLMs without compromising\ntheir general knowledge.\n", "date": "2024-08-25", "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": "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": "2405.04163", "title": "MEDVOC: Vocabulary Adaptation for Fine-tuning Pre-trained Language\n  Models on Medical Text Summarization", "abstract": "  This work presents a dynamic vocabulary adaptation strategy, MEDVOC, for\nfine-tuning pre-trained language models (PLMs) like BertSumAbs, BART, and\nPEGASUS for improved medical text summarization. In contrast to existing domain\nadaptation approaches in summarization, MEDVOC treats vocabulary as an\noptimizable parameter and optimizes the PLM vocabulary based on fragment score\nconditioned only on the downstream task's reference summaries. Unlike previous\nworks on vocabulary adaptation (limited only to classification tasks),\noptimizing vocabulary based on summarization tasks requires an extremely costly\nintermediate fine-tuning step on large summarization datasets. To that end, our\nnovel fragment score-based hyperparameter search very significantly reduces\nthis fine-tuning time -- from 450 days to less than 2 days on average.\nFurthermore, while previous works on vocabulary adaptation are often primarily\ntied to single PLMs, MEDVOC is designed to be deployable across multiple PLMs\n(with varying model vocabulary sizes, pre-training objectives, and model sizes)\n-- bridging the limited vocabulary overlap between the biomedical literature\ndomain and PLMs. MEDVOC outperforms baselines by 15.74% in terms of Rouge-L in\nzero-shot setting and shows gains of 17.29% in high Out-Of-Vocabulary (OOV)\nconcentrations. Our human evaluation shows MEDVOC generates more faithful\nmedical summaries (88% compared to 59% in baselines). We make the codebase\npublicly available at https://github.com/gb-kgp/MEDVOC.\n", "date": "2024-05-07", "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": "2408.08422", "title": "Assessing and Enhancing Large Language Models in Rare Disease\n  Question-answering", "abstract": "  Despite the impressive capabilities of Large Language Models (LLMs) in\ngeneral medical domains, questions remain about their performance in diagnosing\nrare diseases. To answer this question, we aim to assess the diagnostic\nperformance of LLMs in rare diseases, and explore methods to enhance their\neffectiveness in this area. In this work, we introduce a rare disease\nquestion-answering (ReDis-QA) dataset to evaluate the performance of LLMs in\ndiagnosing rare diseases. Specifically, we collected 1360 high-quality\nquestion-answer pairs within the ReDis-QA dataset, covering 205 rare diseases.\nAdditionally, we annotated meta-data for each question, facilitating the\nextraction of subsets specific to any given disease and its property. Based on\nthe ReDis-QA dataset, we benchmarked several open-source LLMs, revealing that\ndiagnosing rare diseases remains a significant challenge for these models.\n  To facilitate retrieval augmentation generation for rare disease diagnosis,\nwe collect the first rare diseases corpus (ReCOP), sourced from the National\nOrganization for Rare Disorders (NORD) database. Specifically, we split the\nreport of each rare disease into multiple chunks, each representing a different\nproperty of the disease, including their overview, symptoms, causes, effects,\nrelated disorders, diagnosis, and standard therapies. This structure ensures\nthat the information within each chunk aligns consistently with a question.\nExperiment results demonstrate that ReCOP can effectively improve the accuracy\nof LLMs on the ReDis-QA dataset by an average of 8%. Moreover, it significantly\nguides LLMs to generate trustworthy answers and explanations that can be traced\nback to existing literature.\n", "date": "2024-08-15", "categories": []}
{"id": "2408.08566", "title": "Overview of the BioLaySumm 2024 Shared Task on the Lay Summarization of\n  Biomedical Research Articles", "abstract": "  This paper presents the setup and results of the second edition of the\nBioLaySumm shared task on the Lay Summarisation of Biomedical Research\nArticles, hosted at the BioNLP Workshop at ACL 2024. In this task edition, we\naim to build on the first edition's success by further increasing research\ninterest in this important task and encouraging participants to explore novel\napproaches that will help advance the state-of-the-art. Encouragingly, we found\nresearch interest in the task to be high, with this edition of the task\nattracting a total of 53 participating teams, a significant increase in\nengagement from the previous edition. Overall, our results show that a broad\nrange of innovative approaches were adopted by task participants, with a\npredictable shift towards the use of Large Language Models (LLMs).\n", "date": "2024-08-16", "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": "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": "2408.05545", "title": "Multi-layer Sequence Labeling-based Joint Biomedical Event Extraction", "abstract": "  In recent years, biomedical event extraction has been dominated by\ncomplicated pipeline and joint methods, which need to be simplified. In\naddition, existing work has not effectively utilized trigger word information\nexplicitly. Hence, we propose MLSL, a method based on multi-layer sequence\nlabeling for joint biomedical event extraction. MLSL does not introduce prior\nknowledge and complex structures. Moreover, it explicitly incorporates the\ninformation of candidate trigger words into the sequence labeling to learn the\ninteraction relationships between trigger words and argument roles. Based on\nthis, MLSL can learn well with just a simple workflow. Extensive\nexperimentation demonstrates the superiority of MLSL in terms of extraction\nperformance compared to other state-of-the-art methods.\n", "date": "2024-08-10", "categories": []}
{"id": "2408.06618", "title": "Generalized knowledge-enhanced framework for biomedical entity and\n  relation extraction", "abstract": "  In recent years, there has been an increasing number of frameworks developed\nfor biomedical entity and relation extraction. This research effort aims to\naddress the accelerating growth in biomedical publications and the intricate\nnature of biomedical texts, which are written for mainly domain experts. To\nhandle these challenges, we develop a novel framework that utilizes external\nknowledge to construct a task-independent and reusable background knowledge\ngraph for biomedical entity and relation extraction. The design of our model is\ninspired by how humans learn domain-specific topics. In particular, humans\noften first acquire the most basic and common knowledge regarding a field to\nbuild the foundational knowledge and then use that as a basis for extending to\nvarious specialized topics. Our framework employs such common-knowledge-sharing\nmechanism to build a general neural-network knowledge graph that is learning\ntransferable to different domain-specific biomedical texts effectively.\nExperimental evaluations demonstrate that our model, equipped with this\ngeneralized and cross-transferable knowledge base, achieves competitive\nperformance benchmarks, including BioRelEx for binding interaction detection\nand ADE for Adverse Drug Effect identification.\n", "date": "2024-08-13", "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": "2408.04775", "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": "2403.00952", "title": "MediSwift: Efficient Sparse Pre-trained Biomedical Language Models", "abstract": "  Large language models (LLMs) are typically trained on general source data for\nvarious domains, but a recent surge in domain-specific LLMs has shown their\npotential to outperform general-purpose models in domain-specific tasks (e.g.,\nbiomedicine). Although domain-specific pre-training enhances efficiency and\nleads to smaller models, the computational costs of training these LLMs remain\nhigh, posing budgeting challenges. We introduce MediSwift, a suite of\nbiomedical LMs that leverage sparse pre-training on domain-specific biomedical\ntext data. By inducing up to 75% weight sparsity during the pre-training phase,\nMediSwift achieves a 2-2.5x reduction in training FLOPs. Notably, all sparse\npre-training was performed on the Cerebras CS-2 system, which is specifically\ndesigned to realize the acceleration benefits from unstructured weight\nsparsity, thereby significantly enhancing the efficiency of the MediSwift\nmodels. Through subsequent dense fine-tuning and strategic soft prompting,\nMediSwift models outperform existing LLMs up to 7B parameters on biomedical\ntasks, setting new benchmarks w.r.t efficiency-accuracy on tasks such as\nPubMedQA. Our results show that sparse pre-training, along with dense\nfine-tuning and soft prompting, offers an effective method for creating\nhigh-performing, computationally efficient models in specialized domains.\n", "date": "2024-03-01", "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": "2408.03874", "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": "2404.03598", "title": "Intent Detection and Entity Extraction from BioMedical Literature", "abstract": "  Biomedical queries have become increasingly prevalent in web searches,\nreflecting the growing interest in accessing biomedical literature. Despite\nrecent research on large-language models (LLMs) motivated by endeavours to\nattain generalized intelligence, their efficacy in replacing task and\ndomain-specific natural language understanding approaches remains questionable.\nIn this paper, we address this question by conducting a comprehensive empirical\nevaluation of intent detection and named entity recognition (NER) tasks from\nbiomedical text. We show that Supervised Fine Tuned approaches are still\nrelevant and more effective than general-purpose LLMs. Biomedical transformer\nmodels such as PubMedBERT can surpass ChatGPT on NER task with only 5\nsupervised examples.\n", "date": "2024-04-04", "categories": []}
{"id": "2408.01869", "title": "MALADE: Orchestration of LLM-powered Agents with Retrieval Augmented\n  Generation for Pharmacovigilance", "abstract": "  In the era of Large Language Models (LLMs), given their remarkable text\nunderstanding and generation abilities, there is an unprecedented opportunity\nto develop new, LLM-based methods for trustworthy medical knowledge synthesis,\nextraction and summarization. This paper focuses on the problem of\nPharmacovigilance (PhV), where the significance and challenges lie in\nidentifying Adverse Drug Events (ADEs) from diverse text sources, such as\nmedical literature, clinical notes, and drug labels. Unfortunately, this task\nis hindered by factors including variations in the terminologies of drugs and\noutcomes, and ADE descriptions often being buried in large amounts of narrative\ntext. We present MALADE, the first effective collaborative multi-agent system\npowered by LLM with Retrieval Augmented Generation for ADE extraction from drug\nlabel data. This technique involves augmenting a query to an LLM with relevant\ninformation extracted from text resources, and instructing the LLM to compose a\nresponse consistent with the augmented data. MALADE is a general LLM-agnostic\narchitecture, and its unique capabilities are: (1) leveraging a variety of\nexternal sources, such as medical literature, drug labels, and FDA tools (e.g.,\nOpenFDA drug information API), (2) extracting drug-outcome association in a\nstructured format along with the strength of the association, and (3) providing\nexplanations for established associations. Instantiated with GPT-4 Turbo or\nGPT-4o, and FDA drug label data, MALADE demonstrates its efficacy with an Area\nUnder ROC Curve of 0.90 against the OMOP Ground Truth table of ADEs. Our\nimplementation leverages the Langroid multi-agent LLM framework and can be\nfound at https://github.com/jihyechoi77/malade.\n", "date": "2024-08-03", "categories": []}
{"id": "2408.00588", "title": "Closing the gap between open-source and commercial large language models\n  for medical evidence summarization", "abstract": "  Large language models (LLMs) hold great promise in summarizing medical\nevidence. Most recent studies focus on the application of proprietary LLMs.\nUsing proprietary LLMs introduces multiple risk factors, including a lack of\ntransparency and vendor dependency. While open-source LLMs allow better\ntransparency and customization, their performance falls short compared to\nproprietary ones. In this study, we investigated to what extent fine-tuning\nopen-source LLMs can further improve their performance in summarizing medical\nevidence. Utilizing a benchmark dataset, MedReview, consisting of 8,161 pairs\nof systematic reviews and summaries, we fine-tuned three broadly-used,\nopen-sourced LLMs, namely PRIMERA, LongT5, and Llama-2. Overall, the fine-tuned\nLLMs obtained an increase of 9.89 in ROUGE-L (95% confidence interval:\n8.94-10.81), 13.21 in METEOR score (95% confidence interval: 12.05-14.37), and\n15.82 in CHRF score (95% confidence interval: 13.89-16.44). The performance of\nfine-tuned LongT5 is close to GPT-3.5 with zero-shot settings. Furthermore,\nsmaller fine-tuned models sometimes even demonstrated superior performance\ncompared to larger zero-shot models. The above trends of improvement were also\nmanifested in both human and GPT4-simulated evaluations. Our results can be\napplied to guide model selection for tasks demanding particular domain\nknowledge, such as medical evidence summarization.\n", "date": "2024-07-25", "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": "2403.16303", "title": "Large Language Models in Biomedical and Health Informatics: A Review\n  with Bibliometric Analysis", "abstract": "  Large Language Models (LLMs) have rapidly become important tools in\nBiomedical and Health Informatics (BHI), enabling new ways to analyze data,\ntreat patients, and conduct research. This study aims to provide a\ncomprehensive overview of LLM applications in BHI, highlighting their\ntransformative potential and addressing the associated ethical and practical\nchallenges. We reviewed 1,698 research articles from January 2022 to December\n2023, categorizing them by research themes and diagnostic categories.\nAdditionally, we conducted network analysis to map scholarly collaborations and\nresearch dynamics. Our findings reveal a substantial increase in the potential\napplications of LLMs to a variety of BHI tasks, including clinical decision\nsupport, patient interaction, and medical document analysis. Notably, LLMs are\nexpected to be instrumental in enhancing the accuracy of diagnostic tools and\npatient care protocols. The network analysis highlights dense and dynamically\nevolving collaborations across institutions, underscoring the interdisciplinary\nnature of LLM research in BHI. A significant trend was the application of LLMs\nin managing specific disease categories such as mental health and neurological\ndisorders, demonstrating their potential to influence personalized medicine and\npublic health strategies. LLMs hold promising potential to further transform\nbiomedical research and healthcare delivery. While promising, the ethical\nimplications and challenges of model validation call for rigorous scrutiny to\noptimize their benefits in clinical settings. This survey serves as a resource\nfor stakeholders in healthcare, including researchers, clinicians, and\npolicymakers, to understand the current state and future potential of LLMs in\nBHI.\n", "date": "2024-03-24", "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": "2407.18471", "title": "Constructing the CORD-19 Vaccine Dataset", "abstract": "  We introduce new dataset 'CORD-19-Vaccination' to cater to scientists\nspecifically looking into COVID-19 vaccine-related research. This dataset is\nextracted from CORD-19 dataset [Wang et al., 2020] and augmented with new\ncolumns for language detail, author demography, keywords, and topic per paper.\nFacebook's fastText model is used to identify languages [Joulin et al., 2016].\nTo establish author demography (author affiliation, lab/institution location,\nand lab/institution country columns) we processed the JSON file for each paper\nand then further enhanced using Google's search API to determine country\nvalues. 'Yake' was used to extract keywords from the title, abstract, and body\nof each paper and the LDA (Latent Dirichlet Allocation) algorithm was used to\nadd topic information [Campos et al., 2020, 2018a,b]. To evaluate the dataset,\nwe demonstrate a question-answering task like the one used in the CORD-19\nKaggle challenge [Goldbloom et al., 2022]. For further evaluation, sequential\nsentence classification was performed on each paper's abstract using the model\nfrom Dernoncourt et al. [2016]. We partially hand annotated the training\ndataset and used a pre-trained BERT-PubMed layer. 'CORD- 19-Vaccination'\ncontains 30k research papers and can be immensely valuable for NLP research\nsuch as text mining, information extraction, and question answering, specific\nto the domain of COVID-19 vaccine research.\n", "date": "2024-07-26", "categories": []}
{"id": "2407.18716", "title": "ChatSchema: A pipeline of extracting structured information with Large\n  Multimodal Models based on schema", "abstract": "  Objective: This study introduces ChatSchema, an effective method for\nextracting and structuring information from unstructured data in medical paper\nreports using a combination of Large Multimodal Models (LMMs) and Optical\nCharacter Recognition (OCR) based on the schema. By integrating predefined\nschema, we intend to enable LMMs to directly extract and standardize\ninformation according to the schema specifications, facilitating further data\nentry. Method: Our approach involves a two-stage process, including\nclassification and extraction for categorizing report scenarios and structuring\ninformation. We established and annotated a dataset to verify the effectiveness\nof ChatSchema, and evaluated key extraction using precision, recall, F1-score,\nand accuracy metrics. Based on key extraction, we further assessed value\nextraction. We conducted ablation studies on two LMMs to illustrate the\nimprovement of structured information extraction with different input modals\nand methods. Result: We analyzed 100 medical reports from Peking University\nFirst Hospital and established a ground truth dataset with 2,945 key-value\npairs. We evaluated ChatSchema using GPT-4o and Gemini 1.5 Pro and found a\nhigher overall performance of GPT-4o. The results are as follows: For the\nresult of key extraction, key-precision was 98.6%, key-recall was 98.5%,\nkey-F1-score was 98.6%. For the result of value extraction based on correct key\nextraction, the overall accuracy was 97.2%, precision was 95.8%, recall was\n95.8%, and F1-score was 95.8%. An ablation study demonstrated that ChatSchema\nachieved significantly higher overall accuracy and overall F1-score of\nkey-value extraction, compared to the Baseline, with increases of 26.9% overall\naccuracy and 27.4% overall F1-score, respectively.\n", "date": "2024-07-26", "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": "2406.03855", "title": "Performance of large language models in numerical vs. semantic medical\n  knowledge: Benchmarking on evidence-based Q&As", "abstract": "  Clinical problem-solving requires processing of semantic medical knowledge\nsuch as illness scripts and numerical medical knowledge of diagnostic tests for\nevidence-based decision-making. As large language models (LLMs) show promising\nresults in many aspects of language-based clinical practice, their ability to\ngenerate non-language evidence-based answers to clinical questions is\ninherently limited by tokenization. Therefore, we evaluated LLMs' performance\non two question types: numeric (correlating findings) and semantic\n(differentiating entities) while examining differences within and between LLMs\nin medical aspects and comparing their performance to humans. To generate\nstraightforward multi-choice questions and answers (QAs) based on\nevidence-based medicine (EBM), we used a comprehensive medical knowledge graph\n(encompassed data from more than 50,00 peer-reviewed articles) and created the\n\"EBMQA\". EBMQA contains 105,000 QAs labeled with medical and non-medical topics\nand classified into numerical or semantic questions. We benchmarked this\ndataset using more than 24,500 QAs on two state-of-the-art LLMs: Chat-GPT4 and\nClaude3-Opus. We evaluated the LLMs accuracy on semantic and numerical question\ntypes and according to sub-labeled topics. For validation, six medical experts\nwere tested on 100 numerical EBMQA questions. We found that both LLMs excelled\nmore in semantic than numerical QAs, with Claude3 surpassing GPT4 in numerical\nQAs. However, both LLMs showed inter and intra gaps in different medical\naspects and remained inferior to humans. Thus, their medical advice should be\naddressed carefully.\n", "date": "2024-06-06", "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": "2407.15237", "title": "Two eyes, Two views, and finally, One summary! Towards Multi-modal\n  Multi-tasking Knowledge-Infused Medical Dialogue Summarization", "abstract": "  We often summarize a multi-party conversation in two stages: chunking with\nhomogeneous units and summarizing the chunks. Thus, we hypothesize that there\nexists a correlation between homogeneous speaker chunking and overall\nsummarization tasks. In this work, we investigate the effectiveness of a\nmulti-faceted approach that simultaneously produces summaries of medical\nconcerns, doctor impressions, and an overall view. We introduce a multi-modal,\nmulti-tasking, knowledge-infused medical dialogue summary generation\n(MMK-Summation) model, which is incorporated with adapter-based fine-tuning\nthrough a gated mechanism for multi-modal information integration. The model,\nMMK-Summation, takes dialogues as input, extracts pertinent external knowledge\nbased on the context, integrates the knowledge and visual cues from the\ndialogues into the textual content, and ultimately generates concise summaries\nencompassing medical concerns, doctor impressions, and a comprehensive\noverview. The introduced model surpasses multiple baselines and traditional\nsummarization models across all evaluation metrics (including human\nevaluation), which firmly demonstrates the efficacy of the knowledge-guided\nmulti-tasking, multimodal medical conversation summarization. The code is\navailable at https://github.com/NLP-RL/MMK-Summation.\n", "date": "2024-07-21", "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": "2407.10086", "title": "Rapid Biomedical Research Classification: The Pandemic PACT Advanced\n  Categorisation Engine", "abstract": "  This paper introduces the Pandemic PACT Advanced Categorisation Engine\n(PPACE) along with its associated dataset. PPACE is a fine-tuned model\ndeveloped to automatically classify research abstracts from funded biomedical\nprojects according to WHO-aligned research priorities. This task is crucial for\nmonitoring research trends and identifying gaps in global health preparedness\nand response. Our approach builds on human-annotated projects, which are\nallocated one or more categories from a predefined list. A large language model\nis then used to generate `rationales' explaining the reasoning behind these\nannotations. This augmented data, comprising expert annotations and rationales,\nis subsequently used to fine-tune a smaller, more efficient model. Developed as\npart of the Pandemic PACT project, which aims to track and analyse research\nfunding and clinical evidence for a wide range of diseases with outbreak\npotential, PPACE supports informed decision-making by research funders,\npolicymakers, and independent researchers. We introduce and release both the\ntrained model and the instruction-based dataset used for its training. Our\nevaluation shows that PPACE significantly outperforms its baselines. The\nrelease of PPACE and its associated dataset offers valuable resources for\nresearchers in multilabel biomedical document classification and supports\nadvancements in aligning biomedical research with key global health priorities.\n", "date": "2024-07-14", "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": "2407.12888", "title": "Explainable Biomedical Hypothesis Generation via Retrieval Augmented\n  Generation enabled Large Language Models", "abstract": "  The vast amount of biomedical information available today presents a\nsignificant challenge for investigators seeking to digest, process, and\nunderstand these findings effectively. Large Language Models (LLMs) have\nemerged as powerful tools to navigate this complex and challenging data\nlandscape. However, LLMs may lead to hallucinatory responses, making Retrieval\nAugmented Generation (RAG) crucial for achieving accurate information. In this\nprotocol, we present RUGGED (Retrieval Under Graph-Guided Explainable disease\nDistinction), a comprehensive workflow designed to support investigators with\nknowledge integration and hypothesis generation, identifying validated paths\nforward. Relevant biomedical information from publications and knowledge bases\nare reviewed, integrated, and extracted via text-mining association analysis\nand explainable graph prediction models on disease nodes, forecasting potential\nlinks among drugs and diseases. These analyses, along with biomedical texts,\nare integrated into a framework that facilitates user-directed mechanism\nelucidation as well as hypothesis exploration through RAG-enabled LLMs. A\nclinical use-case demonstrates RUGGED's ability to evaluate and recommend\ntherapeutics for Arrhythmogenic Cardiomyopathy (ACM) and Dilated Cardiomyopathy\n(DCM), analyzing prescribed drugs for molecular interactions and unexplored\nuses. The platform minimizes LLM hallucinations, offers actionable insights,\nand improves the investigation of novel therapeutics.\n", "date": "2024-07-17", "categories": []}
{"id": "2407.13511", "title": "Can Open-Source LLMs Compete with Commercial Models? Exploring the\n  Few-Shot Performance of Current GPT Models in Biomedical Tasks", "abstract": "  Commercial large language models (LLMs), like OpenAI's GPT-4 powering ChatGPT\nand Anthropic's Claude 3 Opus, have dominated natural language processing (NLP)\nbenchmarks across different domains. New competing Open-Source alternatives\nlike Mixtral 8x7B or Llama 3 have emerged and seem to be closing the gap while\noften offering higher throughput and being less costly to use. Open-Source LLMs\ncan also be self-hosted, which makes them interesting for enterprise and\nclinical use cases where sensitive data should not be processed by third\nparties. We participated in the 12th BioASQ challenge, which is a retrieval\naugmented generation (RAG) setting, and explored the performance of current GPT\nmodels Claude 3 Opus, GPT-3.5-turbo and Mixtral 8x7b with in-context learning\n(zero-shot, few-shot) and QLoRa fine-tuning. We also explored how additional\nrelevant knowledge from Wikipedia added to the context-window of the LLM might\nimprove their performance. Mixtral 8x7b was competitive in the 10-shot setting,\nboth with and without fine-tuning, but failed to produce usable results in the\nzero-shot setting. QLoRa fine-tuning and Wikipedia context did not lead to\nmeasurable performance gains. Our results indicate that the performance gap\nbetween commercial and open-source models in RAG setups exists mainly in the\nzero-shot setting and can be closed by simply collecting few-shot examples for\ndomain-specific use cases. The code needed to rerun these experiments is\navailable through GitHub.\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": "2402.10373", "title": "BioMistral: A Collection of Open-Source Pretrained Large Language Models\n  for Medical Domains", "abstract": "  Large Language Models (LLMs) have demonstrated remarkable versatility in\nrecent years, offering potential applications across specialized domains such\nas healthcare and medicine. Despite the availability of various open-source\nLLMs tailored for health contexts, adapting general-purpose LLMs to the medical\ndomain presents significant challenges. In this paper, we introduce BioMistral,\nan open-source LLM tailored for the biomedical domain, utilizing Mistral as its\nfoundation model and further pre-trained on PubMed Central. We conduct a\ncomprehensive evaluation of BioMistral on a benchmark comprising 10 established\nmedical question-answering (QA) tasks in English. We also explore lightweight\nmodels obtained through quantization and model merging approaches. Our results\ndemonstrate BioMistral's superior performance compared to existing open-source\nmedical models and its competitive edge against proprietary counterparts.\nFinally, to address the limited availability of data beyond English and to\nassess the multilingual generalization of medical LLMs, we automatically\ntranslated and evaluated this benchmark into 7 other languages. This marks the\nfirst large-scale multilingual evaluation of LLMs in the medical domain.\nDatasets, multilingual evaluation benchmarks, scripts, and all the models\nobtained during our experiments are freely released.\n", "date": "2024-02-15", "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": "2407.10990", "title": "MedBench: A Comprehensive, Standardized, and Reliable Benchmarking\n  System for Evaluating Chinese Medical Large Language Models", "abstract": "  Ensuring the general efficacy and goodness for human beings from medical\nlarge language models (LLM) before real-world deployment is crucial. However, a\nwidely accepted and accessible evaluation process for medical LLM, especially\nin the Chinese context, remains to be established. In this work, we introduce\n\"MedBench\", a comprehensive, standardized, and reliable benchmarking system for\nChinese medical LLM. First, MedBench assembles the currently largest evaluation\ndataset (300,901 questions) to cover 43 clinical specialties and performs\nmulti-facet evaluation on medical LLM. Second, MedBench provides a standardized\nand fully automatic cloud-based evaluation infrastructure, with physical\nseparations for question and ground truth. Third, MedBench implements dynamic\nevaluation mechanisms to prevent shortcut learning and answer remembering.\nApplying MedBench to popular general and medical LLMs, we observe unbiased,\nreproducible evaluation results largely aligning with medical professionals'\nperspectives. This study establishes a significant foundation for preparing the\npractical applications of Chinese medical LLMs. MedBench is publicly accessible\nat https://medbench.opencompass.org.cn.\n", "date": "2024-06-24", "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": "2407.11536", "title": "Fine-Tuning Medical Language Models for Enhanced Long-Contextual\n  Understanding and Domain Expertise", "abstract": "  Large Language Models (LLMs) have been widely applied in various professional\nfields. By fine-tuning the models using domain specific question and answer\ndatasets, the professional domain knowledge and Q\\&A abilities of these models\nhave significantly improved, for example, medical professional LLMs that use\nfine-tuning of doctor-patient Q\\&A data exhibit extraordinary disease\ndiagnostic abilities. However, we observed that despite improvements in\nspecific domain knowledge, the performance of medical LLM in long-context\nunderstanding has significantly declined, especially compared to general\nlanguage models with similar parameters. The purpose of this study is to\ninvestigate the phenomenon of reduced performance in understanding long-context\nin medical LLM. We designed a series of experiments to conduct open-book\nprofessional knowledge exams on all models to evaluate their ability to read\nlong-context. By adjusting the proportion and quantity of general data and\nmedical data in the process of fine-tuning, we can determine the best data\ncomposition to optimize the professional model and achieve a balance between\nlong-context performance and specific domain knowledge.\n", "date": "2024-07-16", "categories": []}
{"id": "2407.08940", "title": "Large Language Models as Biomedical Hypothesis Generators: A\n  Comprehensive Evaluation", "abstract": "  The rapid growth of biomedical knowledge has outpaced our ability to\nefficiently extract insights and generate novel hypotheses. Large language\nmodels (LLMs) have emerged as a promising tool to revolutionize knowledge\ninteraction and potentially accelerate biomedical discovery. In this paper, we\npresent a comprehensive evaluation of LLMs as biomedical hypothesis generators.\nWe construct a dataset of background-hypothesis pairs from biomedical\nliterature, carefully partitioned into training, seen, and unseen test sets\nbased on publication date to mitigate data contamination. Using this dataset,\nwe assess the hypothesis generation capabilities of top-tier instructed models\nin zero-shot, few-shot, and fine-tuning settings. To enhance the exploration of\nuncertainty, a crucial aspect of scientific discovery, we incorporate tool use\nand multi-agent interactions in our evaluation framework. Furthermore, we\npropose four novel metrics grounded in extensive literature review to evaluate\nthe quality of generated hypotheses, considering both LLM-based and human\nassessments. Our experiments yield two key findings: 1) LLMs can generate novel\nand validated hypotheses, even when tested on literature unseen during\ntraining, and 2) Increasing uncertainty through multi-agent interactions and\ntool use can facilitate diverse candidate generation and improve zero-shot\nhypothesis generation performance. However, we also observe that the\nintegration of additional knowledge through few-shot learning and tool use may\nnot always lead to performance gains, highlighting the need for careful\nconsideration of the type and scope of external knowledge incorporated. These\nfindings underscore the potential of LLMs as powerful aids in biomedical\nhypothesis generation and provide valuable insights to guide further research\nin this area.\n", "date": "2024-07-12", "categories": []}
{"id": "2407.10020", "title": "Causality extraction from medical text using Large Language Models\n  (LLMs)", "abstract": "  This study explores the potential of natural language models, including large\nlanguage models, to extract causal relations from medical texts, specifically\nfrom Clinical Practice Guidelines (CPGs). The outcomes causality extraction\nfrom Clinical Practice Guidelines for gestational diabetes are presented,\nmarking a first in the field. We report on a set of experiments using variants\nof BERT (BioBERT, DistilBERT, and BERT) and using Large Language Models (LLMs),\nnamely GPT-4 and LLAMA2. Our experiments show that BioBERT performed better\nthan other models, including the Large Language Models, with an average\nF1-score of 0.72. GPT-4 and LLAMA2 results show similar performance but less\nconsistency. We also release the code and an annotated a corpus of causal\nstatements within the Clinical Practice Guidelines for gestational diabetes.\n", "date": "2024-07-13", "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": "2407.09429", "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": "2407.08662", "title": "Uncertainty Estimation of Large Language Models in Medical Question\n  Answering", "abstract": "  Large Language Models (LLMs) show promise for natural language generation in\nhealthcare, but risk hallucinating factually incorrect information. Deploying\nLLMs for medical question answering necessitates reliable uncertainty\nestimation (UE) methods to detect hallucinations. In this work, we benchmark\npopular UE methods with different model sizes on medical question-answering\ndatasets. Our results show that current approaches generally perform poorly in\nthis domain, highlighting the challenge of UE for medical applications. We also\nobserve that larger models tend to yield better results, suggesting a\ncorrelation between model size and the reliability of UE. To address these\nchallenges, we propose Two-phase Verification, a probability-free Uncertainty\nEstimation approach. First, an LLM generates a step-by-step explanation\nalongside its initial answer, followed by formulating verification questions to\ncheck the factual claims in the explanation. The model then answers these\nquestions twice: first independently, and then referencing the explanation.\nInconsistencies between the two sets of answers measure the uncertainty in the\noriginal response. We evaluate our approach on three biomedical\nquestion-answering datasets using Llama 2 Chat models and compare it against\nthe benchmarked baseline methods. The results show that our Two-phase\nVerification method achieves the best overall accuracy and stability across\nvarious datasets and model sizes, and its performance scales as the model size\nincreases.\n", "date": "2024-07-11", "categories": []}
{"id": "2407.07373", "title": "Automatic Extraction of Disease Risk Factors from Medical Publications", "abstract": "  We present a novel approach to automating the identification of risk factors\nfor diseases from medical literature, leveraging pre-trained models in the\nbio-medical domain, while tuning them for the specific task. Faced with the\nchallenges of the diverse and unstructured nature of medical articles, our\nstudy introduces a multi-step system to first identify relevant articles, then\nclassify them based on the presence of risk factor discussions and, finally,\nextract specific risk factor information for a disease through a\nquestion-answering model.\n  Our contributions include the development of a comprehensive pipeline for the\nautomated extraction of risk factors and the compilation of several datasets,\nwhich can serve as valuable resources for further research in this area. These\ndatasets encompass a wide range of diseases, as well as their associated risk\nfactors, meticulously identified and validated through a fine-grained\nevaluation scheme. We conducted both automatic and thorough manual evaluation,\ndemonstrating encouraging results. We also highlight the importance of\nimproving models and expanding dataset comprehensiveness to keep pace with the\nrapidly evolving field of medical research.\n", "date": "2024-07-10", "categories": []}
{"id": "2210.06852", "title": "Overview of BioASQ 2022: The tenth BioASQ challenge on Large-Scale\n  Biomedical Semantic Indexing and Question Answering", "abstract": "  This paper presents an overview of the tenth edition of the BioASQ challenge\nin the context of the Conference and Labs of the Evaluation Forum (CLEF) 2022.\nBioASQ is an ongoing series of challenges that promotes advances in the domain\nof large-scale biomedical semantic indexing and question answering. In this\nedition, the challenge was composed of the three established tasks a, b, and\nSynergy, and a new task named DisTEMIST for automatic semantic annotation and\ngrounding of diseases from clinical content in Spanish, a key concept for\nsemantic indexing and search engines of literature and clinical records. This\nyear, BioASQ received more than 170 distinct systems from 38 teams in total for\nthe four different tasks of the challenge. As in previous years, the majority\nof the competing systems outperformed the strong baselines, indicating the\ncontinuous advancement of the state-of-the-art in this domain.\n", "date": "2022-10-13", "categories": []}
{"id": "2307.05131", "title": "Overview of BioASQ 2023: The eleventh BioASQ challenge on Large-Scale\n  Biomedical Semantic Indexing and Question Answering", "abstract": "  This is an overview of the eleventh edition of the BioASQ challenge in the\ncontext of the Conference and Labs of the Evaluation Forum (CLEF) 2023. BioASQ\nis a series of international challenges promoting advances in large-scale\nbiomedical semantic indexing and question answering. This year, BioASQ\nconsisted of new editions of the two established tasks b and Synergy, and a new\ntask (MedProcNER) on semantic annotation of clinical content in Spanish with\nmedical procedures, which have a critical role in medical practice. In this\nedition of BioASQ, 28 competing teams submitted the results of more than 150\ndistinct systems in total for the three different shared tasks of the\nchallenge. Similarly to previous editions, most of the participating systems\nachieved competitive performance, suggesting the continuous advancement of the\nstate-of-the-art in the field.\n", "date": "2023-07-11", "categories": []}
{"id": "2407.06779", "title": "Using Pretrained Large Language Model with Prompt Engineering to Answer\n  Biomedical Questions", "abstract": "  Our team participated in the BioASQ 2024 Task12b and Synergy tasks to build a\nsystem that can answer biomedical questions by retrieving relevant articles and\nsnippets from the PubMed database and generating exact and ideal answers. We\npropose a two-level information retrieval and question-answering system based\non pre-trained large language models (LLM), focused on LLM prompt engineering\nand response post-processing. We construct prompts with in-context few-shot\nexamples and utilize post-processing techniques like resampling and malformed\nresponse detection. We compare the performance of various pre-trained LLM\nmodels on this challenge, including Mixtral, OpenAI GPT and Llama2. Our\nbest-performing system achieved 0.14 MAP score on document retrieval, 0.05 MAP\nscore on snippet retrieval, 0.96 F1 score for yes/no questions, 0.38 MRR score\nfor factoid questions and 0.50 F1 score for list questions in Task 12b.\n", "date": "2024-07-09", "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": "2407.05015", "title": "How do you know that? Teaching Generative Language Models to Reference\n  Answers to Biomedical Questions", "abstract": "  Large language models (LLMs) have recently become the leading source of\nanswers for users' questions online. Despite their ability to offer eloquent\nanswers, their accuracy and reliability can pose a significant challenge. This\nis especially true for sensitive domains such as biomedicine, where there is a\nhigher need for factually correct answers. This paper introduces a biomedical\nretrieval-augmented generation (RAG) system designed to enhance the reliability\nof generated responses. The system is based on a fine-tuned LLM for the\nreferenced question-answering, where retrieved relevant abstracts from PubMed\nare passed to LLM's context as input through a prompt. Its output is an answer\nbased on PubMed abstracts, where each statement is referenced accordingly,\nallowing the users to verify the answer. Our retrieval system achieves an\nabsolute improvement of 23% compared to the PubMed search engine. Based on the\nmanual evaluation on a small sample, our fine-tuned LLM component achieves\ncomparable results to GPT-4 Turbo in referencing relevant abstracts. We make\nthe dataset used to fine-tune the models and the fine-tuned models based on\nMistral-7B-instruct-v0.1 and v0.2 publicly available.\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": "2407.06011", "title": "Igea: a Decoder-Only Language Model for Biomedical Text Generation in\n  Italian", "abstract": "  The development of domain-specific language models has significantly advanced\nnatural language processing applications in various specialized fields,\nparticularly in biomedicine. However, the focus has largely been on\nEnglish-language models, leaving a gap for less-resourced languages such as\nItalian. This paper introduces Igea, the first decoder-only language model\ndesigned explicitly for biomedical text generation in Italian. Built on the\nMinerva model and continually pretrained on a diverse corpus of Italian medical\ntexts, Igea is available in three model sizes: 350 million, 1 billion, and 3\nbillion parameters. The models aim to balance computational efficiency and\nperformance, addressing the challenges of managing the peculiarities of medical\nterminology in Italian. We evaluate Igea using a mix of in-domain biomedical\ncorpora and general-purpose benchmarks, highlighting its efficacy and retention\nof general knowledge even after the domain-specific training. This paper\ndiscusses the model's development and evaluation, providing a foundation for\nfuture advancements in Italian biomedical NLP.\n", "date": "2024-07-08", "categories": []}
{"id": "2305.13338", "title": "Gene Set Summarization using Large Language Models", "abstract": "  Molecular biologists frequently interpret gene lists derived from\nhigh-throughput experiments and computational analysis. This is typically done\nas a statistical enrichment analysis that measures the over- or\nunder-representation of biological function terms associated with genes or\ntheir properties, based on curated assertions from a knowledge base (KB) such\nas the Gene Ontology (GO). Interpreting gene lists can also be framed as a\ntextual summarization task, enabling the use of Large Language Models (LLMs),\npotentially utilizing scientific texts directly and avoiding reliance on a KB.\n  We developed SPINDOCTOR (Structured Prompt Interpolation of Natural Language\nDescriptions of Controlled Terms for Ontology Reporting), a method that uses\nGPT models to perform gene set function summarization as a complement to\nstandard enrichment analysis. This method can use different sources of gene\nfunctional information: (1) structured text derived from curated ontological KB\nannotations, (2) ontology-free narrative gene summaries, or (3) direct model\nretrieval.\n  We demonstrate that these methods are able to generate plausible and\nbiologically valid summary GO term lists for gene sets. However, GPT-based\napproaches are unable to deliver reliable scores or p-values and often return\nterms that are not statistically significant. Crucially, these methods were\nrarely able to recapitulate the most precise and informative term from standard\nenrichment, likely due to an inability to generalize and reason using an\nontology. Results are highly nondeterministic, with minor variations in prompt\nresulting in radically different term lists. Our results show that at this\npoint, LLM-based methods are unsuitable as a replacement for standard term\nenrichment analysis and that manual curation of ontological assertions remains\nnecessary.\n", "date": "2023-05-21", "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": "2407.02719", "title": "Boosting Biomedical Concept Extraction by Rule-Based Data Augmentation", "abstract": "  Document-level biomedical concept extraction is the task of identifying\nbiomedical concepts mentioned in a given document. Recent advancements have\nadapted pre-trained language models for this task. However, the scarcity of\ndomain-specific data and the deviation of concepts from their canonical names\noften hinder these models' effectiveness. To tackle this issue, we employ\nMetaMapLite, an existing rule-based concept mapping system, to generate\nadditional pseudo-annotated data from PubMed and PMC. The annotated data are\nused to augment the limited training data. Through extensive experiments, this\nstudy demonstrates the utility of a manually crafted concept mapping tool for\ntraining a better concept extraction model.\n", "date": "2024-07-03", "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": "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": "2404.00376", "title": "Small Language Models Learn Enhanced Reasoning Skills from Medical\n  Textbooks", "abstract": "  While recent advancements in commercial large language models (LM) have shown\npromising results in medical tasks, their closed-source nature poses\nsignificant privacy and security concerns, hindering their widespread use in\nthe medical field. Despite efforts to create open-source models, their limited\nparameters often result in insufficient multi-step reasoning capabilities\nrequired for solving complex medical problems. To address this, we introduce\nMeerkat, a new family of medical AI systems ranging from 7 to 70 billion\nparameters. The models were trained using our new synthetic dataset consisting\nof high-quality chain-of-thought reasoning paths sourced from 18 medical\ntextbooks, along with diverse instruction-following datasets. Our systems\nachieved remarkable accuracy across six medical benchmarks, surpassing the\nprevious best models such as MediTron and BioMistral, and GPT-3.5 by a large\nmargin. Notably, Meerkat-7B surpassed the passing threshold of the United\nStates Medical Licensing Examination (USMLE) for the first time for a\n7B-parameter model, while Meerkat-70B outperformed GPT-4 by an average of 1.3%.\nAdditionally, Meerkat-70B correctly diagnosed 21 out of 38 complex clinical\ncases, outperforming humans' 13.8 and closely matching GPT-4's 21.8. Our\nsystems offered more detailed free-form responses to clinical queries compared\nto existing small models, approaching the performance level of large commercial\nmodels. This significantly narrows the performance gap with large LMs,\nshowcasing its effectiveness in addressing complex medical challenges.\n", "date": "2024-03-30", "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": "2402.17887", "title": "JMLR: Joint Medical LLM and Retrieval Training for Enhancing Reasoning\n  and Professional Question Answering Capability", "abstract": "  Large Language Models (LLMs) have demonstrated a remarkable potential in\nmedical knowledge acquisition and question-answering. However, LLMs can\npotentially hallucinate and yield factually incorrect outcomes, even with\ndomain-specific pretraining. Previously, retrieval augmented generation (RAG)\nhas limited success in addressing hallucinations. Unlike previous methods in\nRAG where the retrieval model was trained separately from the LLM, we introduce\nJMLR (for Jointly trains LLM and information Retrieval) during the fine-tuning\nphase. The synchronized training mechanism enhances JMLR's ability to retrieve\nclinical guidelines and leverage medical knowledge to reason and answer\nquestions and reduces the demand for computational resources. We evaluated JMLR\non the important medical question-answering application. Our experimental\nresults demonstrate that JMLR-13B (70.5%) outperforms a previous\nstate-of-the-art open-source model using conventional pre-training and\nfine-tuning Meditron-70B (68.9%) and Llama2-13B with RAG (67.7%) on a medical\nquestion-answering dataset. Comprehensive evaluations reveal JMLR-13B enhances\nreasoning quality and reduces hallucinations better than Claude3-Opus.\nAdditionally, JMLR-13B (148 GPU hours) also trains much faster than\nMeditron-70B (42630 GPU hours). Through this work, we provide a new and\nefficient knowledge enhancement method for healthcare, demonstrating the\npotential of integrating retrieval and LLM training for medical\nquestion-answering systems.\n", "date": "2024-02-27", "categories": []}
{"id": "2406.20038", "title": "BioMNER: A Dataset for Biomedical Method Entity Recognition", "abstract": "  Named entity recognition (NER) stands as a fundamental and pivotal task\nwithin the realm of Natural Language Processing. Particularly within the domain\nof Biomedical Method NER, this task presents notable challenges, stemming from\nthe continual influx of domain-specific terminologies in scholarly literature.\nCurrent research in Biomedical Method (BioMethod) NER suffers from a scarcity\nof resources, primarily attributed to the intricate nature of methodological\nconcepts, which necessitate a profound understanding for precise delineation.\nIn this study, we propose a novel dataset for biomedical method entity\nrecognition, employing an automated BioMethod entity recognition and\ninformation retrieval system to assist human annotation. Furthermore, we\ncomprehensively explore a range of conventional and contemporary open-domain\nNER methodologies, including the utilization of cutting-edge large-scale\nlanguage models (LLMs) customised to our dataset. Our empirical findings reveal\nthat the large parameter counts of language models surprisingly inhibit the\neffective assimilation of entity extraction patterns pertaining to biomedical\nmethods. Remarkably, the approach, leveraging the modestly sized ALBERT model\n(only 11MB), in conjunction with conditional random fields (CRF), achieves\nstate-of-the-art (SOTA) performance.\n", "date": "2024-06-28", "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": "2406.18626", "title": "An LLM-based Knowledge Synthesis and Scientific Reasoning Framework for\n  Biomedical Discovery", "abstract": "  We present BioLunar, developed using the Lunar framework, as a tool for\nsupporting biological analyses, with a particular emphasis on molecular-level\nevidence enrichment for biomarker discovery in oncology. The platform\nintegrates Large Language Models (LLMs) to facilitate complex scientific\nreasoning across distributed evidence spaces, enhancing the capability for\nharmonizing and reasoning over heterogeneous data sources. Demonstrating its\nutility in cancer research, BioLunar leverages modular design, reusable data\naccess and data analysis components, and a low-code user interface, enabling\nresearchers of all programming levels to construct LLM-enabled scientific\nworkflows. By facilitating automatic scientific discovery and inference from\nheterogeneous evidence, BioLunar exemplifies the potential of the integration\nbetween LLMs, specialised databases and biomedical tools to support\nexpert-level knowledge synthesis and discovery.\n", "date": "2024-06-26", "categories": []}
{"id": "2406.18049", "title": "Improving Entity Recognition Using Ensembles of Deep Learning and\n  Fine-tuned Large Language Models: A Case Study on Adverse Event Extraction\n  from Multiple Sources", "abstract": "  Adverse event (AE) extraction following COVID-19 vaccines from text data is\ncrucial for monitoring and analyzing the safety profiles of immunizations.\nTraditional deep learning models are adept at learning intricate feature\nrepresentations and dependencies in sequential data, but often require\nextensive labeled data. In contrast, large language models (LLMs) excel in\nunderstanding contextual information, but exhibit unstable performance on named\nentity recognition tasks, possibly due to their broad but unspecific training.\nThis study aims to evaluate the effectiveness of LLMs and traditional deep\nlearning models in AE extraction, and to assess the impact of ensembling these\nmodels on performance. In this study, we utilized reports and posts from the\nVAERS (n=621), Twitter (n=9,133), and Reddit (n=131) as our corpora. Our goal\nwas to extract three types of entities: \"vaccine\", \"shot\", and \"ae\". We\nexplored and fine-tuned (except GPT-4) multiple LLMs, including GPT-2, GPT-3.5,\nGPT-4, and Llama-2, as well as traditional deep learning models like RNN and\nBioBERT. To enhance performance, we created ensembles of the three models with\nthe best performance. For evaluation, we used strict and relaxed F1 scores to\nevaluate the performance for each entity type, and micro-average F1 was used to\nassess the overall performance. The ensemble model achieved the highest\nperformance in \"vaccine\", \"shot\", and \"ae\" with strict F1-scores of 0.878,\n0.930, and 0.925, respectively, along with a micro-average score of 0.903. In\nconclusion, this study demonstrates the effectiveness and robustness of\nensembling fine-tuned traditional deep learning models and LLMs, for extracting\nAE-related information. This study contributes to the advancement of biomedical\nnatural language processing, providing valuable insights into improving AE\nextraction from text data for pharmacovigilance and public health surveillance.\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": "2402.15422", "title": "A Data-Centric Approach To Generate Faithful and High Quality Patient\n  Summaries with Large Language Models", "abstract": "  Patients often face difficulties in understanding their hospitalizations,\nwhile healthcare workers have limited resources to provide explanations. In\nthis work, we investigate the potential of large language models to generate\npatient summaries based on doctors' notes and study the effect of training data\non the faithfulness and quality of the generated summaries. To this end, we\nrelease (i) a rigorous labeling protocol for errors in medical texts and (ii) a\npublicly available dataset of annotated hallucinations in 100 doctor-written\nand 100 generated summaries. We show that fine-tuning on hallucination-free\ndata effectively reduces hallucinations from 2.60 to 1.55 per summary for Llama\n2, while preserving relevant information. We observe a similar effect on GPT-4\n(0.70 to 0.40), when the few-shot examples are hallucination-free. We also\nconduct a qualitative evaluation using hallucination-free and improved training\ndata. We find that common quantitative metrics do not correlate well with\nfaithfulness and quality. Finally, we test GPT-4 for automatic hallucination\ndetection, which clearly outperforms common baselines.\n", "date": "2024-02-23", "categories": []}
{"id": "2405.05506", "title": "Cross-Care: Assessing the Healthcare Implications of Pre-training Data\n  on Language Model Bias", "abstract": "  Large language models (LLMs) are increasingly essential in processing natural\nlanguages, yet their application is frequently compromised by biases and\ninaccuracies originating in their training data. In this study, we introduce\nCross-Care, the first benchmark framework dedicated to assessing biases and\nreal world knowledge in LLMs, specifically focusing on the representation of\ndisease prevalence across diverse demographic groups. We systematically\nevaluate how demographic biases embedded in pre-training corpora like $ThePile$\ninfluence the outputs of LLMs. We expose and quantify discrepancies by\njuxtaposing these biases against actual disease prevalences in various U.S.\ndemographic groups. Our results highlight substantial misalignment between LLM\nrepresentation of disease prevalence and real disease prevalence rates across\ndemographic subgroups, indicating a pronounced risk of bias propagation and a\nlack of real-world grounding for medical applications of LLMs. Furthermore, we\nobserve that various alignment methods minimally resolve inconsistencies in the\nmodels' representation of disease prevalence across different languages. For\nfurther exploration and analysis, we make all data and a data visualization\ntool available at: www.crosscare.net.\n", "date": "2024-05-09", "categories": []}
{"id": "2405.13179", "title": "RAG-RLRC-LaySum at BioLaySumm: Integrating Retrieval-Augmented\n  Generation and Readability Control for Layman Summarization of Biomedical\n  Texts", "abstract": "  This paper introduces the RAG-RLRC-LaySum framework, designed to make complex\nbiomedical research understandable to laymen through advanced Natural Language\nProcessing (NLP) techniques. Our Retrieval Augmented Generation (RAG) solution,\nenhanced by a reranking method, utilizes multiple knowledge sources to ensure\nthe precision and pertinence of lay summaries. Additionally, our Reinforcement\nLearning for Readability Control (RLRC) strategy improves readability, making\nscientific content comprehensible to non-specialists. Evaluations using the\npublicly accessible PLOS and eLife datasets show that our methods surpass Plain\nGemini model, demonstrating a 20% increase in readability scores, a 15%\nimprovement in ROUGE-2 relevance scores, and a 10% enhancement in factual\naccuracy. The RAG-RLRC-LaySum framework effectively democratizes scientific\nknowledge, enhancing public engagement with biomedical discoveries.\n", "date": "2024-05-21", "categories": []}
{"id": "2406.14757", "title": "A Large Language Model Outperforms Other Computational Approaches to the\n  High-Throughput Phenotyping of Physician Notes", "abstract": "  High-throughput phenotyping, the automated mapping of patient signs and\nsymptoms to standardized ontology concepts, is essential to gaining value from\nelectronic health records (EHR) in the support of precision medicine. Despite\ntechnological advances, high-throughput phenotyping remains a challenge. This\nstudy compares three computational approaches to high-throughput phenotyping: a\nLarge Language Model (LLM) incorporating generative AI, a Natural Language\nProcessing (NLP) approach utilizing deep learning for span categorization, and\na hybrid approach combining word vectors with machine learning. The approach\nthat implemented GPT-4 (a Large Language Model) demonstrated superior\nperformance, suggesting that Large Language Models are poised to be the\npreferred method for high-throughput phenotyping of physician notes.\n", "date": "2024-06-20", "categories": []}
{"id": "2406.14882", "title": "70B-parameter large language models in Japanese medical\n  question-answering", "abstract": "  Since the rise of large language models (LLMs), the domain adaptation has\nbeen one of the hot topics in various domains. Many medical LLMs trained with\nEnglish medical dataset have made public recently. However, Japanese LLMs in\nmedical domain still lack its research. Here we utilize multiple 70B-parameter\nLLMs for the first time and show that instruction tuning using Japanese medical\nquestion-answering dataset significantly improves the ability of Japanese LLMs\nto solve Japanese medical license exams, surpassing 50\\% in accuracy. In\nparticular, the Japanese-centric models exhibit a more significant leap in\nimprovement through instruction tuning compared to their English-centric\ncounterparts. This underscores the importance of continual pretraining and the\nadjustment of the tokenizer in our local language. We also examine two slightly\ndifferent prompt formats, resulting in non-negligible performance improvement.\n", "date": "2024-06-21", "categories": []}
{"id": "2406.15019", "title": "MedOdyssey: A Medical Domain Benchmark for Long Context Evaluation Up to\n  200K Tokens", "abstract": "  Numerous advanced Large Language Models (LLMs) now support context lengths up\nto 128K, and some extend to 200K. Some benchmarks in the generic domain have\nalso followed up on evaluating long-context capabilities. In the medical\ndomain, tasks are distinctive due to the unique contexts and need for domain\nexpertise, necessitating further evaluation. However, despite the frequent\npresence of long texts in medical scenarios, evaluation benchmarks of\nlong-context capabilities for LLMs in this field are still rare. In this paper,\nwe propose MedOdyssey, the first medical long-context benchmark with seven\nlength levels ranging from 4K to 200K tokens. MedOdyssey consists of two\nprimary components: the medical-context \"needles in a haystack\" task and a\nseries of tasks specific to medical applications, together comprising 10\ndatasets. The first component includes challenges such as counter-intuitive\nreasoning and novel (unknown) facts injection to mitigate knowledge leakage and\ndata contamination of LLMs. The second component confronts the challenge of\nrequiring professional medical expertise. Especially, we design the ``Maximum\nIdentical Context'' principle to improve fairness by guaranteeing that\ndifferent LLMs observe as many identical contexts as possible. Our experiment\nevaluates advanced proprietary and open-source LLMs tailored for processing\nlong contexts and presents detailed performance analyses. This highlights that\nLLMs still face challenges and need for further research in this area. Our code\nand data are released in the repository:\n\\url{https://github.com/JOHNNY-fans/MedOdyssey.}\n", "date": "2024-06-21", "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": "2406.12066", "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": "2406.14500", "title": "Improving Expert Radiology Report Summarization by Prompting Large\n  Language Models with a Layperson Summary", "abstract": "  Radiology report summarization (RRS) is crucial for patient care, requiring\nconcise \"Impressions\" from detailed \"Findings.\" This paper introduces a novel\nprompting strategy to enhance RRS by first generating a layperson summary. This\napproach normalizes key observations and simplifies complex information using\nnon-expert communication techniques inspired by doctor-patient interactions.\nCombined with few-shot in-context learning, this method improves the model's\nability to link general terms to specific findings. We evaluate this approach\non the MIMIC-CXR, CheXpert, and MIMIC-III datasets, benchmarking it against\n7B/8B parameter state-of-the-art open-source large language models (LLMs) like\nMeta-Llama-3-8B-Instruct. Our results demonstrate improvements in summarization\naccuracy and accessibility, particularly in out-of-domain tests, with\nimprovements as high as 5% for some metrics.\n", "date": "2024-06-20", "categories": []}
{"id": "2401.15269", "title": "Improving Medical Reasoning through Retrieval and Self-Reflection with\n  Retrieval-Augmented Large Language Models", "abstract": "  Recent proprietary large language models (LLMs), such as GPT-4, have achieved\na milestone in tackling diverse challenges in the biomedical domain, ranging\nfrom multiple-choice questions to long-form generations. To address challenges\nthat still cannot be handled with the encoded knowledge of LLMs, various\nretrieval-augmented generation (RAG) methods have been developed by searching\ndocuments from the knowledge corpus and appending them unconditionally or\nselectively to the input of LLMs for generation. However, when applying\nexisting methods to different domain-specific problems, poor generalization\nbecomes apparent, leading to fetching incorrect documents or making inaccurate\njudgments. In this paper, we introduce Self-BioRAG, a framework reliable for\nbiomedical text that specializes in generating explanations, retrieving\ndomain-specific documents, and self-reflecting generated responses. We utilize\n84k filtered biomedical instruction sets to train Self-BioRAG that can assess\nits generated explanations with customized reflective tokens. Our work proves\nthat domain-specific components, such as a retriever, domain-related document\ncorpus, and instruction sets are necessary for adhering to domain-related\ninstructions. Using three major medical question-answering benchmark datasets,\nexperimental results of Self-BioRAG demonstrate significant performance gains\nby achieving a 7.2% absolute improvement on average over the state-of-the-art\nopen-foundation model with a parameter size of 7B or less. Overall, we analyze\nthat Self-BioRAG finds the clues in the question, retrieves relevant documents\nif needed, and understands how to answer with information from retrieved\ndocuments and encoded knowledge as a medical expert does. We release our data\nand code for training our framework components and model weights (7B and 13B)\nto enhance capabilities in biomedical and clinical domains.\n", "date": "2024-01-27", "categories": []}
{"id": "2406.12182", "title": "Aqulia-Med LLM: Pioneering Full-Process Open-Source Medical Language\n  Models", "abstract": "  Recently, both closed-source LLMs and open-source communities have made\nsignificant strides, outperforming humans in various general domains. However,\ntheir performance in specific professional fields such as medicine, especially\nwithin the open-source community, remains suboptimal due to the complexity of\nmedical knowledge. We propose Aquila-Med, a bilingual medical LLM based on\nAquila, addressing these challenges through continue pre-training, supervised\nfine-tuning (SFT), and reinforcement learning from human feedback (RLHF). We\nconstruct a large-scale Chinese and English medical dataset for continue\npre-training and a high-quality SFT dataset, covering extensive medical\nspecialties. Additionally, we develop a high-quality Direct Preference\nOptimization (DPO) dataset for further alignment. Aquila-Med achieves notable\nresults across single-turn, multi-turn dialogues, and medical multiple-choice\nquestions, demonstrating the effectiveness of our approach. We open-source the\ndatasets and the entire training process, contributing valuable resources to\nthe research community. Our models and datasets will released at\nhttps://huggingface.co/BAAI/AquilaMed-RL.\n", "date": "2024-06-18", "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": "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": "2406.09103", "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": "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": "2406.05845", "title": "MedREQAL: Examining Medical Knowledge Recall of Large Language Models\n  via Question Answering", "abstract": "  In recent years, Large Language Models (LLMs) have demonstrated an impressive\nability to encode knowledge during pre-training on large text corpora. They can\nleverage this knowledge for downstream tasks like question answering (QA), even\nin complex areas involving health topics. Considering their high potential for\nfacilitating clinical work in the future, understanding the quality of encoded\nmedical knowledge and its recall in LLMs is an important step forward. In this\nstudy, we examine the capability of LLMs to exhibit medical knowledge recall by\nconstructing a novel dataset derived from systematic reviews -- studies\nsynthesizing evidence-based answers for specific medical questions. Through\nexperiments on the new MedREQAL dataset, comprising question-answer pairs\nextracted from rigorous systematic reviews, we assess six LLMs, such as GPT and\nMixtral, analyzing their classification and generation performance. Our\nexperimental insights into LLM performance on the novel biomedical QA dataset\nreveal the still challenging nature of this task.\n", "date": "2024-06-09", "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": "2406.03699", "title": "M-QALM: A Benchmark to Assess Clinical Reading Comprehension and\n  Knowledge Recall in Large Language Models via Question Answering", "abstract": "  There is vivid research on adapting Large Language Models (LLMs) to perform a\nvariety of tasks in high-stakes domains such as healthcare. Despite their\npopularity, there is a lack of understanding of the extent and contributing\nfactors that allow LLMs to recall relevant knowledge and combine it with\npresented information in the clinical and biomedical domain: a fundamental\npre-requisite for success on down-stream tasks. Addressing this gap, we use\nMultiple Choice and Abstractive Question Answering to conduct a large-scale\nempirical study on 22 datasets in three generalist and three specialist\nbiomedical sub-domains. Our multifaceted analysis of the performance of 15\nLLMs, further broken down by sub-domain, source of knowledge and model\narchitecture, uncovers success factors such as instruction tuning that lead to\nimproved recall and comprehension. We further show that while recently proposed\ndomain-adapted models may lack adequate knowledge, directly fine-tuning on our\ncollected medical knowledge datasets shows encouraging results, even\ngeneralising to unseen specialist sub-domains. We complement the quantitative\nresults with a skill-oriented manual error analysis, which reveals a\nsignificant gap between the models' capabilities to simply recall necessary\nknowledge and to integrate it with the presented context. To foster research\nand collaboration in this field we share M-QALM, our resources, standardised\nmethodology, and evaluation results, with the research community to facilitate\nfurther advancements in clinical knowledge representation learning within\nlanguage models.\n", "date": "2024-06-06", "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": "2406.02350", "title": "LlamaCare: A Large Medical Language Model for Enhancing Healthcare\n  Knowledge Sharing", "abstract": "  Large language models (LLMs) have shown amazing capabilities in knowledge\nmemorization and the present. However, when it comes to domain-specific\nknowledge and downstream tasks like medical, general LLMs are often unable to\ngive precise answers. In addition, when people want LLMs to answer\nclassification questions, they usually go through instruction tuning first.\nHowever, LLMs do not always give a direct index of the categorization after\ninstruction tuning. In this paper, we proposed LlamaCare, a fine-tuned medical\nlanguage model, and Extended Classification Integration(ECI), a module to\nhandle classification problems of LLMs. Our contributions are : (i) We\nfine-tuned a large language model of medical knowledge with very low carbon\nemissions and achieved similar performance with ChatGPT by a 24G GPU. (ii) We\nsolved the problem of redundant categorical answers and improved the\nperformance of LLMs by proposing a new module called Extended Classification\nIntegration. (iii) We released our processed data for one-shot and few-shot\ntraining for some benchmarks such as PubMedQA and USMLE 1-3 step. Our method\nachieves a close performance comparable to some state-of-the-art models with\nthe same quantity of parameters on benchmarks, while being more environmentally\nfriendly by using less GPU computation time. Our models, codes, and datasets\ncan be found at \\url{https://github.com/Stephen-SMJ/LLamaCare}.\n", "date": "2024-06-04", "categories": []}
{"id": "2406.02826", "title": "Exploring Robustness in Doctor-Patient Conversation Summarization: An\n  Analysis of Out-of-Domain SOAP Notes", "abstract": "  Summarizing medical conversations poses unique challenges due to the\nspecialized domain and the difficulty of collecting in-domain training data. In\nthis study, we investigate the performance of state-of-the-art doctor-patient\nconversation generative summarization models on the out-of-domain data. We\ndivide the summarization model of doctor-patient conversation into two\nconfigurations: (1) a general model, without specifying subjective (S),\nobjective (O), and assessment (A) and plan (P) notes; (2) a SOAP-oriented model\nthat generates a summary with SOAP sections. We analyzed the limitations and\nstrengths of the fine-tuning language model-based methods and GPTs on both\nconfigurations. We also conducted a Linguistic Inquiry and Word Count analysis\nto compare the SOAP notes from different datasets. The results exhibit a strong\ncorrelation for reference notes across different datasets, indicating that\nformat mismatch (i.e., discrepancies in word distribution) is not the main\ncause of performance decline on out-of-domain data. Lastly, a detailed analysis\nof SOAP notes is included to provide insights into missing information and\nhallucinations introduced by the models.\n", "date": "2024-06-05", "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": "2402.13963", "title": "Towards Building Multilingual Language Model for Medicine", "abstract": "  The development of open-source, multilingual medical language models can\nbenefit a wide, linguistically diverse audience from different regions. To\npromote this domain, we present contributions from the following: First, we\nconstruct a multilingual medical corpus, containing approximately 25.5B tokens\nencompassing 6 main languages, termed as MMedC, enabling auto-regressive domain\nadaptation for general LLMs; Second, to monitor the development of multilingual\nmedical LLMs, we propose a multilingual medical multi-choice question-answering\nbenchmark with rationale, termed as MMedBench; Third, we have assessed a number\nof open-source large language models (LLMs) on our benchmark, along with those\nfurther auto-regressive trained on MMedC. Our final model, MMed-Llama 3, with\nonly 8B parameters, achieves superior performance compared to all other\nopen-source models on both MMedBench and English benchmarks, even rivaling\nGPT-4. In conclusion, in this work, we present a large-scale corpus, a\nbenchmark and a series of models to support the development of multilingual\nmedical LLMs.\n", "date": "2024-02-21", "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": "2401.15854", "title": "LSTM-based Deep Neural Network With A Focus on Sentence Representation\n  for Sequential Sentence Classification in Medical Scientific Abstracts", "abstract": "  The Sequential Sentence Classification task within the domain of medical\nabstracts, termed as SSC, involves the categorization of sentences into\npre-defined headings based on their roles in conveying critical information in\nthe abstract. In the SSC task, sentences are sequentially related to each\nother. For this reason, the role of sentence embeddings is crucial for\ncapturing both the semantic information between words in the sentence and the\ncontextual relationship of sentences within the abstract, which then enhances\nthe SSC system performance. In this paper, we propose a LSTM-based deep\nlearning network with a focus on creating comprehensive sentence representation\nat the sentence level. To demonstrate the efficacy of the created sentence\nrepresentation, a system utilizing these sentence embeddings is also developed,\nwhich consists of a Convolutional-Recurrent neural network (C-RNN) at the\nabstract level and a multi-layer perception network (MLP) at the segment level.\nOur proposed system yields highly competitive results compared to\nstate-of-the-art systems and further enhances the F1 scores of the baseline by\n1.0%, 2.8%, and 2.6% on the benchmark datasets PudMed 200K RCT, PudMed 20K RCT\nand NICTA-PIBOSO, respectively. This indicates the significant impact of\nimproving sentence representation on boosting model performance.\n", "date": "2024-01-29", "categories": []}
{"id": "2405.20585", "title": "GAMedX: Generative AI-based Medical Entity Data Extractor Using Large\n  Language Models", "abstract": "  In the rapidly evolving field of healthcare and beyond, the integration of\ngenerative AI in Electronic Health Records (EHRs) represents a pivotal\nadvancement, addressing a critical gap in current information extraction\ntechniques. This paper introduces GAMedX, a Named Entity Recognition (NER)\napproach utilizing Large Language Models (LLMs) to efficiently extract entities\nfrom medical narratives and unstructured text generated throughout various\nphases of the patient hospital visit. By addressing the significant challenge\nof processing unstructured medical text, GAMedX leverages the capabilities of\ngenerative AI and LLMs for improved data extraction. Employing a unified\napproach, the methodology integrates open-source LLMs for NER, utilizing\nchained prompts and Pydantic schemas for structured output to navigate the\ncomplexities of specialized medical jargon. The findings reveal significant\nROUGE F1 score on one of the evaluation datasets with an accuracy of 98\\%. This\ninnovation enhances entity extraction, offering a scalable, cost-effective\nsolution for automated forms filling from unstructured data. As a result,\nGAMedX streamlines the processing of unstructured narratives, and sets a new\nstandard in NER applications, contributing significantly to theoretical and\npractical advancements beyond the medical technology sphere.\n", "date": "2024-05-31", "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": "2405.16295", "title": "Comparative Analysis of Open-Source Language Models in Summarizing\n  Medical Text Data", "abstract": "  Unstructured text in medical notes and dialogues contains rich information.\nRecent advancements in Large Language Models (LLMs) have demonstrated superior\nperformance in question answering and summarization tasks on unstructured text\ndata, outperforming traditional text analysis approaches. However, there is a\nlack of scientific studies in the literature that methodically evaluate and\nreport on the performance of different LLMs, specifically for domain-specific\ndata such as medical chart notes. We propose an evaluation approach to analyze\nthe performance of open-source LLMs such as Llama2 and Mistral for medical\nsummarization tasks, using GPT-4 as an assessor. Our innovative approach to\nquantitative evaluation of LLMs can enable quality control, support the\nselection of effective LLMs for specific tasks, and advance knowledge discovery\nin digital health.\n", "date": "2024-05-25", "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": "2405.18028", "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": "2405.15134", "title": "Efficient Biomedical Entity Linking: Clinical Text Standardization with\n  Low-Resource Techniques", "abstract": "  Clinical text is rich in information, with mentions of treatment, medication\nand anatomy among many other clinical terms. Multiple terms can refer to the\nsame core concepts which can be referred as a clinical entity. Ontologies like\nthe Unified Medical Language System (UMLS) are developed and maintained to\nstore millions of clinical entities including the definitions, relations and\nother corresponding information. These ontologies are used for standardization\nof clinical text by normalizing varying surface forms of a clinical term\nthrough Biomedical entity linking. With the introduction of transformer-based\nlanguage models, there has been significant progress in Biomedical entity\nlinking. In this work, we focus on learning through synonym pairs associated\nwith the entities. As compared to the existing approaches, our approach\nsignificantly reduces the training data and resource consumption. Moreover, we\npropose a suite of context-based and context-less reranking techniques for\nperforming the entity disambiguation. Overall, we achieve similar performance\nto the state-of-the-art zero-shot and distant supervised entity linking\ntechniques on the Medmentions dataset, the largest annotated dataset on UMLS,\nwithout any domain-based training. Finally, we show that retrieval performance\nalone might not be sufficient as an evaluation metric and introduce an article\nlevel quantitative and qualitative analysis to reveal further insights on the\nperformance of entity linking methods.\n", "date": "2024-05-24", "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": "2405.16205", "title": "GeneAgent: Self-verification Language Agent for Gene Set Knowledge\n  Discovery using Domain Databases", "abstract": "  Gene set knowledge discovery is essential for advancing human functional\ngenomics. Recent studies have shown promising performance by harnessing the\npower of Large Language Models (LLMs) on this task. Nonetheless, their results\nare subject to several limitations common in LLMs such as hallucinations. In\nresponse, we present GeneAgent, a first-of-its-kind language agent featuring\nself-verification capability. It autonomously interacts with various biological\ndatabases and leverages relevant domain knowledge to improve accuracy and\nreduce hallucination occurrences. Benchmarking on 1,106 gene sets from\ndifferent sources, GeneAgent consistently outperforms standard GPT-4 by a\nsignificant margin. Moreover, a detailed manual review confirms the\neffectiveness of the self-verification module in minimizing hallucinations and\ngenerating more reliable analytical narratives. To demonstrate its practical\nutility, we apply GeneAgent to seven novel gene sets derived from mouse B2905\nmelanoma cell lines, with expert evaluations showing that GeneAgent offers\nnovel insights into gene functions and subsequently expedites knowledge\ndiscovery.\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": "2405.13219", "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": "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": "2405.11219", "title": "Identifying and Aligning Medical Claims Made on Social Media with\n  Medical Evidence", "abstract": "  Evidence-based medicine is the practice of making medical decisions that\nadhere to the latest, and best known evidence at that time. Currently, the best\nevidence is often found in the form of documents, such as randomized control\ntrials, meta-analyses and systematic reviews. This research focuses on aligning\nmedical claims made on social media platforms with this medical evidence. By\ndoing so, individuals without medical expertise can more effectively assess the\nveracity of such medical claims. We study three core tasks: identifying medical\nclaims, extracting medical vocabulary from these claims, and retrieving\nevidence relevant to those identified medical claims. We propose a novel system\nthat can generate synthetic medical claims to aid each of these core tasks. We\nadditionally introduce a novel dataset produced by our synthetic generator\nthat, when applied to these tasks, demonstrates not only a more flexible and\nholistic approach, but also an improvement in all comparable metrics. We make\nour dataset, the Expansive Medical Claim Corpus (EMCC), available at\nhttps://zenodo.org/records/8321460\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": "2402.15189", "title": "Biomedical Entity Linking as Multiple Choice Question Answering", "abstract": "  Although biomedical entity linking (BioEL) has made significant progress with\npre-trained language models, challenges still exist for fine-grained and\nlong-tailed entities. To address these challenges, we present BioELQA, a novel\nmodel that treats Biomedical Entity Linking as Multiple Choice Question\nAnswering. BioELQA first obtains candidate entities with a fast retriever,\njointly presents the mention and candidate entities to a generator, and then\noutputs the predicted symbol associated with its chosen entity. This\nformulation enables explicit comparison of different candidate entities, thus\ncapturing fine-grained interactions between mentions and entities, as well as\namong entities themselves. To improve generalization for long-tailed entities,\nwe retrieve similar labeled training instances as clues and concatenate the\ninput with retrieved instances for the generator. Extensive experimental\nresults show that BioELQA outperforms state-of-the-art baselines on several\ndatasets.\n", "date": "2024-02-23", "categories": []}
{"id": "2405.10893", "title": "COGNET-MD, an evaluation framework and dataset for Large Language Model\n  benchmarks in the medical domain", "abstract": "  Large Language Models (LLMs) constitute a breakthrough state-of-the-art\nArtificial Intelligence (AI) technology which is rapidly evolving and promises\nto aid in medical diagnosis either by assisting doctors or by simulating a\ndoctor's workflow in more advanced and complex implementations. In this\ntechnical paper, we outline Cognitive Network Evaluation Toolkit for Medical\nDomains (COGNET-MD), which constitutes a novel benchmark for LLM evaluation in\nthe medical domain. Specifically, we propose a scoring-framework with increased\ndifficulty to assess the ability of LLMs in interpreting medical text. The\nproposed framework is accompanied with a database of Multiple Choice Quizzes\n(MCQs). To ensure alignment with current medical trends and enhance safety,\nusefulness, and applicability, these MCQs have been constructed in\ncollaboration with several associated medical experts in various medical\ndomains and are characterized by varying degrees of difficulty. The current\n(first) version of the database includes the medical domains of Psychiatry,\nDentistry, Pulmonology, Dermatology and Endocrinology, but it will be\ncontinuously extended and expanded to include additional medical domains.\n", "date": "2024-05-17", "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": "2405.09153", "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": "2311.17330", "title": "Biomedical knowledge graph-optimized prompt generation for large\n  language models", "abstract": "  Large Language Models (LLMs) are being adopted at an unprecedented rate, yet\nstill face challenges in knowledge-intensive domains like biomedicine.\nSolutions such as pre-training and domain-specific fine-tuning add substantial\ncomputational overhead, requiring further domain expertise. Here, we introduce\na token-optimized and robust Knowledge Graph-based Retrieval Augmented\nGeneration (KG-RAG) framework by leveraging a massive biomedical KG (SPOKE)\nwith LLMs such as Llama-2-13b, GPT-3.5-Turbo and GPT-4, to generate meaningful\nbiomedical text rooted in established knowledge. Compared to the existing RAG\ntechnique for Knowledge Graphs, the proposed method utilizes minimal graph\nschema for context extraction and uses embedding methods for context pruning.\nThis optimization in context extraction results in more than 50% reduction in\ntoken consumption without compromising the accuracy, making a cost-effective\nand robust RAG implementation on proprietary LLMs. KG-RAG consistently enhanced\nthe performance of LLMs across diverse biomedical prompts by generating\nresponses rooted in established knowledge, accompanied by accurate provenance\nand statistical evidence (if available) to substantiate the claims. Further\nbenchmarking on human curated datasets, such as biomedical true/false and\nmultiple-choice questions (MCQ), showed a remarkable 71% boost in the\nperformance of the Llama-2 model on the challenging MCQ dataset, demonstrating\nthe framework's capacity to empower open-source models with fewer parameters\nfor domain specific questions. Furthermore, KG-RAG enhanced the performance of\nproprietary GPT models, such as GPT-3.5 and GPT-4. In summary, the proposed\nframework combines explicit and implicit knowledge of KG and LLM in a token\noptimized fashion, thus enhancing the adaptability of general-purpose LLMs to\ntackle domain-specific questions in a cost-effective fashion.\n", "date": "2023-11-29", "categories": []}
{"id": "2405.07348", "title": "MedConceptsQA: Open Source Medical Concepts QA Benchmark", "abstract": "  We present MedConceptsQA, a dedicated open source benchmark for medical\nconcepts question answering. The benchmark comprises of questions of various\nmedical concepts across different vocabularies: diagnoses, procedures, and\ndrugs. The questions are categorized into three levels of difficulty: easy,\nmedium, and hard. We conducted evaluations of the benchmark using various Large\nLanguage Models. Our findings show that pre-trained clinical Large Language\nModels achieved accuracy levels close to random guessing on this benchmark,\ndespite being pre-trained on medical data. However, GPT-4 achieves an absolute\naverage improvement of nearly 27%-37% (27% for zero-shot learning and 37% for\nfew-shot learning) when compared to clinical Large Language Models. Our\nbenchmark serves as a valuable resource for evaluating the understanding and\nreasoning of medical concepts by Large Language Models. Our benchmark is\navailable at https://huggingface.co/datasets/ofir408/MedConceptsQA\n", "date": "2024-05-12", "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": "2405.08784", "title": "Refinement of an Epilepsy Dictionary through Human Annotation of\n  Health-related posts on Instagram", "abstract": "  We used a dictionary built from biomedical terminology extracted from various\nsources such as DrugBank, MedDRA, MedlinePlus, TCMGeneDIT, to tag more than 8\nmillion Instagram posts by users who have mentioned an epilepsy-relevant drug\nat least once, between 2010 and early 2016. A random sample of 1,771 posts with\n2,947 term matches was evaluated by human annotators to identify\nfalse-positives. OpenAI's GPT series models were compared against human\nannotation. Frequent terms with a high false-positive rate were removed from\nthe dictionary. Analysis of the estimated false-positive rates of the annotated\nterms revealed 8 ambiguous terms (plus synonyms) used in Instagram posts, which\nwere removed from the original dictionary. To study the effect of removing\nthose terms, we constructed knowledge networks using the refined and the\noriginal dictionaries and performed an eigenvector-centrality analysis on both\nnetworks. We show that the refined dictionary thus produced leads to a\nsignificantly different rank of important terms, as measured by their\neigenvector-centrality of the knowledge networks. Furthermore, the most\nimportant terms obtained after refinement are of greater medical relevance. In\naddition, we show that OpenAI's GPT series models fare worse than human\nannotators in this task.\n", "date": "2024-05-14", "categories": []}
{"id": "2405.07468", "title": "Evaluating large language models in medical applications: a survey", "abstract": "  Large language models (LLMs) have emerged as powerful tools with\ntransformative potential across numerous domains, including healthcare and\nmedicine. In the medical domain, LLMs hold promise for tasks ranging from\nclinical decision support to patient education. However, evaluating the\nperformance of LLMs in medical contexts presents unique challenges due to the\ncomplex and critical nature of medical information. This paper provides a\ncomprehensive overview of the landscape of medical LLM evaluation, synthesizing\ninsights from existing studies and highlighting evaluation data sources, task\nscenarios, and evaluation methods. Additionally, it identifies key challenges\nand opportunities in medical LLM evaluation, emphasizing the need for continued\nresearch and innovation to ensure the responsible integration of LLMs into\nclinical practice.\n", "date": "2024-05-13", "categories": []}
{"id": "2405.07500", "title": "PromptLink: Leveraging Large Language Models for Cross-Source Biomedical\n  Concept Linking", "abstract": "  Linking (aligning) biomedical concepts across diverse data sources enables\nvarious integrative analyses, but it is challenging due to the discrepancies in\nconcept naming conventions. Various strategies have been developed to overcome\nthis challenge, such as those based on string-matching rules, manually crafted\nthesauri, and machine learning models. However, these methods are constrained\nby limited prior biomedical knowledge and can hardly generalize beyond the\nlimited amounts of rules, thesauri, or training samples. Recently, large\nlanguage models (LLMs) have exhibited impressive results in diverse biomedical\nNLP tasks due to their unprecedentedly rich prior knowledge and strong\nzero-shot prediction abilities. However, LLMs suffer from issues including high\ncosts, limited context length, and unreliable predictions. In this research, we\npropose PromptLink, a novel biomedical concept linking framework that leverages\nLLMs. It first employs a biomedical-specialized pre-trained language model to\ngenerate candidate concepts that can fit in the LLM context windows. Then it\nutilizes an LLM to link concepts through two-stage prompts, where the\nfirst-stage prompt aims to elicit the biomedical prior knowledge from the LLM\nfor the concept linking task and the second-stage prompt enforces the LLM to\nreflect on its own predictions to further enhance their reliability. Empirical\nresults on the concept linking task between two EHR datasets and an external\nbiomedical KG demonstrate the effectiveness of PromptLink. Furthermore,\nPromptLink is a generic framework without reliance on additional prior\nknowledge, context, or training data, making it well-suited for concept linking\nacross various types of data sources. The source code is available at\nhttps://github.com/constantjxyz/PromptLink.\n", "date": "2024-05-13", "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": "2405.06145", "title": "Reddit-Impacts: A Named Entity Recognition Dataset for Analyzing\n  Clinical and Social Effects of Substance Use Derived from Social Media", "abstract": "  Substance use disorders (SUDs) are a growing concern globally, necessitating\nenhanced understanding of the problem and its trends through data-driven\nresearch. Social media are unique and important sources of information about\nSUDs, particularly since the data in such sources are often generated by people\nwith lived experiences. In this paper, we introduce Reddit-Impacts, a\nchallenging Named Entity Recognition (NER) dataset curated from subreddits\ndedicated to discussions on prescription and illicit opioids, as well as\nmedications for opioid use disorder. The dataset specifically concentrates on\nthe lesser-studied, yet critically important, aspects of substance use--its\nclinical and social impacts. We collected data from chosen subreddits using the\npublicly available Application Programming Interface for Reddit. We manually\nannotated text spans representing clinical and social impacts reported by\npeople who also reported personal nonmedical use of substances including but\nnot limited to opioids, stimulants and benzodiazepines. Our objective is to\ncreate a resource that can enable the development of systems that can\nautomatically detect clinical and social impacts of substance use from\ntext-based social media data. The successful development of such systems may\nenable us to better understand how nonmedical use of substances affects\nindividual health and societal dynamics, aiding the development of effective\npublic health strategies. In addition to creating the annotated data set, we\napplied several machine learning models to establish baseline performances.\nSpecifically, we experimented with transformer models like BERT, and RoBERTa,\none few-shot learning model DANN by leveraging the full training dataset, and\nGPT-3.5 by using one-shot learning, for automatic NER of clinical and social\nimpacts. The dataset has been made available through the 2024 SMM4H shared\ntasks.\n", "date": "2024-05-09", "categories": []}
{"id": "2304.08448", "title": "An Iterative Optimizing Framework for Radiology Report Summarization\n  with ChatGPT", "abstract": "  The 'Impression' section of a radiology report is a critical basis for\ncommunication between radiologists and other physicians, and it is typically\nwritten by radiologists based on the 'Findings' section. However, writing\nnumerous impressions can be laborious and error-prone for radiologists.\nAlthough recent studies have achieved promising results in automatic impression\ngeneration using large-scale medical text data for pre-training and fine-tuning\npre-trained language models, such models often require substantial amounts of\nmedical text data and have poor generalization performance. While large\nlanguage models (LLMs) like ChatGPT have shown strong generalization\ncapabilities and performance, their performance in specific domains, such as\nradiology, remains under-investigated and potentially limited. To address this\nlimitation, we propose ImpressionGPT, which leverages the in-context learning\ncapability of LLMs by constructing dynamic contexts using domain-specific,\nindividualized data. This dynamic prompt approach enables the model to learn\ncontextual knowledge from semantically similar examples from existing data.\nAdditionally, we design an iterative optimization algorithm that performs\nautomatic evaluation on the generated impression results and composes the\ncorresponding instruction prompts to further optimize the model. The proposed\nImpressionGPT model achieves state-of-the-art performance on both MIMIC-CXR and\nOpenI datasets without requiring additional training data or fine-tuning the\nLLMs. This work presents a paradigm for localizing LLMs that can be applied in\na wide range of similar application scenarios, bridging the gap between\ngeneral-purpose LLMs and the specific language processing needs of various\ndomains.\n", "date": "2023-04-17", "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": "2307.00186", "title": "How far is Language Model from 100% Few-shot Named Entity Recognition in\n  Medical Domain", "abstract": "  Recent advancements in language models (LMs) have led to the emergence of\npowerful models such as Small LMs (e.g., T5) and Large LMs (e.g., GPT-4). These\nmodels have demonstrated exceptional capabilities across a wide range of tasks,\nsuch as name entity recognition (NER) in the general domain. (We define SLMs as\npre-trained models with fewer parameters compared to models like GPT-3/3.5/4,\nsuch as T5, BERT, and others.) Nevertheless, their efficacy in the medical\nsection remains uncertain and the performance of medical NER always needs high\naccuracy because of the particularity of the field. This paper aims to provide\na thorough investigation to compare the performance of LMs in medical few-shot\nNER and answer How far is LMs from 100\\% Few-shot NER in Medical Domain, and\nmoreover to explore an effective entity recognizer to help improve the NER\nperformance. Based on our extensive experiments conducted on 16 NER models\nspanning from 2018 to 2023, our findings clearly indicate that LLMs outperform\nSLMs in few-shot medical NER tasks, given the presence of suitable examples and\nappropriate logical frameworks. Despite the overall superiority of LLMs in\nfew-shot medical NER tasks, it is important to note that they still encounter\nsome challenges, such as misidentification, wrong template prediction, etc.\nBuilding on previous findings, we introduce a simple and effective method\ncalled \\textsc{RT} (Retrieving and Thinking), which serves as retrievers,\nfinding relevant examples, and as thinkers, employing a step-by-step reasoning\nprocess. Experimental results show that our proposed \\textsc{RT} framework\nsignificantly outperforms the strong open baselines on the two open medical\nbenchmark datasets\n", "date": "2023-07-01", "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": "2405.01886", "title": "Aloe: A Family of Fine-tuned Open Healthcare LLMs", "abstract": "  As the capabilities of Large Language Models (LLMs) in healthcare and\nmedicine continue to advance, there is a growing need for competitive\nopen-source models that can safeguard public interest. With the increasing\navailability of highly competitive open base models, the impact of continued\npre-training is increasingly uncertain. In this work, we explore the role of\ninstruct tuning, model merging, alignment, red teaming and advanced inference\nschemes, as means to improve current open models. To that end, we introduce the\nAloe family, a set of open medical LLMs highly competitive within its scale\nrange. Aloe models are trained on the current best base models (Mistral, LLaMA\n3), using a new custom dataset which combines public data sources improved with\nsynthetic Chain of Thought (CoT). Aloe models undergo an alignment phase,\nbecoming one of the first few policy-aligned open healthcare LLM using Direct\nPreference Optimization, setting a new standard for ethical performance in\nhealthcare LLMs. Model evaluation expands to include various bias and toxicity\ndatasets, a dedicated red teaming effort, and a much-needed risk assessment for\nhealthcare LLMs. Finally, to explore the limits of current LLMs in inference,\nwe study several advanced prompt engineering strategies to boost performance\nacross benchmarks, yielding state-of-the-art results for open healthcare 7B\nLLMs, unprecedented at this scale.\n", "date": "2024-05-03", "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": "2310.18463", "title": "Benchingmaking Large Langage Models in Biomedical Triple Extraction", "abstract": "  Biomedical triple extraction systems aim to automatically extract biomedical\nentities and relations between entities. The exploration of applying large\nlanguage models (LLM) to triple extraction is still relatively unexplored. In\nthis work, we mainly focus on sentence-level biomedical triple extraction.\nFurthermore, the absence of a high-quality biomedical triple extraction dataset\nimpedes the progress in developing robust triple extraction systems. To address\nthese challenges, initially, we compare the performance of various large\nlanguage models. Additionally, we present GIT, an expert-annotated biomedical\ntriple extraction dataset that covers a wider range of relation types.\n", "date": "2023-10-27", "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": "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": "2404.15488", "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": "2309.13061", "title": "Applying BioBERT to Extract Germline Gene-Disease Associations for\n  Building a Knowledge Graph from the Biomedical Literature", "abstract": "  Published biomedical information has and continues to rapidly increase. The\nrecent advancements in Natural Language Processing (NLP), have generated\nconsiderable interest in automating the extraction, normalization, and\nrepresentation of biomedical knowledge about entities such as genes and\ndiseases. Our study analyzes germline abstracts in the construction of\nknowledge graphs of the of the immense work that has been done in this area for\ngenes and diseases. This paper presents SimpleGermKG, an automatic knowledge\ngraph construction approach that connects germline genes and diseases. For the\nextraction of genes and diseases, we employ BioBERT, a pre-trained BERT model\non biomedical corpora. We propose an ontology-based and rule-based algorithm to\nstandardize and disambiguate medical terms. For semantic relationships between\narticles, genes, and diseases, we implemented a part-whole relation approach to\nconnect each entity with its data source and visualize them in a graph-based\nknowledge representation. Lastly, we discuss the knowledge graph applications,\nlimitations, and challenges to inspire the future research of germline corpora.\nOur knowledge graph contains 297 genes, 130 diseases, and 46,747 triples.\nGraph-based visualizations are used to show the results.\n", "date": "2023-09-11", "categories": []}
{"id": "2404.00152", "title": "On-the-fly Definition Augmentation of LLMs for Biomedical NER", "abstract": "  Despite their general capabilities, LLMs still struggle on biomedical NER\ntasks, which are difficult due to the presence of specialized terminology and\nlack of training data. In this work we set out to improve LLM performance on\nbiomedical NER in limited data settings via a new knowledge augmentation\napproach which incorporates definitions of relevant concepts on-the-fly. During\nthis process, to provide a test bed for knowledge augmentation, we perform a\ncomprehensive exploration of prompting strategies. Our experiments show that\ndefinition augmentation is useful for both open source and closed LLMs. For\nexample, it leads to a relative improvement of 15\\% (on average) in GPT-4\nperformance (F1) across all (six) of our test datasets. We conduct extensive\nablations and analyses to demonstrate that our performance improvements stem\nfrom adding relevant definitional knowledge. We find that careful prompting\nstrategies also improve LLM performance, allowing them to outperform fine-tuned\nlanguage models in few-shot settings. To facilitate future research in this\ndirection, we release our code at https://github.com/allenai/beacon.\n", "date": "2024-03-29", "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": "2404.13057", "title": "\"Hey..! This medicine made me sick\": Sentiment Analysis of\n  User-Generated Drug Reviews using Machine Learning Techniques", "abstract": "  Sentiment analysis has become increasingly important in healthcare,\nespecially in the biomedical and pharmaceutical fields. The data generated by\nthe general public on the effectiveness, side effects, and adverse drug\nreactions are goldmines for different agencies and medicine producers to\nunderstand the concerns and reactions of people. Despite the challenge of\nobtaining datasets on drug-related problems, sentiment analysis on this topic\nwould be a significant boon to the field. This project proposes a drug review\nclassification system that classifies user reviews on a particular drug into\ndifferent classes, such as positive, negative, and neutral. This approach uses\na dataset that is collected from publicly available sources containing drug\nreviews, such as drugs.com. The collected data is manually labeled and verified\nmanually to ensure that the labels are correct. Three pre-trained language\nmodels, such as BERT, SciBERT, and BioBERT, are used to obtain embeddings,\nwhich were later used as features to different machine learning classifiers\nsuch as decision trees, support vector machines, random forests, and also deep\nlearning algorithms such as recurrent neural networks. The performance of these\nclassifiers is quantified using precision, recall, and f1-score, and the\nresults show that the proposed approaches are useful in analyzing the\nsentiments of people on different drugs.\n", "date": "2024-04-09", "categories": []}
{"id": "2404.13779", "title": "Automated Text Mining of Experimental Methodologies from Biomedical\n  Literature", "abstract": "  Biomedical literature is a rapidly expanding field of science and technology.\nClassification of biomedical texts is an essential part of biomedicine\nresearch, especially in the field of biology. This work proposes the fine-tuned\nDistilBERT, a methodology-specific, pre-trained generative classification\nlanguage model for mining biomedicine texts. The model has proven its\neffectiveness in linguistic understanding capabilities and has reduced the size\nof BERT models by 40\\% but by 60\\% faster. The main objective of this project\nis to improve the model and assess the performance of the model compared to the\nnon-fine-tuned model. We used DistilBert as a support model and pre-trained on\na corpus of 32,000 abstracts and complete text articles; our results were\nimpressive and surpassed those of traditional literature classification methods\nby using RNN or LSTM. Our aim is to integrate this highly specialised and\nspecific model into different research industries.\n", "date": "2024-04-21", "categories": []}
{"id": "2404.14209", "title": "EnzChemRED, a rich enzyme chemistry relation extraction dataset", "abstract": "  Expert curation is essential to capture knowledge of enzyme functions from\nthe scientific literature in FAIR open knowledgebases but cannot keep pace with\nthe rate of new discoveries and new publications. In this work we present\nEnzChemRED, for Enzyme Chemistry Relation Extraction Dataset, a new training\nand benchmarking dataset to support the development of Natural Language\nProcessing (NLP) methods such as (large) language models that can assist enzyme\ncuration. EnzChemRED consists of 1,210 expert curated PubMed abstracts in which\nenzymes and the chemical reactions they catalyze are annotated using\nidentifiers from the UniProt Knowledgebase (UniProtKB) and the ontology of\nChemical Entities of Biological Interest (ChEBI). We show that fine-tuning\npre-trained language models with EnzChemRED can significantly boost their\nability to identify mentions of proteins and chemicals in text (Named Entity\nRecognition, or NER) and to extract the chemical conversions in which they\nparticipate (Relation Extraction, or RE), with average F1 score of 86.30% for\nNER, 86.66% for RE for chemical conversion pairs, and 83.79% for RE for\nchemical conversion pairs and linked enzymes. We combine the best performing\nmethods after fine-tuning using EnzChemRED to create an end-to-end pipeline for\nknowledge extraction from text and apply this to abstracts at PubMed scale to\ncreate a draft map of enzyme functions in literature to guide curation efforts\nin UniProtKB and the reaction knowledgebase Rhea. The EnzChemRED corpus is\nfreely available at https://ftp.expasy.org/databases/rhea/nlp/.\n", "date": "2024-04-22", "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": "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": "2404.08654", "title": "Optimal path for Biomedical Text Summarization Using Pointer GPT", "abstract": "  Biomedical text summarization is a critical tool that enables clinicians to\neffectively ascertain patient status. Traditionally, text summarization has\nbeen accomplished with transformer models, which are capable of compressing\nlong documents into brief summaries. However, transformer models are known to\nbe among the most challenging natural language processing (NLP) tasks.\nSpecifically, GPT models have a tendency to generate factual errors, lack\ncontext, and oversimplify words. To address these limitations, we replaced the\nattention mechanism in the GPT model with a pointer network. This modification\nwas designed to preserve the core values of the original text during the\nsummarization process. The effectiveness of the Pointer-GPT model was evaluated\nusing the ROUGE score. The results demonstrated that Pointer-GPT outperformed\nthe original GPT model. These findings suggest that pointer networks can be a\nvaluable addition to EMR systems and can provide clinicians with more accurate\nand informative summaries of patient medical records. This research has the\npotential to usher in a new paradigm in EMR systems and to revolutionize the\nway that clinicians interact with patient medical records.\n", "date": "2024-03-22", "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": "2404.07613", "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": "2304.09667", "title": "GeneGPT: Augmenting Large Language Models with Domain Tools for Improved\n  Access to Biomedical Information", "abstract": "  While large language models (LLMs) have been successfully applied to various\ntasks, they still face challenges with hallucinations. Augmenting LLMs with\ndomain-specific tools such as database utilities can facilitate easier and more\nprecise access to specialized knowledge. In this paper, we present GeneGPT, a\nnovel method for teaching LLMs to use the Web APIs of the National Center for\nBiotechnology Information (NCBI) for answering genomics questions.\nSpecifically, we prompt Codex to solve the GeneTuring tests with NCBI Web APIs\nby in-context learning and an augmented decoding algorithm that can detect and\nexecute API calls. Experimental results show that GeneGPT achieves\nstate-of-the-art performance on eight tasks in the GeneTuring benchmark with an\naverage score of 0.83, largely surpassing retrieval-augmented LLMs such as the\nnew Bing (0.44), biomedical LLMs such as BioMedLM (0.08) and BioGPT (0.04), as\nwell as GPT-3 (0.16) and ChatGPT (0.12). Our further analyses suggest that: (1)\nAPI demonstrations have good cross-task generalizability and are more useful\nthan documentations for in-context learning; (2) GeneGPT can generalize to\nlonger chains of API calls and answer multi-hop questions in GeneHop, a novel\ndataset introduced in this work; (3) Different types of errors are enriched in\ndifferent tasks, providing valuable insights for future improvements.\n", "date": "2023-04-19", "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": "2404.06121", "title": "Detection of fields of applications in biomedical abstracts with the\n  support of argumentation elements", "abstract": "  Focusing on particular facts, instead of the complete text, can potentially\nimprove searching for specific information in the scientific literature. In\nparticular, argumentative elements allow focusing on specific parts of a\npublication, e.g., the background section or the claims from the authors. We\nevaluated some tools for the extraction of argumentation elements for a\nspecific task in biomedicine, namely, for detecting the fields of the\napplication in a biomedical publication, e.g, whether it addresses the problem\nof disease diagnosis or drug development. We performed experiments with the\nPubMedBERT pre-trained model, which was fine-tuned on a specific corpus for the\ntask. We compared the use of title and abstract to restricting to only some\nargumentative elements. The top F1 scores ranged from 0.22 to 0.84, depending\non the field of application. The best argumentative labels were the ones\nrelated the conclusion and background sections of an abstract.\n", "date": "2024-04-09", "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": "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": "2404.05144", "title": "Enhancing Clinical Efficiency through LLM: Discharge Note Generation for\n  Cardiac Patients", "abstract": "  Medical documentation, including discharge notes, is crucial for ensuring\npatient care quality, continuity, and effective medical communication. However,\nthe manual creation of these documents is not only time-consuming but also\nprone to inconsistencies and potential errors. The automation of this\ndocumentation process using artificial intelligence (AI) represents a promising\narea of innovation in healthcare. This study directly addresses the\ninefficiencies and inaccuracies in creating discharge notes manually,\nparticularly for cardiac patients, by employing AI techniques, specifically\nlarge language model (LLM). Utilizing a substantial dataset from a cardiology\ncenter, encompassing wide-ranging medical records and physician assessments,\nour research evaluates the capability of LLM to enhance the documentation\nprocess. Among the various models assessed, Mistral-7B distinguished itself by\naccurately generating discharge notes that significantly improve both\ndocumentation efficiency and the continuity of care for patients. These notes\nunderwent rigorous qualitative evaluation by medical expert, receiving high\nmarks for their clinical relevance, completeness, readability, and contribution\nto informed decision-making and care planning. Coupled with quantitative\nanalyses, these results confirm Mistral-7B's efficacy in distilling complex\nmedical information into concise, coherent summaries. Overall, our findings\nilluminate the considerable promise of specialized LLM, such as Mistral-7B, in\nrefining healthcare documentation workflows and advancing patient care. This\nstudy lays the groundwork for further integrating advanced AI technologies in\nhealthcare, demonstrating their potential to revolutionize patient\ndocumentation and support better care outcomes.\n", "date": "2024-04-08", "categories": []}
{"id": "2310.18912", "title": "Sentence Bag Graph Formulation for Biomedical Distant Supervision\n  Relation Extraction", "abstract": "  We introduce a novel graph-based framework for alleviating key challenges in\ndistantly-supervised relation extraction and demonstrate its effectiveness in\nthe challenging and important domain of biomedical data. Specifically, we\npropose a graph view of sentence bags referring to an entity pair, which\nenables message-passing based aggregation of information related to the entity\npair over the sentence bag. The proposed framework alleviates the common\nproblem of noisy labeling in distantly supervised relation extraction and also\neffectively incorporates inter-dependencies between sentences within a bag.\nExtensive experiments on two large-scale biomedical relation datasets and the\nwidely utilized NYT dataset demonstrate that our proposed framework\nsignificantly outperforms the state-of-the-art methods for biomedical distant\nsupervision relation extraction while also providing excellent performance for\nrelation extraction in the general text mining domain.\n", "date": "2023-10-29", "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": "2308.08833", "title": "CMB: A Comprehensive Medical Benchmark in Chinese", "abstract": "  Large Language Models (LLMs) provide a possibility to make a great\nbreakthrough in medicine. The establishment of a standardized medical benchmark\nbecomes a fundamental cornerstone to measure progression. However, medical\nenvironments in different regions have their local characteristics, e.g., the\nubiquity and significance of traditional Chinese medicine within China.\nTherefore, merely translating English-based medical evaluation may result in\n\\textit{contextual incongruities} to a local region. To solve the issue, we\npropose a localized medical benchmark called CMB, a Comprehensive Medical\nBenchmark in Chinese, designed and rooted entirely within the native Chinese\nlinguistic and cultural framework. While traditional Chinese medicine is\nintegral to this evaluation, it does not constitute its entirety. Using this\nbenchmark, we have evaluated several prominent large-scale LLMs, including\nChatGPT, GPT-4, dedicated Chinese LLMs, and LLMs specialized in the medical\ndomain. We hope this benchmark provide first-hand experience in existing LLMs\nfor medicine and also facilitate the widespread adoption and enhancement of\nmedical LLMs within China. Our data and code are publicly available at\nhttps://github.com/FreedomIntelligence/CMB.\n", "date": "2023-08-17", "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": "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": "2403.19726", "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": "2403.19802", "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": "2403.18421", "title": "BioMedLM: A 2.7B Parameter Language Model Trained On Biomedical Text", "abstract": "  Models such as GPT-4 and Med-PaLM 2 have demonstrated impressive performance\non a wide variety of biomedical NLP tasks. However, these models have hundreds\nof billions of parameters, are computationally expensive to run, require users\nto send their input data over the internet, and are trained on unknown data\nsources. Can smaller, more targeted models compete? To address this question,\nwe build and release BioMedLM, a 2.7 billion parameter GPT-style autoregressive\nmodel trained exclusively on PubMed abstracts and full articles. When\nfine-tuned, BioMedLM can produce strong multiple-choice biomedical\nquestion-answering results competitive with much larger models, such as\nachieving a score of 57.3% on MedMCQA (dev) and 69.0% on the MMLU Medical\nGenetics exam. BioMedLM can also be fine-tuned to produce useful answers to\npatient questions on medical topics. This demonstrates that smaller models can\npotentially serve as transparent, privacy-preserving, economical and\nenvironmentally friendly foundations for particular NLP applications, such as\nin biomedicine. The model is available on the Hugging Face Hub:\nhttps://huggingface.co/stanford-crfm/BioMedLM.\n", "date": "2024-03-27", "categories": []}
{"id": "2312.08274", "title": "High-throughput Biomedical Relation Extraction for Semi-Structured Web\n  Articles Empowered by Large Language Models", "abstract": "  Objective: To develop a high-throughput biomedical relation extraction system\nthat takes advantage of the large language models'(LLMs) reading comprehension\nability and biomedical world knowledge in a scalable and evidential manner.\nMethods: We formulate the relation extraction task as binary classifications\nfor large language models. Specifically, LLMs make the decision based on the\nexternal corpus and its world knowledge, giving the reason for the judgment for\nfactual verification. This method is tailored for semi-structured web articles,\nwherein we designate the main title as the tail entity and explicitly\nincorporate it into the context, and the potential head entities are matched\nbased on a biomedical thesaurus. Moreover, lengthy contents are sliced into\ntext chunks, embedded, and retrieved with additional embedding models. Results:\nUsing an open-source LLM, we extracted 248659 relation triplets of three\ndistinct relation types from three reputable biomedical websites. To assess the\nefficacy of the basic pipeline employed for biomedical relation extraction, we\ncurated a benchmark dataset annotated by a medical expert. Evaluation results\nindicate that the pipeline exhibits performance comparable to that of GPT-4.\nCase studies further illuminate challenges faced by contemporary LLMs in the\ncontext of biomedical relation extraction for semi-structured web articles.\nConclusion: The proposed method has demonstrated its effectiveness in\nleveraging the strengths of LLMs for high-throughput biomedical relation\nextraction. Its adaptability is evident, as it can be seamlessly extended to\ndiverse semi-structured biomedical websites, facilitating the extraction of\nvarious types of biomedical relations with ease.\n", "date": "2023-12-13", "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": "2309.08503", "title": "HealthFC: Verifying Health Claims with Evidence-Based Medical\n  Fact-Checking", "abstract": "  In the digital age, seeking health advice on the Internet has become a common\npractice. At the same time, determining the trustworthiness of online medical\ncontent is increasingly challenging. Fact-checking has emerged as an approach\nto assess the veracity of factual claims using evidence from credible knowledge\nsources. To help advance automated Natural Language Processing (NLP) solutions\nfor this task, in this paper we introduce a novel dataset HealthFC. It consists\nof 750 health-related claims in German and English, labeled for veracity by\nmedical experts and backed with evidence from systematic reviews and clinical\ntrials. We provide an analysis of the dataset, highlighting its characteristics\nand challenges. The dataset can be used for NLP tasks related to automated\nfact-checking, such as evidence retrieval, claim verification, or explanation\ngeneration. For testing purposes, we provide baseline systems based on\ndifferent approaches, examine their performance, and discuss the findings. We\nshow that the dataset is a challenging test bed with a high potential for\nfuture use.\n", "date": "2023-09-15", "categories": []}
{"id": "2403.15673", "title": "AI for Biomedicine in the Era of Large Language Models", "abstract": "  The capabilities of AI for biomedicine span a wide spectrum, from the atomic\nlevel, where it solves partial differential equations for quantum systems, to\nthe molecular level, predicting chemical or protein structures, and further\nextending to societal predictions like infectious disease outbreaks. Recent\nadvancements in large language models, exemplified by models like ChatGPT, have\nshowcased significant prowess in natural language tasks, such as translating\nlanguages, constructing chatbots, and answering questions. When we consider\nbiomedical data, we observe a resemblance to natural language in terms of\nsequences: biomedical literature and health records presented as text,\nbiological sequences or sequencing data arranged in sequences, or sensor data\nlike brain signals as time series. The question arises: Can we harness the\npotential of recent large language models to drive biomedical knowledge\ndiscoveries? In this survey, we will explore the application of large language\nmodels to three crucial categories of biomedical data: 1) textual data, 2)\nbiological sequences, and 3) brain signals. Furthermore, we will delve into\nlarge language model challenges in biomedical research, including ensuring\ntrustworthiness, achieving personalization, and adapting to multi-modal data\nrepresentation\n", "date": "2024-03-23", "categories": []}
{"id": "2403.15800", "title": "MRC-based Nested Medical NER with Co-prediction and Adaptive\n  Pre-training", "abstract": "  In medical information extraction, medical Named Entity Recognition (NER) is\nindispensable, playing a crucial role in developing medical knowledge graphs,\nenhancing medical question-answering systems, and analyzing electronic medical\nrecords. The challenge in medical NER arises from the complex nested structures\nand sophisticated medical terminologies, distinguishing it from its\ncounterparts in traditional domains. In response to these complexities, we\npropose a medical NER model based on Machine Reading Comprehension (MRC), which\nuses a task-adaptive pre-training strategy to improve the model's capability in\nthe medical field. Meanwhile, our model introduces multiple word-pair\nembeddings and multi-granularity dilated convolution to enhance the model's\nrepresentation ability and uses a combined predictor of Biaffine and MLP to\nimprove the model's recognition performance. Experimental evaluations conducted\non the CMeEE, a benchmark for Chinese nested medical NER, demonstrate that our\nproposed model outperforms the compared state-of-the-art (SOTA) models.\n", "date": "2024-03-23", "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": "2403.12297", "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": "2403.12386", "title": "Pipelined Biomedical Event Extraction Rivaling Joint Learning", "abstract": "  Biomedical event extraction is an information extraction task to obtain\nevents from biomedical text, whose targets include the type, the trigger, and\nthe respective arguments involved in an event. Traditional biomedical event\nextraction usually adopts a pipelined approach, which contains trigger\nidentification, argument role recognition, and finally event construction\neither using specific rules or by machine learning. In this paper, we propose\nan n-ary relation extraction method based on the BERT pre-training model to\nconstruct Binding events, in order to capture the semantic information about an\nevent's context and its participants. The experimental results show that our\nmethod achieves promising results on the GE11 and GE13 corpora of the BioNLP\nshared task with F1 scores of 63.14% and 59.40%, respectively. It demonstrates\nthat by significantly improving theperformance of Binding events, the overall\nperformance of the pipelined event extraction approach or even exceeds those of\ncurrent joint learning methods.\n", "date": "2024-03-19", "categories": []}
{"id": "2306.16001", "title": "Streamlining Social Media Information Retrieval for COVID-19 Research\n  with Deep Learning", "abstract": "  Objective: Social media-based public health research is crucial for epidemic\nsurveillance, but most studies identify relevant corpora with keyword-matching.\nThis study develops a system to streamline the process of curating colloquial\nmedical dictionaries. We demonstrate the pipeline by curating a UMLS-colloquial\nsymptom dictionary from COVID-19-related tweets as proof of concept. Methods:\nCOVID-19-related tweets from February 1, 2020, to April 30, 2022 were used. The\npipeline includes three modules: a named entity recognition module to detect\nsymptoms in tweets; an entity normalization module to aggregate detected\nentities; and a mapping module that iteratively maps entities to Unified\nMedical Language System concepts. A random 500 entity sample were drawn from\nthe final dictionary for accuracy validation. Additionally, we conducted a\nsymptom frequency distribution analysis to compare our dictionary to a\npre-defined lexicon from previous research. Results: We identified 498,480\nunique symptom entity expressions from the tweets. Pre-processing reduces the\nnumber to 18,226. The final dictionary contains 38,175 unique expressions of\nsymptoms that can be mapped to 966 UMLS concepts (accuracy = 95%). Symptom\ndistribution analysis found that our dictionary detects more symptoms and is\neffective at identifying psychiatric disorders like anxiety and depression,\noften missed by pre-defined lexicons. Conclusions: This study advances public\nhealth research by implementing a novel, systematic pipeline for curating\nsymptom lexicons from social media data. The final lexicon's high accuracy,\nvalidated by medical professionals, underscores the potential of this\nmethodology to reliably interpret and categorize vast amounts of unstructured\nsocial media data into actionable medical insights across diverse linguistic\nand regional landscapes.\n", "date": "2023-06-28", "categories": []}
{"id": "2309.13202", "title": "Investigating Large Language Models and Control Mechanisms to Improve\n  Text Readability of Biomedical Abstracts", "abstract": "  Biomedical literature often uses complex language and inaccessible\nprofessional terminologies. That is why simplification plays an important role\nin improving public health literacy. Applying Natural Language Processing (NLP)\nmodels to automate such tasks allows for quick and direct accessibility for lay\nreaders. In this work, we investigate the ability of state-of-the-art large\nlanguage models (LLMs) on the task of biomedical abstract simplification, using\nthe publicly available dataset for plain language adaptation of biomedical\nabstracts (\\textbf{PLABA}). The methods applied include domain fine-tuning and\nprompt-based learning (PBL) on: 1) Encoder-decoder models (T5, SciFive, and\nBART), 2) Decoder-only GPT models (GPT-3.5 and GPT-4) from OpenAI and BioGPT,\nand 3) Control-token mechanisms on BART-based models. We used a range of\nautomatic evaluation metrics, including BLEU, ROUGE, SARI, and BERTscore, and\nalso conducted human evaluations. BART-Large with Control Token (BART-L-w-CT)\nmechanisms reported the highest SARI score of 46.54 and T5-base reported the\nhighest BERTscore 72.62. In human evaluation, BART-L-w-CTs achieved a better\nsimplicity score over T5-Base (2.9 vs. 2.2), while T5-Base achieved a better\nmeaning preservation score over BART-L-w-CTs (3.1 vs. 2.6). We also categorised\nthe system outputs with examples, hoping this will shed some light for future\nresearch on this task. Our code, fine-tuned models, and data splits are\navailable at \\url{https://github.com/HECTA-UoM/PLABA-MU} \\begin{IEEEkeywords}\nLarge Language Models, Text Simplification, Biomedical NLP, Control Mechanisms,\nHealth Informatics \\end{IEEEkeywords}\n", "date": "2023-09-22", "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": "2212.12799", "title": "A Comprehensive Study of Gender Bias in Chemical Named Entity\n  Recognition Models", "abstract": "  Chemical named entity recognition (NER) models are used in many downstream\ntasks, from adverse drug reaction identification to pharmacoepidemiology.\nHowever, it is unknown whether these models work the same for everyone.\nPerformance disparities can potentially cause harm rather than the intended\ngood. This paper assesses gender-related performance disparities in chemical\nNER systems. We develop a framework for measuring gender bias in chemical NER\nmodels using synthetic data and a newly annotated corpus of over 92,405 words\nwith self-identified gender information from Reddit. Our evaluation of multiple\nbiomedical NER models reveals evident biases. For instance, synthetic data\nsuggests female-related names are frequently misclassified as chemicals,\nespecially for brand name mentions. Additionally, we observe performance\ndisparities between female- and male-associated data in both datasets. Many\nsystems fail to detect contraceptives such as birth control. Our findings\nemphasize the biases in chemical NER models, urging practitioners to account\nfor these biases in downstream applications.\n", "date": "2022-12-24", "categories": []}
{"id": "2402.01724", "title": "CERM: Context-aware Literature-based Discovery via Sentiment Analysis", "abstract": "  Driven by the abundance of biomedical publications, we introduce a sentiment\nanalysis task to understand food-health relationship. Prior attempts to\nincorporate health into recipe recommendation and analysis systems have\nprimarily focused on ingredient nutritional components or utilized basic\ncomputational models trained on curated labeled data. Enhanced models that\ncapture the inherent relationship between food ingredients and biomedical\nconcepts can be more beneficial for food-related research, given the wealth of\ninformation in biomedical texts. Considering the costly data labeling process,\nthese models should effectively utilize both labeled and unlabeled data. This\npaper introduces Entity Relationship Sentiment Analysis (ERSA), a new task that\ncaptures the sentiment of a text based on an entity pair. ERSA extends the\nwidely studied Aspect Based Sentiment Analysis (ABSA) task. Specifically, our\nstudy concentrates on the ERSA task applied to biomedical texts, focusing on\n(entity-entity) pairs of biomedical and food concepts. ERSA poses a significant\nchallenge compared to traditional sentiment analysis tasks, as sentence\nsentiment may not align with entity relationship sentiment. Additionally, we\npropose CERM, a semi-supervised architecture that combines different word\nembeddings to enhance the encoding of the ERSA task. Experimental results\nshowcase the model's efficiency across diverse learning scenarios.\n", "date": "2024-01-27", "categories": []}
{"id": "2403.08377", "title": "Learning to Describe for Predicting Zero-shot Drug-Drug Interactions", "abstract": "  Adverse drug-drug interactions~(DDIs) can compromise the effectiveness of\nconcurrent drug administration, posing a significant challenge in healthcare.\nAs the development of new drugs continues, the potential for unknown adverse\neffects resulting from DDIs becomes a growing concern. Traditional\ncomputational methods for DDI prediction may fail to capture interactions for\nnew drugs due to the lack of knowledge. In this paper, we introduce a new\nproblem setup as zero-shot DDI prediction that deals with the case of new\ndrugs. Leveraging textual information from online databases like DrugBank and\nPubChem, we propose an innovative approach TextDDI with a language model-based\nDDI predictor and a reinforcement learning~(RL)-based information selector,\nenabling the selection of concise and pertinent text for accurate DDI\nprediction on new drugs. Empirical results show the benefits of the proposed\napproach on several settings including zero-shot and few-shot DDI prediction,\nand the selected texts are semantically relevant. Our code and data are\navailable at \\url{https://github.com/zhufq00/DDIs-Prediction}.\n", "date": "2024-03-13", "categories": []}
{"id": "2403.05602", "title": "Extracting Protein-Protein Interactions (PPIs) from Biomedical\n  Literature using Attention-based Relational Context Information", "abstract": "  Because protein-protein interactions (PPIs) are crucial to understand living\nsystems, harvesting these data is essential to probe disease development and\ndiscern gene/protein functions and biological processes. Some curated datasets\ncontain PPI data derived from the literature and other sources (e.g., IntAct,\nBioGrid, DIP, and HPRD). However, they are far from exhaustive, and their\nmaintenance is a labor-intensive process. On the other hand, machine learning\nmethods to automate PPI knowledge extraction from the scientific literature\nhave been limited by a shortage of appropriate annotated data. This work\npresents a unified, multi-source PPI corpora with vetted interaction\ndefinitions augmented by binary interaction type labels and a Transformer-based\ndeep learning method that exploits entities' relational context information for\nrelation representation to improve relation classification performance. The\nmodel's performance is evaluated on four widely studied biomedical relation\nextraction datasets, as well as this work's target PPI datasets, to observe the\neffectiveness of the representation to relation extraction tasks in various\ndata. Results show the model outperforms prior state-of-the-art models. The\ncode and data are available at:\nhttps://github.com/BNLNLP/PPI-Relation-Extraction\n", "date": "2024-03-08", "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": "2401.11389", "title": "MedLM: Exploring Language Models for Medical Question Answering Systems", "abstract": "  In the face of rapidly expanding online medical literature, automated systems\nfor aggregating and summarizing information are becoming increasingly crucial\nfor healthcare professionals and patients. Large Language Models (LLMs), with\ntheir advanced generative capabilities, have shown promise in various NLP\ntasks, and their potential in the healthcare domain, particularly for\nClosed-Book Generative QnA, is significant. However, the performance of these\nmodels in domain-specific tasks such as medical Q&A remains largely unexplored.\nThis study aims to fill this gap by comparing the performance of general and\nmedical-specific distilled LMs for medical Q&A. We aim to evaluate the\neffectiveness of fine-tuning domain-specific LMs and compare the performance of\ndifferent families of Language Models. The study will address critical\nquestions about these models' reliability, comparative performance, and\neffectiveness in the context of medical Q&A. The findings will provide valuable\ninsights into the suitability of different LMs for specific applications in the\nmedical domain.\n", "date": "2024-01-21", "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": "2402.18046", "title": "Data augmentation method for modeling health records with applications\n  to clopidogrel treatment failure detection", "abstract": "  We present a novel data augmentation method to address the challenge of data\nscarcity in modeling longitudinal patterns in Electronic Health Records (EHR)\nof patients using natural language processing (NLP) algorithms. The proposed\nmethod generates augmented data by rearranging the orders of medical records\nwithin a visit where the order of elements are not obvious, if any. Applying\nthe proposed method to the clopidogrel treatment failure detection task enabled\nup to 5.3% absolute improvement in terms of ROC-AUC (from 0.908 without\naugmentation to 0.961 with augmentation) when it was used during the\npre-training procedure. It was also shown that the augmentation helped to\nimprove performance during fine-tuning procedures, especially when the amount\nof labeled training data is limited.\n", "date": "2024-02-28", "categories": []}
{"id": "2402.15700", "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": "2402.16038", "title": "Deep Learning Approaches for Improving Question Answering Systems in\n  Hepatocellular Carcinoma Research", "abstract": "  In recent years, advancements in natural language processing (NLP) have been\nfueled by deep learning techniques, particularly through the utilization of\npowerful computing resources like GPUs and TPUs. Models such as BERT and GPT-3,\ntrained on vast amounts of data, have revolutionized language understanding and\ngeneration. These pre-trained models serve as robust bases for various tasks\nincluding semantic understanding, intelligent writing, and reasoning, paving\nthe way for a more generalized form of artificial intelligence. NLP, as a vital\napplication of AI, aims to bridge the gap between humans and computers through\nnatural language interaction. This paper delves into the current landscape and\nfuture prospects of large-scale model-based NLP, focusing on the\nquestion-answering systems within this domain. Practical cases and developments\nin artificial intelligence-driven question-answering systems are analyzed to\nfoster further exploration and research in the realm of large-scale NLP.\n", "date": "2024-02-25", "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": "2402.13498", "title": "The Lay Person's Guide to Biomedicine: Orchestrating Large Language\n  Models", "abstract": "  Automated lay summarisation (LS) aims to simplify complex technical documents\ninto a more accessible format to non-experts. Existing approaches using\npre-trained language models, possibly augmented with external background\nknowledge, tend to struggle with effective simplification and explanation.\nMoreover, automated methods that can effectively assess the `layness' of\ngenerated summaries are lacking. Recently, large language models (LLMs) have\ndemonstrated a remarkable capacity for text simplification, background\ninformation generation, and text evaluation. This has motivated our systematic\nexploration into using LLMs to generate and evaluate lay summaries of\nbiomedical articles. We propose a novel \\textit{Explain-then-Summarise} LS\nframework, which leverages LLMs to generate high-quality background knowledge\nto improve supervised LS. We also evaluate the performance of LLMs for\nzero-shot LS and propose two novel LLM-based LS evaluation metrics, which\nassess layness from multiple perspectives. Finally, we conduct a human\nassessment of generated lay summaries. Our experiments reveal that\nLLM-generated background information can support improved supervised LS.\nFurthermore, our novel zero-shot LS evaluation metric demonstrates a high\ndegree of alignment with human preferences. We conclude that LLMs have an\nimportant part to play in improving both the performance and evaluation of LS\nmethods.\n", "date": "2024-02-21", "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": "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": "2402.10951", "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": "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": "2307.00933", "title": "Data-Driven Information Extraction and Enrichment of Molecular Profiling\n  Data for Cancer Cell Lines", "abstract": "  With the proliferation of research means and computational methodologies,\npublished biomedical literature is growing exponentially in numbers and volume.\nCancer cell lines are frequently used models in biological and medical research\nthat are currently applied for a wide range of purposes, from studies of\ncellular mechanisms to drug development, which has led to a wealth of related\ndata and publications. Sifting through large quantities of text to gather\nrelevant information on the cell lines of interest is tedious and extremely\nslow when performed by humans. Hence, novel computational information\nextraction and correlation mechanisms are required to boost meaningful\nknowledge extraction. In this work, we present the design, implementation and\napplication of a novel data extraction and exploration system. This system\nextracts deep semantic relations between textual entities from scientific\nliterature to enrich existing structured clinical data in the domain of cancer\ncell lines. We introduce a new public data exploration portal, which enables\nautomatic linking of genomic copy number variants plots with ranked, related\nentities such as affected genes. Each relation is accompanied by\nliterature-derived evidences, allowing for deep, yet rapid, literature search,\nusing existing structured data as a springboard. Our system is publicly\navailable on the web at https://cancercelllines.org\n", "date": "2023-07-03", "categories": []}
{"id": "2402.06675", "title": "A Masked language model for multi-source EHR trajectories contextual\n  representation learning", "abstract": "  Using electronic health records data and machine learning to guide future\ndecisions needs to address challenges, including 1) long/short-term\ndependencies and 2) interactions between diseases and interventions.\nBidirectional transformers have effectively addressed the first challenge. Here\nwe tackled the latter challenge by masking one source (e.g., ICD10 codes) and\ntraining the transformer to predict it using other sources (e.g., ATC codes).\n", "date": "2024-02-07", "categories": []}
{"id": "2402.07023", "title": "Gemini Goes to Med School: Exploring the Capabilities of Multimodal\n  Large Language Models on Medical Challenge Problems & Hallucinations", "abstract": "  Large language models have the potential to be valuable in the healthcare\nindustry, but it's crucial to verify their safety and effectiveness through\nrigorous evaluation. For this purpose, we comprehensively evaluated both\nopen-source LLMs and Google's new multimodal LLM called Gemini across Medical\nreasoning, hallucination detection, and Medical Visual Question Answering\ntasks. While Gemini showed competence, it lagged behind state-of-the-art models\nlike MedPaLM 2 and GPT-4 in diagnostic accuracy. Additionally, Gemini achieved\nan accuracy of 61.45\\% on the medical VQA dataset, significantly lower than\nGPT-4V's score of 88\\%. Our analysis revealed that Gemini is highly susceptible\nto hallucinations, overconfidence, and knowledge gaps, which indicate risks if\ndeployed uncritically. We also performed a detailed analysis by medical subject\nand test type, providing actionable feedback for developers and clinicians. To\nmitigate risks, we applied prompting strategies that improved performance.\nAdditionally, we facilitated future research and development by releasing a\nPython module for medical LLM evaluation and establishing a dedicated\nleaderboard on Hugging Face for medical domain LLMs. Python module can be found\nat https://github.com/promptslab/RosettaEval\n", "date": "2024-02-10", "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": "2402.03597", "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": "2402.04075", "title": "Iterative Prompt Refinement for Radiation Oncology Symptom Extraction\n  Using Teacher-Student Large Language Models", "abstract": "  This study introduces a novel teacher-student architecture utilizing Large\nLanguage Models (LLMs) to improve prostate cancer radiotherapy symptom\nextraction from clinical notes. Mixtral, the student model, initially extracts\nsymptoms, followed by GPT-4, the teacher model, which refines prompts based on\nMixtral's performance. This iterative process involved 294 single symptom\nclinical notes across 12 symptoms, with up to 16 rounds of refinement per\nepoch. Results showed significant improvements in extracting symptoms from both\nsingle and multi-symptom notes. For 59 single symptom notes, accuracy increased\nfrom 0.51 to 0.71, precision from 0.52 to 0.82, recall from 0.52 to 0.72, and\nF1 score from 0.49 to 0.73. In 375 multi-symptom notes, accuracy rose from 0.24\nto 0.43, precision from 0.6 to 0.76, recall from 0.24 to 0.43, and F1 score\nfrom 0.20 to 0.44. These results demonstrate the effectiveness of advanced\nprompt engineering in LLMs for radiation oncology use.\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": "2402.01826", "title": "Leveraging Large Language Models for Analyzing Blood Pressure Variations\n  Across Biological Sex from Scientific Literature", "abstract": "  Hypertension, defined as blood pressure (BP) that is above normal, holds\nparamount significance in the realm of public health, as it serves as a\ncritical precursor to various cardiovascular diseases (CVDs) and significantly\ncontributes to elevated mortality rates worldwide. However, many existing BP\nmeasurement technologies and standards might be biased because they do not\nconsider clinical outcomes, comorbidities, or demographic factors, making them\ninconclusive for diagnostic purposes. There is limited data-driven research\nfocused on studying the variance in BP measurements across these variables. In\nthis work, we employed GPT-35-turbo, a large language model (LLM), to\nautomatically extract the mean and standard deviation values of BP for both\nmales and females from a dataset comprising 25 million abstracts sourced from\nPubMed. 993 article abstracts met our predefined inclusion criteria (i.e.,\npresence of references to blood pressure, units of blood pressure such as mmHg,\nand mention of biological sex). Based on the automatically-extracted\ninformation from these articles, we conducted an analysis of the variations of\nBP values across biological sex. Our results showed the viability of utilizing\nLLMs to study the BP variations across different demographic factors.\n", "date": "2024-02-02", "categories": []}
{"id": "2402.01916", "title": "CoLe and LYS at BioASQ MESINESP8 Task: similarity based descriptor\n  assignment in Spanish", "abstract": "  In this paper, we describe our participation in the MESINESP Task of the\nBioASQ biomedical semantic indexing challenge. The participating system follows\nan approach based solely on conventional information retrieval tools. We have\nevaluated various alternatives for extracting index terms from IBECS/LILACS\ndocuments in order to be stored in an Apache Lucene index. Those indexed\nrepresentations are queried using the contents of the article to be annotated\nand a ranked list of candidate labels is created from the retrieved documents.\nWe also have evaluated a sort of limited Label Powerset approach which creates\nmeta-labels joining pairs of DeCS labels with high co-occurrence scores, and an\nalternative method based on label profile matching. Results obtained in\nofficial runs seem to confirm the suitability of this approach for languages\nlike Spanish.\n", "date": "2024-02-02", "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": "2402.03172", "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": "2402.01360", "title": "What Makes Medical Claims (Un)Verifiable? Analyzing Entity and Relation\n  Properties for Fact Verification", "abstract": "  Biomedical claim verification fails if no evidence can be discovered. In\nthese cases, the fact-checking verdict remains unknown and the claim is\nunverifiable. To improve upon this, we have to understand if there are any\nclaim properties that impact its verifiability. In this work we assume that\nentities and relations define the core variables in a biomedical claim's\nanatomy and analyze if their properties help us to differentiate verifiable\nfrom unverifiable claims. In a study with trained annotation experts we prompt\nthem to find evidence for biomedical claims, and observe how they refine search\nqueries for their evidence search. This leads to the first corpus for\nscientific fact verification annotated with subject-relation-object triplets,\nevidence documents, and fact-checking verdicts (the BEAR-Fact corpus). We find\n(1) that discovering evidence for negated claims (e.g., X-does-not-cause-Y) is\nparticularly challenging. Further, we see that annotators process queries\nmostly by adding constraints to the search and by normalizing entities to\ncanonical names. (2) We compare our in-house annotations with a small\ncrowdsourcing setting where we employ medical experts and laypeople. We find\nthat domain expertise does not have a substantial effect on the reliability of\nannotations. Finally, (3), we demonstrate that it is possible to reliably\nestimate the success of evidence retrieval purely from the claim text~(.82\\F),\nwhereas identifying unverifiable claims proves more challenging (.27\\F). The\ndataset is available at http://www.ims.uni-stuttgart.de/data/bioclaim.\n", "date": "2024-02-02", "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": "2310.07276", "title": "BioT5: Enriching Cross-modal Integration in Biology with Chemical\n  Knowledge and Natural Language Associations", "abstract": "  Recent advancements in biological research leverage the integration of\nmolecules, proteins, and natural language to enhance drug discovery. However,\ncurrent models exhibit several limitations, such as the generation of invalid\nmolecular SMILES, underutilization of contextual information, and equal\ntreatment of structured and unstructured knowledge. To address these issues, we\npropose $\\mathbf{BioT5}$, a comprehensive pre-training framework that enriches\ncross-modal integration in biology with chemical knowledge and natural language\nassociations. $\\mathbf{BioT5}$ utilizes SELFIES for $100%$ robust molecular\nrepresentations and extracts knowledge from the surrounding context of\nbio-entities in unstructured biological literature. Furthermore,\n$\\mathbf{BioT5}$ distinguishes between structured and unstructured knowledge,\nleading to more effective utilization of information. After fine-tuning, BioT5\nshows superior performance across a wide range of tasks, demonstrating its\nstrong capability of capturing underlying relations and properties of\nbio-entities. Our code is available at\n$\\href{https://github.com/QizhiPei/BioT5}{Github}$.\n", "date": "2023-10-11", "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": "2401.12994", "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": "2401.07510", "title": "Developing ChatGPT for Biology and Medicine: A Complete Review of\n  Biomedical Question Answering", "abstract": "  ChatGPT explores a strategic blueprint of question answering (QA) in\ndelivering medical diagnosis, treatment recommendations, and other healthcare\nsupport. This is achieved through the increasing incorporation of medical\ndomain data via natural language processing (NLP) and multimodal paradigms. By\ntransitioning the distribution of text, images, videos, and other modalities\nfrom the general domain to the medical domain, these techniques have expedited\nthe progress of medical domain question answering (MDQA). They bridge the gap\nbetween human natural language and sophisticated medical domain knowledge or\nexpert manual annotations, handling large-scale, diverse, unbalanced, or even\nunlabeled data analysis scenarios in medical contexts. Central to our focus is\nthe utilizing of language models and multimodal paradigms for medical question\nanswering, aiming to guide the research community in selecting appropriate\nmechanisms for their specific medical research requirements. Specialized tasks\nsuch as unimodal-related question answering, reading comprehension, reasoning,\ndiagnosis, relation extraction, probability modeling, and others, as well as\nmultimodal-related tasks like vision question answering, image caption,\ncross-modal retrieval, report summarization, and generation, are discussed in\ndetail. Each section delves into the intricate specifics of the respective\nmethod under consideration. This paper highlights the structures and\nadvancements of medical domain explorations against general domain methods,\nemphasizing their applications across different tasks and datasets. It also\noutlines current challenges and opportunities for future medical domain\nresearch, paving the way for continued innovation and application in this\nrapidly evolving field.\n", "date": "2024-01-15", "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": "2401.11048", "title": "PubTator 3.0: an AI-powered Literature Resource for Unlocking Biomedical\n  Knowledge", "abstract": "  PubTator 3.0 (https://www.ncbi.nlm.nih.gov/research/pubtator3/) is a\nbiomedical literature resource using state-of-the-art AI techniques to offer\nsemantic and relation searches for key concepts like proteins, genetic\nvariants, diseases, and chemicals. It currently provides over one billion\nentity and relation annotations across approximately 36 million PubMed\nabstracts and 6 million full-text articles from the PMC open access subset,\nupdated weekly. PubTator 3.0's online interface and API utilize these\nprecomputed entity relations and synonyms to provide advanced search\ncapabilities and enable large-scale analyses, streamlining many complex\ninformation needs. We showcase the retrieval quality of PubTator 3.0 using a\nseries of entity pair queries, demonstrating that PubTator 3.0 retrieves a\ngreater number of articles than either PubMed or Google Scholar, with higher\nprecision in the top 20 results. We further show that integrating ChatGPT\n(GPT-4) with PubTator APIs dramatically improves the factuality and\nverifiability of its responses. In summary, PubTator 3.0 offers a comprehensive\nset of features and tools that allow researchers to navigate the ever-expanding\nwealth of biomedical literature, expediting research and unlocking valuable\ninsights for scientific discovery.\n", "date": "2024-01-19", "categories": []}
{"id": "2311.13274", "title": "Enhancing Summarization Performance through Transformer-Based Prompt\n  Engineering in Automated Medical Reporting", "abstract": "  Customized medical prompts enable Large Language Models (LLM) to effectively\naddress medical dialogue summarization. The process of medical reporting is\noften time-consuming for healthcare professionals. Implementing medical\ndialogue summarization techniques presents a viable solution to alleviate this\ntime constraint by generating automated medical reports. The effectiveness of\nLLMs in this process is significantly influenced by the formulation of the\nprompt, which plays a crucial role in determining the quality and relevance of\nthe generated reports. In this research, we used a combination of two distinct\nprompting strategies, known as shot prompting and pattern prompting to enhance\nthe performance of automated medical reporting. The evaluation of the automated\nmedical reports is carried out using the ROUGE score and a human evaluation\nwith the help of an expert panel. The two-shot prompting approach in\ncombination with scope and domain context outperforms other methods and\nachieves the highest score when compared to the human reference set by a\ngeneral practitioner. However, the automated reports are approximately twice as\nlong as the human references, due to the addition of both redundant and\nrelevant statements that are added to the report.\n", "date": "2023-11-22", "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": "2306.05323", "title": "Advancing Italian Biomedical Information Extraction with\n  Transformers-based Models: Methodological Insights and Multicenter Practical\n  Application", "abstract": "  The introduction of computerized medical records in hospitals has reduced\nburdensome activities like manual writing and information fetching. However,\nthe data contained in medical records are still far underutilized, primarily\nbecause extracting data from unstructured textual medical records takes time\nand effort. Information Extraction, a subfield of Natural Language Processing,\ncan help clinical practitioners overcome this limitation by using automated\ntext-mining pipelines. In this work, we created the first Italian\nneuropsychiatric Named Entity Recognition dataset, PsyNIT, and used it to\ndevelop a Transformers-based model. Moreover, we collected and leveraged three\nexternal independent datasets to implement an effective multicenter model, with\noverall F1-score 84.77%, Precision 83.16%, Recall 86.44%. The lessons learned\nare: (i) the crucial role of a consistent annotation process and (ii) a\nfine-tuning strategy that combines classical methods with a \"low-resource\"\napproach. This allowed us to establish methodological guidelines that pave the\nway for Natural Language Processing studies in less-resourced languages.\n", "date": "2023-06-08", "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": "2401.07447", "title": "Taec: a Manually annotated text dataset for trait and phenotype\n  extraction and entity linking in wheat breeding literature", "abstract": "  Wheat varieties show a large diversity of traits and phenotypes. Linking them\nto genetic variability is essential for shorter and more efficient wheat\nbreeding programs. Newly desirable wheat variety traits include disease\nresistance to reduce pesticide use, adaptation to climate change, resistance to\nheat and drought stresses, or low gluten content of grains. Wheat breeding\nexperiments are documented by a large body of scientific literature and\nobservational data obtained in-field and under controlled conditions. The\ncross-referencing of complementary information from the literature and\nobservational data is essential to the study of the genotype-phenotype\nrelationship and to the improvement of wheat selection. The scientific\nliterature on genetic marker-assisted selection describes much information\nabout the genotype-phenotype relationship. However, the variety of expressions\nused to refer to traits and phenotype values in scientific articles is a hinder\nto finding information and cross-referencing it. When trained adequately by\nannotated examples, recent text mining methods perform highly in named entity\nrecognition and linking in the scientific domain. While several corpora contain\nannotations of human and animal phenotypes, currently, no corpus is available\nfor training and evaluating named entity recognition and entity-linking methods\nin plant phenotype literature. The Triticum aestivum trait Corpus is a new gold\nstandard for traits and phenotypes of wheat. It consists of 540 PubMed\nreferences fully annotated for trait, phenotype, and species named entities\nusing the Wheat Trait and Phenotype Ontology and the species taxonomy of the\nNational Center for Biotechnology Information. A study of the performance of\ntools trained on the Triticum aestivum trait Corpus shows that the corpus is\nsuitable for the training and evaluation of named entity recognition and\nlinking.\n", "date": "2024-01-15", "categories": []}
{"id": "2401.07877", "title": "EMBRE: Entity-aware Masking for Biomedical Relation Extraction", "abstract": "  Information extraction techniques, including named entity recognition (NER)\nand relation extraction (RE), are crucial in many domains to support making\nsense of vast amounts of unstructured text data by identifying and connecting\nrelevant information. Such techniques can assist researchers in extracting\nvaluable insights. In this paper, we introduce the Entity-aware Masking for\nBiomedical Relation Extraction (EMBRE) method for biomedical relation\nextraction, as applied in the context of the BioRED challenge Task 1, in which\nhuman-annotated entities are provided as input. Specifically, we integrate\nentity knowledge into a deep neural network by pretraining the backbone model\nwith an entity masking objective. We randomly mask named entities for each\ninstance and let the model identify the masked entity along with its type. In\nthis way, the model is capable of learning more specific knowledge and more\nrobust representations. Then, we utilize the pre-trained model as our backbone\nto encode language representations and feed these representations into two\nmultilayer perceptron (MLPs) to predict the logits for relation and novelty,\nrespectively. The experimental results demonstrate that our proposed method can\nimprove the performances of entity pair, relation and novelty extraction over\nour baseline.\n", "date": "2024-01-15", "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": "2401.05125", "title": "BELHD: Improving Biomedical Entity Linking with Homonoym Disambiguation", "abstract": "  Biomedical entity linking (BEL) is the task of grounding entity mentions to a\nknowledge base (KB). A popular approach to the task are name-based methods,\ni.e. those identifying the most appropriate name in the KB for a given mention,\neither via dense retrieval or autoregressive modeling. However, as these\nmethods directly return KB names, they cannot cope with homonyms, i.e.\ndifferent KB entities sharing the exact same name. This significantly affects\ntheir performance, especially for KBs where homonyms account for a large amount\nof entity mentions (e.g. UMLS and NCBI Gene). We therefore present BELHD\n(Biomedical Entity Linking with Homonym Disambiguation), a new name-based\nmethod that copes with this challenge. Specifically, BELHD builds upon the\nBioSyn (Sung et al.,2020) model introducing two crucial extensions. First, it\nperforms a preprocessing of the KB in which it expands homonyms with an\nautomatically chosen disambiguating string, thus enforcing unique linking\ndecisions. Second, we introduce candidate sharing, a novel strategy to select\ncandidates for contrastive learning that enhances the overall training signal.\nExperiments with 10 corpora and five entity types show that BELHD improves upon\nstate-of-the-art approaches, achieving the best results in 6 out 10 corpora\nwith an average improvement of 4.55pp recall@1. Furthermore, the KB\npreprocessing is orthogonal to the core prediction model and thus can also\nimprove other methods, which we exemplify for GenBioEL (Yuan et al, 2022), a\ngenerative name-based BEL approach. Code is available at: link added upon\npublication.\n", "date": "2024-01-10", "categories": []}
{"id": "2312.01040", "title": "From Beginner to Expert: Modeling Medical Knowledge into General LLMs", "abstract": "  Recently, large language model (LLM) based artificial intelligence (AI)\nsystems have demonstrated remarkable capabilities in natural language\nunderstanding and generation. However, these models face a significant\nchallenge when it comes to sensitive applications, such as reasoning over\nmedical knowledge and answering medical questions in a physician-like manner.\nPrior studies attempted to overcome this challenge by increasing the model size\n(>100B) to learn more general medical knowledge, while there is still room for\nimprovement in LLMs with smaller-scale model sizes (<100B). In this work, we\nstart from a pre-trained general LLM model (AntGLM-10B) and fine-tune it from a\nmedical beginner towards a medical expert (called AntGLM-Med-10B), which\nleverages a 3-stage optimization procedure, i.e., general medical knowledge\ninjection, medical domain instruction tuning, and specific medical task\nadaptation. Our contributions are threefold: (1) We specifically investigate\nhow to adapt a pre-trained general LLM in medical domain, especially for a\nspecific medical task. (2) We collect and construct large-scale medical\ndatasets for each stage of the optimization process. These datasets encompass\nvarious data types and tasks, such as question-answering, medical reasoning,\nmulti-choice questions, and medical conversations. (3) Specifically for\nmulti-choice questions in the medical domain, we propose a novel\nVerification-of-Choice approach for prompting engineering, which significantly\nenhances the reasoning ability of LLMs. Remarkably, by combining the above\napproaches, our AntGLM-Med-10B model can outperform the most of LLMs on\nPubMedQA, including both general and medical LLMs, even when these LLMs have\nlarger model size.\n", "date": "2023-12-02", "categories": []}
{"id": "2401.02034", "title": "Text2MDT: Extracting Medical Decision Trees from Medical Texts", "abstract": "  Knowledge of the medical decision process, which can be modeled as medical\ndecision trees (MDTs), is critical to build clinical decision support systems.\nHowever, the current MDT construction methods rely heavily on time-consuming\nand laborious manual annotation. In this work, we propose a novel task,\nText2MDT, to explore the automatic extraction of MDTs from medical texts such\nas medical guidelines and textbooks. We normalize the form of the MDT and\ncreate an annotated Text-to-MDT dataset in Chinese with the participation of\nmedical experts. We investigate two different methods for the Text2MDT tasks:\n(a) an end-to-end framework which only relies on a GPT style large language\nmodels (LLM) instruction tuning to generate all the node information and tree\nstructures. (b) The pipeline framework which decomposes the Text2MDT task to\nthree subtasks. Experiments on our Text2MDT dataset demonstrate that: (a) the\nend-to-end method basd on LLMs (7B parameters or larger) show promising\nresults, and successfully outperform the pipeline methods. (b) The\nchain-of-thought (COT) prompting method \\cite{Wei2022ChainOT} can improve the\nperformance of the fine-tuned LLMs on the Text2MDT test set. (c) the\nlightweight pipelined method based on encoder-based pretrained models can\nperform comparably with LLMs with model complexity two magnititudes smaller.\nOur Text2MDT dataset is open-sourced at\n\\url{https://tianchi.aliyun.com/dataset/95414}, and the source codes are\nopen-sourced at \\url{https://github.com/michael-wzhu/text2dt}.\n", "date": "2024-01-04", "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": "2401.01469", "title": "Question-Answering Based Summarization of Electronic Health Records\n  using Retrieval Augmented Generation", "abstract": "  Summarization of electronic health records (EHRs) can substantially minimize\n'screen time' for both patients as well as medical personnel. In recent years\nsummarization of EHRs have employed machine learning pipelines using state of\nthe art neural models. However, these models have produced less than adequate\nresults that are attributed to the difficulty of obtaining sufficient annotated\ndata for training. Moreover, the requirement to consider the entire content of\nan EHR in summarization has resulted in poor performance due to the fact that\nattention mechanisms in modern large language models (LLMs) adds a quadratic\ncomplexity in terms of the size of the input. We propose here a method that\nmitigates these shortcomings by combining semantic search, retrieval augmented\ngeneration (RAG) and question-answering using the latest LLMs. In our approach\nsummarization is the extraction of answers to specific questions that are\ndeemed important by subject-matter experts (SMEs). Our approach is quite\nefficient; requires minimal to no training; does not suffer from the\n'hallucination' problem of LLMs; and it ensures diversity, since the summary\nwill not have repeated content but diverse answers to specific questions.\n", "date": "2024-01-03", "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": "2309.01398", "title": "Zero-shot information extraction from radiological reports using ChatGPT", "abstract": "  Electronic health records contain an enormous amount of valuable information,\nbut many are recorded in free text. Information extraction is the strategy to\ntransform the sequence of characters into structured data, which can be\nemployed for secondary analysis. However, the traditional information\nextraction components, such as named entity recognition and relation\nextraction, require annotated data to optimize the model parameters, which has\nbecome one of the major bottlenecks in building information extraction systems.\nWith the large language models achieving good performances on various\ndownstream NLP tasks without parameter tuning, it becomes possible to use large\nlanguage models for zero-shot information extraction. In this study, we aim to\nexplore whether the most popular large language model, ChatGPT, can extract\nuseful information from the radiological reports. We first design the prompt\ntemplate for the interested information in the CT reports. Then, we generate\nthe prompts by combining the prompt template with the CT reports as the inputs\nof ChatGPT to obtain the responses. A post-processing module is developed to\ntransform the responses into structured extraction results. We conducted the\nexperiments with 847 CT reports collected from Peking University Cancer\nHospital. The experimental results indicate that ChatGPT can achieve\ncompetitive performances for some extraction tasks compared with the baseline\ninformation extraction system, but some limitations need to be further\nimproved.\n", "date": "2023-09-04", "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": "2312.15550", "title": "Multi-level biomedical NER through multi-granularity embeddings and\n  enhanced labeling", "abstract": "  Biomedical Named Entity Recognition (NER) is a fundamental task of Biomedical\nNatural Language Processing for extracting relevant information from biomedical\ntexts, such as clinical records, scientific publications, and electronic health\nrecords. The conventional approaches for biomedical NER mainly use traditional\nmachine learning techniques, such as Conditional Random Fields and Support\nVector Machines or deep learning-based models like Recurrent Neural Networks\nand Convolutional Neural Networks. Recently, Transformer-based models,\nincluding BERT, have been used in the domain of biomedical NER and have\ndemonstrated remarkable results. However, these models are often based on\nword-level embeddings, limiting their ability to capture character-level\ninformation, which is effective in biomedical NER due to the high variability\nand complexity of biomedical texts. To address these limitations, this paper\nproposes a hybrid approach that integrates the strengths of multiple models. In\nthis paper, we proposed an approach that leverages fine-tuned BERT to provide\ncontextualized word embeddings, a pre-trained multi-channel CNN for\ncharacter-level information capture, and following by a BiLSTM + CRF for\nsequence labelling and modelling dependencies between the words in the text. In\naddition, also we propose an enhanced labelling method as part of\npre-processing to enhance the identification of the entity's beginning word and\nthus improve the identification of multi-word entities, a common challenge in\nbiomedical NER. By integrating these models and the pre-processing method, our\nproposed model effectively captures both contextual information and detailed\ncharacter-level information. We evaluated our model on the benchmark i2b2/2010\ndataset, achieving an F1-score of 90.11. These results illustrate the\nproficiency of our proposed model in performing biomedical Named Entity\nRecognition.\n", "date": "2023-12-24", "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": "2312.13881", "title": "Diversifying Knowledge Enhancement of Biomedical Language Models using\n  Adapter Modules and Knowledge Graphs", "abstract": "  Recent advances in natural language processing (NLP) owe their success to\npre-training language models on large amounts of unstructured data. Still,\nthere is an increasing effort to combine the unstructured nature of LMs with\nstructured knowledge and reasoning. Particularly in the rapidly evolving field\nof biomedical NLP, knowledge-enhanced language models (KELMs) have emerged as\npromising tools to bridge the gap between large language models and\ndomain-specific knowledge, considering the available biomedical knowledge\ngraphs (KGs) curated by experts over the decades. In this paper, we develop an\napproach that uses lightweight adapter modules to inject structured biomedical\nknowledge into pre-trained language models (PLMs). We use two large KGs, the\nbiomedical knowledge system UMLS and the novel biochemical ontology OntoChem,\nwith two prominent biomedical PLMs, PubMedBERT and BioLinkBERT. The approach\nincludes partitioning knowledge graphs into smaller subgraphs, fine-tuning\nadapter modules for each subgraph, and combining the knowledge in a fusion\nlayer. We test the performance on three downstream tasks: document\nclassification,question answering, and natural language inference. We show that\nour methodology leads to performance improvements in several instances while\nkeeping requirements in computing power low. Finally, we provide a detailed\ninterpretation of the results and report valuable insights for future work.\n", "date": "2023-12-21", "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": "2312.12989", "title": "Benchmarking and Analyzing In-context Learning, Fine-tuning and\n  Supervised Learning for Biomedical Knowledge Curation: a focused study on\n  chemical entities of biological interest", "abstract": "  Automated knowledge curation for biomedical ontologies is key to ensure that\nthey remain comprehensive, high-quality and up-to-date. In the era of\nfoundational language models, this study compares and analyzes three NLP\nparadigms for curation tasks: in-context learning (ICL), fine-tuning (FT), and\nsupervised learning (ML). Using the Chemical Entities of Biological Interest\n(ChEBI) database as a model ontology, three curation tasks were devised. For\nICL, three prompting strategies were employed with GPT-4, GPT-3.5, BioGPT.\nPubmedBERT was chosen for the FT paradigm. For ML, six embedding models were\nutilized for training Random Forest and Long-Short Term Memory models. Five\nsetups were designed to assess ML and FT model performance across different\ndata availability scenarios.Datasets for curation tasks included: task 1\n(620,386), task 2 (611,430), and task 3 (617,381), maintaining a 50:50 positive\nversus negative ratio. For ICL models, GPT-4 achieved best accuracy scores of\n0.916, 0.766 and 0.874 for tasks 1-3 respectively. In a direct comparison, ML\n(trained on ~260,000 triples) outperformed ICL in accuracy across all tasks.\n(accuracy differences: +.11, +.22 and +.17). Fine-tuned PubmedBERT performed\nsimilarly to leading ML models in tasks 1 & 2 (F1 differences: -.014 and\n+.002), but worse in task 3 (-.048). Simulations revealed performance declines\nin both ML and FT models with smaller and higher imbalanced training data.\nwhere ICL (particularly GPT-4) excelled in tasks 1 & 3. GPT-4 excelled in tasks\n1 and 3 with less than 6,000 triples, surpassing ML/FT. ICL underperformed\nML/FT in task 2.ICL-augmented foundation models can be good assistants for\nknowledge curation with correct prompting, however, not making ML and FT\nparadigms obsolete. The latter two require task-specific data to beat ICL. In\nsuch cases, ML relies on small pretrained embeddings, minimizing computational\ndemands.\n", "date": "2023-12-20", "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": "2312.10652", "title": "Explorers at #SMM4H 2023: Enhancing BERT for Health Applications through\n  Knowledge and Model Fusion", "abstract": "  An increasing number of individuals are willing to post states and opinions\nin social media, which has become a valuable data resource for studying human\nhealth. Furthermore, social media has been a crucial research point for\nhealthcare now. This paper outlines the methods in our participation in the\n#SMM4H 2023 Shared Tasks, including data preprocessing, continual pre-training\nand fine-tuned optimization strategies. Especially for the Named Entity\nRecognition (NER) task, we utilize the model architecture named W2NER that\neffectively enhances the model generalization ability. Our method achieved\nfirst place in the Task 3. This paper has been peer-reviewed and accepted for\npresentation at the #SMM4H 2023 Workshop.\n", "date": "2023-12-17", "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": "2312.09806", "title": "Improving Biomedical Entity Linking with Retrieval-enhanced Learning", "abstract": "  Biomedical entity linking (BioEL) has achieved remarkable progress with the\nhelp of pre-trained language models. However, existing BioEL methods usually\nstruggle to handle rare and difficult entities due to long-tailed distribution.\nTo address this limitation, we introduce a new scheme $k$NN-BioEL, which\nprovides a BioEL model with the ability to reference similar instances from the\nentire training corpus as clues for prediction, thus improving the\ngeneralization capabilities. Moreover, we design a contrastive learning\nobjective with dynamic hard negative sampling (DHNS) that improves the quality\nof the retrieved neighbors during inference. Extensive experimental results\nshow that $k$NN-BioEL outperforms state-of-the-art baselines on several\ndatasets.\n", "date": "2023-12-15", "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": "2303.17728", "title": "Evaluation of GPT and BERT-based models on identifying protein-protein\n  interactions in biomedical text", "abstract": "  Detecting protein-protein interactions (PPIs) is crucial for understanding\ngenetic mechanisms, disease pathogenesis, and drug design. However, with the\nfast-paced growth of biomedical literature, there is a growing need for\nautomated and accurate extraction of PPIs to facilitate scientific knowledge\ndiscovery. Pre-trained language models, such as generative pre-trained\ntransformers (GPT) and bidirectional encoder representations from transformers\n(BERT), have shown promising results in natural language processing (NLP)\ntasks. We evaluated the performance of PPI identification of multiple GPT and\nBERT models using three manually curated gold-standard corpora: Learning\nLanguage in Logic (LLL) with 164 PPIs in 77 sentences, Human Protein Reference\nDatabase with 163 PPIs in 145 sentences, and Interaction Extraction Performance\nAssessment with 335 PPIs in 486 sentences. BERT-based models achieved the best\noverall performance, with BioBERT achieving the highest recall (91.95%) and\nF1-score (86.84%) and PubMedBERT achieving the highest precision (85.25%).\nInterestingly, despite not being explicitly trained for biomedical texts, GPT-4\nachieved commendable performance, comparable to the top-performing BERT models.\nIt achieved a precision of 88.37%, a recall of 85.14%, and an F1-score of\n86.49% on the LLL dataset. These results suggest that GPT models can\neffectively detect PPIs from text data, offering promising avenues for\napplication in biomedical literature mining. Further research could explore how\nthese models might be fine-tuned for even more specialized tasks within the\nbiomedical domain.\n", "date": "2023-03-30", "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": "2312.05585", "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": "2312.02296", "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": "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": "2305.07637", "title": "Text2Cohort: Facilitating Intuitive Access to Biomedical Data with\n  Natural Language Cohort Discovery", "abstract": "  The Imaging Data Commons (IDC) is a cloud-based database that provides\nresearchers with open access to cancer imaging data, with the goal of\nfacilitating collaboration. However, cohort discovery within the IDC database\nhas a significant technical learning curve. Recently, large language models\n(LLM) have demonstrated exceptional utility for natural language processing\ntasks. We developed Text2Cohort, a LLM-powered toolkit to facilitate\nuser-friendly natural language cohort discovery in the IDC. Our method\ntranslates user input into IDC queries using grounding techniques and returns\nthe query's response. We evaluate Text2Cohort on 50 natural language inputs,\nfrom information extraction to cohort discovery. Our toolkit successfully\ngenerated responses with an 88% accuracy and 0.94 F1 score. We demonstrate that\nText2Cohort can enable researchers to discover and curate cohorts on IDC with\nhigh levels of accuracy using natural language in a more intuitive and\nuser-friendly way.\n", "date": "2023-05-12", "categories": []}
{"id": "2311.15106", "title": "Solving the Right Problem is Key for Translational NLP: A Case Study in\n  UMLS Vocabulary Insertion", "abstract": "  As the immense opportunities enabled by large language models become more\napparent, NLP systems will be increasingly expected to excel in real-world\nsettings. However, in many instances, powerful models alone will not yield\ntranslational NLP solutions, especially if the formulated problem is not well\naligned with the real-world task. In this work, we study the case of UMLS\nvocabulary insertion, an important real-world task in which hundreds of\nthousands of new terms, referred to as atoms, are added to the UMLS, one of the\nmost comprehensive open-source biomedical knowledge bases. Previous work aimed\nto develop an automated NLP system to make this time-consuming, costly, and\nerror-prone task more efficient. Nevertheless, practical progress in this\ndirection has been difficult to achieve due to a problem formulation and\nevaluation gap between research output and the real-world task. In order to\naddress this gap, we introduce a new formulation for UMLS vocabulary insertion\nwhich mirrors the real-world task, datasets which faithfully represent it and\nseveral strong baselines we developed through re-purposing existing solutions.\nAdditionally, we propose an effective rule-enhanced biomedical language model\nwhich enables important new model behavior, outperforms all strong baselines\nand provides measurable qualitative improvements to editors who carry out the\nUVI task. We hope this case study provides insight into the considerable\nimportance of problem formulation for the success of translational NLP\nsolutions.\n", "date": "2023-11-25", "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": "2311.16075", "title": "BioLORD-2023: Semantic Textual Representations Fusing LLM and Clinical\n  Knowledge Graph Insights", "abstract": "  In this study, we investigate the potential of Large Language Models to\ncomplement biomedical knowledge graphs in the training of semantic models for\nthe biomedical and clinical domains. Drawing on the wealth of the UMLS\nknowledge graph and harnessing cutting-edge Large Language Models, we propose a\nnew state-of-the-art approach for obtaining high-fidelity representations of\nbiomedical concepts and sentences, consisting of three steps: an improved\ncontrastive learning phase, a novel self-distillation phase, and a weight\naveraging phase. Through rigorous evaluations via the extensive BioLORD testing\nsuite and diverse downstream tasks, we demonstrate consistent and substantial\nperformance improvements over the previous state of the art (e.g. +2pts on\nMedSTS, +2.5pts on MedNLI-S, +6.1pts on EHR-Rel-B). Besides our new\nstate-of-the-art biomedical model for English, we also distill and release a\nmultilingual model compatible with 50+ languages and finetuned on 7 European\nlanguages. Many clinical pipelines can benefit from our latest models. Our new\nmultilingual model enables a range of languages to benefit from our\nadvancements in biomedical semantic representation learning, opening a new\navenue for bioinformatics researchers around the world. As a result, we hope to\nsee BioLORD-2023 becoming a precious tool for future biomedical applications.\n", "date": "2023-11-27", "categories": []}
{"id": "2311.16079", "title": "MEDITRON-70B: Scaling Medical Pretraining for Large Language Models", "abstract": "  Large language models (LLMs) can potentially democratize access to medical\nknowledge. While many efforts have been made to harness and improve LLMs'\nmedical knowledge and reasoning capacities, the resulting models are either\nclosed-source (e.g., PaLM, GPT-4) or limited in scale (<= 13B parameters),\nwhich restricts their abilities. In this work, we improve access to large-scale\nmedical LLMs by releasing MEDITRON: a suite of open-source LLMs with 7B and 70B\nparameters adapted to the medical domain. MEDITRON builds on Llama-2 (through\nour adaptation of Nvidia's Megatron-LM distributed trainer), and extends\npretraining on a comprehensively curated medical corpus, including selected\nPubMed articles, abstracts, and internationally-recognized medical guidelines.\nEvaluations using four major medical benchmarks show significant performance\ngains over several state-of-the-art baselines before and after task-specific\nfinetuning. Overall, MEDITRON achieves a 6% absolute performance gain over the\nbest public baseline in its parameter class and 3% over the strongest baseline\nwe finetuned from Llama-2. Compared to closed-source LLMs, MEDITRON-70B\noutperforms GPT-3.5 and Med-PaLM and is within 5% of GPT-4 and 10% of\nMed-PaLM-2. We release our code for curating the medical pretraining corpus and\nthe MEDITRON model weights to drive open-source development of more capable\nmedical LLMs.\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": "2311.13735", "title": "Surpassing GPT-4 Medical Coding with a Two-Stage Approach", "abstract": "  Recent advances in large language models (LLMs) show potential for clinical\napplications, such as clinical decision support and trial recommendations.\nHowever, the GPT-4 LLM predicts an excessive number of ICD codes for medical\ncoding tasks, leading to high recall but low precision. To tackle this\nchallenge, we introduce LLM-codex, a two-stage approach to predict ICD codes\nthat first generates evidence proposals using an LLM and then employs an\nLSTM-based verification stage. The LSTM learns from both the LLM's high recall\nand human expert's high precision, using a custom loss function. Our model is\nthe only approach that simultaneously achieves state-of-the-art results in\nmedical coding accuracy, accuracy on rare codes, and sentence-level evidence\nidentification to support coding decisions without training on human-annotated\nevidence according to experiments on the MIMIC dataset.\n", "date": "2023-11-22", "categories": []}
{"id": "2210.11913", "title": "NEREL-BIO: A Dataset of Biomedical Abstracts Annotated with Nested Named\n  Entities", "abstract": "  This paper describes NEREL-BIO -- an annotation scheme and corpus of PubMed\nabstracts in Russian and smaller number of abstracts in English. NEREL-BIO\nextends the general domain dataset NEREL by introducing domain-specific entity\ntypes. NEREL-BIO annotation scheme covers both general and biomedical domains\nmaking it suitable for domain transfer experiments. NEREL-BIO provides\nannotation for nested named entities as an extension of the scheme employed for\nNEREL. Nested named entities may cross entity boundaries to connect to shorter\nentities nested within longer entities, making them harder to detect.\n  NEREL-BIO contains annotations for 700+ Russian and 100+ English abstracts.\nAll English PubMed annotations have corresponding Russian counterparts. Thus,\nNEREL-BIO comprises the following specific features: annotation of nested named\nentities, it can be used as a benchmark for cross-domain (NEREL -> NEREL-BIO)\nand cross-language (English -> Russian) transfer. We experiment with both\ntransformer-based sequence models and machine reading comprehension (MRC)\nmodels and report their results.\n  The dataset is freely available at https://github.com/nerel-ds/NEREL-BIO.\n", "date": "2022-10-21", "categories": []}
{"id": "2210.13238", "title": "Multimodal Model with Text and Drug Embeddings for Adverse Drug Reaction\n  Classification", "abstract": "  In this paper, we focus on the classification of tweets as sources of\npotential signals for adverse drug effects (ADEs) or drug reactions (ADRs).\nFollowing the intuition that text and drug structure representations are\ncomplementary, we introduce a multimodal model with two components. These\ncomponents are state-of-the-art BERT-based models for language understanding\nand molecular property prediction. Experiments were carried out on multilingual\nbenchmarks of the Social Media Mining for Health Research and Applications\n(#SMM4H) initiative. Our models obtained state-of-the-art results of 0.61 F1\nand 0.57 F1 on #SMM4H 2021 Shared Tasks 1a and 2 in English and Russian,\nrespectively. On the classification of French tweets from SMM4H 2020 Task 1,\nour approach pushes the state of the art by an absolute gain of 8% F1. Our\nexperiments show that the molecular information obtained from neural networks\nis more beneficial for ADE classification than traditional molecular\ndescriptors. The source code for our models is freely available at\nhttps://github.com/Andoree/smm4h_2021_classification.\n", "date": "2022-10-21", "categories": []}
{"id": "2310.19727", "title": "Generating Medical Prescriptions with Conditional Transformer", "abstract": "  Access to real-world medication prescriptions is essential for medical\nresearch and healthcare quality improvement. However, access to real medication\nprescriptions is often limited due to the sensitive nature of the information\nexpressed. Additionally, manually labelling these instructions for training and\nfine-tuning Natural Language Processing (NLP) models can be tedious and\nexpensive. We introduce a novel task-specific model architecture,\nLabel-To-Text-Transformer (\\textbf{LT3}), tailored to generate synthetic\nmedication prescriptions based on provided labels, such as a vocabulary list of\nmedications and their attributes. LT3 is trained on a set of around 2K lines of\nmedication prescriptions extracted from the MIMIC-III database, allowing the\nmodel to produce valuable synthetic medication prescriptions. We evaluate LT3's\nperformance by contrasting it with a state-of-the-art Pre-trained Language\nModel (PLM), T5, analysing the quality and diversity of generated texts. We\ndeploy the generated synthetic data to train the SpacyNER model for the Named\nEntity Recognition (NER) task over the n2c2-2018 dataset. The experiments show\nthat the model trained on synthetic data can achieve a 96-98\\% F1 score at\nLabel Recognition on Drug, Frequency, Route, Strength, and Form. LT3 codes and\ndata will be shared at\n\\url{https://github.com/HECTA-UoM/Label-To-Text-Transformer}\n", "date": "2023-10-30", "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": "2311.11564", "title": "KBioXLM: A Knowledge-anchored Biomedical Multilingual Pretrained\n  Language Model", "abstract": "  Most biomedical pretrained language models are monolingual and cannot handle\nthe growing cross-lingual requirements. The scarcity of non-English domain\ncorpora, not to mention parallel data, poses a significant hurdle in training\nmultilingual biomedical models. Since knowledge forms the core of\ndomain-specific corpora and can be translated into various languages\naccurately, we propose a model called KBioXLM, which transforms the\nmultilingual pretrained model XLM-R into the biomedical domain using a\nknowledge-anchored approach. We achieve a biomedical multilingual corpus by\nincorporating three granularity knowledge alignments (entity, fact, and passage\nlevels) into monolingual corpora. Then we design three corresponding training\ntasks (entity masking, relation masking, and passage relation prediction) and\ncontinue training on top of the XLM-R model to enhance its domain cross-lingual\nability. To validate the effectiveness of our model, we translate the English\nbenchmarks of multiple tasks into Chinese. Experimental results demonstrate\nthat our model significantly outperforms monolingual and multilingual\npretrained models in cross-lingual zero-shot and few-shot scenarios, achieving\nimprovements of up to 10+ points. Our code is publicly available at\nhttps://github.com/ngwlh-gl/KBioXLM.\n", "date": "2023-11-20", "categories": []}
{"id": "2311.11981", "title": "H-COAL: Human Correction of AI-Generated Labels for Biomedical Named\n  Entity Recognition", "abstract": "  With the rapid advancement of machine learning models for NLP tasks,\ncollecting high-fidelity labels from AI models is a realistic possibility.\nFirms now make AI available to customers via predictions as a service (PaaS).\nThis includes PaaS products for healthcare. It is unclear whether these labels\ncan be used for training a local model without expensive annotation checking by\nin-house experts. In this work, we propose a new framework for Human Correction\nof AI-Generated Labels (H-COAL). By ranking AI-generated outputs, one can\nselectively correct labels and approach gold standard performance (100% human\nlabeling) with significantly less human effort. We show that correcting 5% of\nlabels can close the AI-human performance gap by up to 64% relative\nimprovement, and correcting 20% of labels can close the performance gap by up\nto 86% relative improvement.\n", "date": "2023-11-20", "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": "2309.00312", "title": "Insights Into the Nutritional Prevention of Macular Degeneration based\n  on a Comparative Topic Modeling Approach", "abstract": "  Topic modeling and text mining are subsets of Natural Language Processing\n(NLP) with relevance for conducting meta-analysis (MA) and systematic review\n(SR). For evidence synthesis, the above NLP methods are conventionally used for\ntopic-specific literature searches or extracting values from reports to\nautomate essential phases of SR and MA. Instead, this work proposes a\ncomparative topic modeling approach to analyze reports of contradictory results\non the same general research question. Specifically, the objective is to\nidentify topics exhibiting distinct associations with significant results for\nan outcome of interest by ranking them according to their proportional\noccurrence in (and consistency of distribution across) reports of significant\neffects. The proposed method was tested on broad-scope studies addressing\nwhether supplemental nutritional compounds significantly benefit macular\ndegeneration (MD). Four of these were further supported in terms of\neffectiveness upon conducting a follow-up literature search for validation\n(omega-3 fatty acids, copper, zeaxanthin, and nitrates). The two not supported\nby the follow-up literature search (niacin and molybdenum) also had scores in\nthe lowest range under the proposed scoring system, suggesting that the\nproposed methods score for a given topic may be a viable proxy for its degree\nof association with the outcome of interest and can be helpful in the search\nfor potentially causal relationships. These results underpin the proposed\nmethods potential to add specificity in understanding effects from broad-scope\nreports, elucidate topics of interest for future research, and guide evidence\nsynthesis in a systematic and scalable way. All of this is accomplished while\nyielding valuable insights into the prevention of MD.\n", "date": "2023-09-01", "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": "2310.14088", "title": "MedEval: A Multi-Level, Multi-Task, and Multi-Domain Medical Benchmark\n  for Language Model Evaluation", "abstract": "  Curated datasets for healthcare are often limited due to the need of human\nannotations from experts. In this paper, we present MedEval, a multi-level,\nmulti-task, and multi-domain medical benchmark to facilitate the development of\nlanguage models for healthcare. MedEval is comprehensive and consists of data\nfrom several healthcare systems and spans 35 human body regions from 8\nexamination modalities. With 22,779 collected sentences and 21,228 reports, we\nprovide expert annotations at multiple levels, offering a granular potential\nusage of the data and supporting a wide range of tasks. Moreover, we\nsystematically evaluated 10 generic and domain-specific language models under\nzero-shot and finetuning settings, from domain-adapted baselines in healthcare\nto general-purposed state-of-the-art large language models (e.g., ChatGPT). Our\nevaluations reveal varying effectiveness of the two categories of language\nmodels across different tasks, from which we notice the importance of\ninstruction tuning for few-shot usage of large language models. Our\ninvestigation paves the way toward benchmarking language models for healthcare\nand provides valuable insights into the strengths and limitations of adopting\nlarge language models in medical domains, informing their practical\napplications and future advancements.\n", "date": "2023-10-21", "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": "2305.17353", "title": "Complementary and Integrative Health Lexicon (CIHLex) and Entity\n  Recognition in the Literature", "abstract": "  Objective: Our study aimed to construct an exhaustive Complementary and\nIntegrative Health (CIH) Lexicon (CIHLex) to better represent the often\nunderrepresented physical and psychological CIH approaches in standard\nterminologies. We also intended to apply advanced Natural Language Processing\n(NLP) models such as Bidirectional Encoder Representations from Transformers\n(BERT) and GPT-3.5 Turbo for CIH named entity recognition, evaluating their\nperformance against established models like MetaMap and CLAMP. Materials and\nMethods: We constructed the CIHLex by integrating various resources, compiling\nand integrating data from biomedical literature and relevant knowledge bases.\nThe Lexicon encompasses 198 unique concepts with 1090 corresponding unique\nterms. We matched these concepts to the Unified Medical Language System (UMLS).\nAdditionally, we developed and utilized BERT models and compared their\nefficiency in CIH named entity recognition to that of other models such as\nMetaMap, CLAMP, and GPT3.5-turbo. Results: From the 198 unique concepts in\nCIHLex, 62.1% could be matched to at least one term in the UMLS. Moreover,\n75.7% of the mapped UMLS Concept Unique Identifiers (CUIs) were categorized as\n\"Therapeutic or Preventive Procedure.\" Among the models applied to CIH named\nentity recognition, BLUEBERT delivered the highest macro average F1-score of\n0.90, surpassing other models. Conclusion: Our CIHLex significantly augments\nrepresentation of CIH approaches in biomedical literature. Demonstrating the\nutility of advanced NLP models, BERT notably excelled in CIH entity\nrecognition. These results highlight promising strategies for enhancing\nstandardization and recognition of CIH terminology in biomedical contexts.\n", "date": "2023-05-27", "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": "2311.06364", "title": "Relation Extraction in underexplored biomedical domains: A\n  diversity-optimised sampling and synthetic data generation approach", "abstract": "  The sparsity of labelled data is an obstacle to the development of Relation\nExtraction models and the completion of databases in various biomedical areas.\nWhile being of high interest in drug-discovery, the natural-products\nliterature, reporting the identification of potential bioactive compounds from\norganisms, is a concrete example of such an overlooked topic. To mark the start\nof this new task, we created the first curated evaluation dataset and extracted\nliterature items from the LOTUS database to build training sets. To this end,\nwe developed a new sampler inspired by diversity metrics in ecology, named\nGreedy Maximum Entropy sampler, or GME-sampler\n(https://github.com/idiap/gme-sampler). The strategic optimization of both\nbalance and diversity of the selected items in the evaluation set is important\ngiven the resource-intensive nature of manual curation. After quantifying the\nnoise in the training set, in the form of discrepancies between the input\nabstracts text and the expected output labels, we explored different strategies\naccordingly. Framing the task as an end-to-end Relation Extraction, we\nevaluated the performance of standard fine-tuning as a generative task and\nfew-shot learning with open Large Language Models (LLaMA 7B-65B). In addition\nto their evaluation in few-shot settings, we explore the potential of open\nLarge Language Models (Vicuna-13B) as synthetic data generator and propose a\nnew workflow for this purpose. All evaluated models exhibited substantial\nimprovements when fine-tuned on synthetic abstracts rather than the original\nnoisy data. We provide our best performing (f1-score=59.0) BioGPT-Large model\nfor end-to-end RE of natural-products relationships along with all the\ngenerated synthetic data and the evaluation dataset. See more details at\nhttps://github.com/idiap/abroad-re.\n", "date": "2023-11-10", "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": "2311.02271", "title": "FaMeSumm: Investigating and Improving Faithfulness of Medical\n  Summarization", "abstract": "  Summaries of medical text shall be faithful by being consistent and factual\nwith source inputs, which is an important but understudied topic for safety and\nefficiency in healthcare. In this paper, we investigate and improve\nfaithfulness in summarization on a broad range of medical summarization tasks.\nOur investigation reveals that current summarization models often produce\nunfaithful outputs for medical input text. We then introduce FaMeSumm, a\nframework to improve faithfulness by fine-tuning pre-trained language models\nbased on medical knowledge. FaMeSumm performs contrastive learning on designed\nsets of faithful and unfaithful summaries, and it incorporates medical terms\nand their contexts to encourage faithful generation of medical terms. We\nconduct comprehensive experiments on three datasets in two languages: health\nquestion and radiology report summarization datasets in English, and a\npatient-doctor dialogue dataset in Chinese. Results demonstrate that FaMeSumm\nis flexible and effective by delivering consistent improvements over mainstream\nlanguage models such as BART, T5, mT5, and PEGASUS, yielding state-of-the-art\nperformances on metrics for faithfulness and general quality. Human evaluation\nby doctors also shows that FaMeSumm generates more faithful outputs. Our code\nis available at https://github.com/psunlpgroup/FaMeSumm .\n", "date": "2023-11-03", "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": "2311.05169", "title": "Large Language Models and Prompt Engineering for Biomedical Query\n  Focused Multi-Document Summarisation", "abstract": "  This paper reports on the use of prompt engineering and GPT-3.5 for\nbiomedical query-focused multi-document summarisation. Using GPT-3.5 and\nappropriate prompts, our system achieves top ROUGE-F1 results in the task of\nobtaining short-paragraph-sized answers to biomedical questions in the 2023\nBioASQ Challenge (BioASQ 11b). This paper confirms what has been observed in\nother domains: 1) Prompts that incorporated few-shot samples generally improved\non their counterpart zero-shot variants; 2) The largest improvement was\nachieved by retrieval augmented generation. The fact that these prompts allow\nour top runs to rank within the top two runs of BioASQ 11b demonstrate the\npower of using adequate prompts for Large Language Models in general, and\nGPT-3.5 in particular, for query-focused summarisation.\n", "date": "2023-11-09", "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": "2308.12877", "title": "DS4DH at #SMM4H 2023: Zero-Shot Adverse Drug Events Normalization using\n  Sentence Transformers and Reciprocal-Rank Fusion", "abstract": "  This paper outlines the performance evaluation of a system for adverse drug\nevent normalization, developed by the Data Science for Digital Health (DS4DH)\ngroup for the Social Media Mining for Health Applications (SMM4H) 2023 shared\ntask 5. Shared task 5 targeted the normalization of adverse drug event mentions\nin Twitter to standard concepts of the Medical Dictionary for Regulatory\nActivities terminology. Our system hinges on a two-stage approach: BERT\nfine-tuning for entity recognition, followed by zero-shot normalization using\nsentence transformers and reciprocal-rank fusion. The approach yielded a\nprecision of 44.9%, recall of 40.5%, and an F1-score of 42.6%. It outperformed\nthe median performance in shared task 5 by 10% and demonstrated the highest\nperformance among all participants. These results substantiate the\neffectiveness of our approach and its potential application for adverse drug\nevent normalization in the realm of social media text mining.\n", "date": "2023-08-15", "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": "2311.00204", "title": "Continuous Training and Fine-tuning for Domain-Specific Language Models\n  in Medical Question Answering", "abstract": "  Large language models exhibit promising general capabilities but often lack\nspecialized knowledge for domain-specific tasks. Developing domain experts from\na base model enables a range of applications without prohibitive training\ncosts. This work demonstrates a method using continuous training and\ninstruction fine-tuning to rapidly adapt Llama 2 base models to the Chinese\nmedical domain. We first conduct continuous training on 1B tokens from Chinese\nmedical references to teach relevant vocabulary and knowledge. The models are\nthen fine-tuned on 54K examples sourced from the Chinese National Medical\nLicensing Examination. Experiments on Chinese medical data confirm the\neffectiveness of this approach, producing a model comparable to GPT-3.5-turbo\nwhile using way less computational resource. The resulting domain-specific\nmodel could be useful for various Chinese medical applications. More broadly,\nthis provides a template for domain-specific training of large language models\nin areas where pre-trained models lack the required expertise, such as law,\nscience, and engineering.\n", "date": "2023-11-01", "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": "2310.18431", "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": "2309.17332", "title": "Overview of the BioLaySumm 2023 Shared Task on Lay Summarization of\n  Biomedical Research Articles", "abstract": "  This paper presents the results of the shared task on Lay Summarisation of\nBiomedical Research Articles (BioLaySumm), hosted at the BioNLP Workshop at ACL\n2023. The goal of this shared task is to develop abstractive summarisation\nmodels capable of generating \"lay summaries\" (i.e., summaries that are\ncomprehensible to non-technical audiences) in both a controllable and\nnon-controllable setting. There are two subtasks: 1) Lay Summarisation, where\nthe goal is for participants to build models for lay summary generation only,\ngiven the full article text and the corresponding abstract as input; and 2)\nReadability-controlled Summarisation, where the goal is for participants to\ntrain models to generate both the technical abstract and the lay summary, given\nan article's main text as input. In addition to overall results, we report on\nthe setup and insights from the BioLaySumm shared task, which attracted a total\nof 20 participating teams across both subtasks.\n", "date": "2023-09-29", "categories": []}
{"id": "2310.15572", "title": "Natural Language Processing for Drug Discovery Knowledge Graphs:\n  promises and pitfalls", "abstract": "  Building and analysing knowledge graphs (KGs) to aid drug discovery is a\ntopical area of research. A salient feature of KGs is their ability to combine\nmany heterogeneous data sources in a format that facilitates discovering\nconnections. The utility of KGs has been exemplified in areas such as drug\nrepurposing, with insights made through manual exploration and modelling of the\ndata. In this article, we discuss promises and pitfalls of using natural\nlanguage processing (NLP) to mine unstructured text typically from scientific\nliterature as a data source for KGs. This draws on our experience of initially\nparsing structured data sources such as ChEMBL as the basis for data within a\nKG, and then enriching or expanding upon them using NLP. The fundamental\npromise of NLP for KGs is the automated extraction of data from millions of\ndocuments a task practically impossible to do via human curation alone.\nHowever, there are many potential pitfalls in NLP-KG pipelines such as\nincorrect named entity recognition and ontology linking all of which could\nultimately lead to erroneous inferences and conclusions.\n", "date": "2023-10-24", "categories": []}
{"id": "2310.15684", "title": "Improving Biomedical Abstractive Summarisation with Knowledge\n  Aggregation from Citation Papers", "abstract": "  Abstracts derived from biomedical literature possess distinct domain-specific\ncharacteristics, including specialised writing styles and biomedical\nterminologies, which necessitate a deep understanding of the related\nliterature. As a result, existing language models struggle to generate\ntechnical summaries that are on par with those produced by biomedical experts,\ngiven the absence of domain-specific background knowledge. This paper aims to\nenhance the performance of language models in biomedical abstractive\nsummarisation by aggregating knowledge from external papers cited within the\nsource article. We propose a novel attention-based citation aggregation model\nthat integrates domain-specific knowledge from citation papers, allowing neural\nnetworks to generate summaries by leveraging both the paper content and\nrelevant knowledge from citation papers. Furthermore, we construct and release\na large-scale biomedical summarisation dataset that serves as a foundation for\nour research. Extensive experiments demonstrate that our model outperforms\nstate-of-the-art approaches and achieves substantial improvements in\nabstractive biomedical text summarisation.\n", "date": "2023-10-24", "categories": []}
{"id": "2310.15702", "title": "Enhancing Biomedical Lay Summarisation with External Knowledge Graphs", "abstract": "  Previous approaches for automatic lay summarisation are exclusively reliant\non the source article that, given it is written for a technical audience (e.g.,\nresearchers), is unlikely to explicitly define all technical concepts or state\nall of the background information that is relevant for a lay audience. We\naddress this issue by augmenting eLife, an existing biomedical lay\nsummarisation dataset, with article-specific knowledge graphs, each containing\ndetailed information on relevant biomedical concepts. Using both automatic and\nhuman evaluations, we systematically investigate the effectiveness of three\ndifferent approaches for incorporating knowledge graphs within lay\nsummarisation models, with each method targeting a distinct area of the\nencoder-decoder model architecture. Our results confirm that integrating\ngraph-based domain knowledge can significantly benefit lay summarisation by\nsubstantially increasing the readability of generated text and improving the\nexplanation of technical concepts.\n", "date": "2023-10-24", "categories": []}
{"id": "2310.14151", "title": "PromptCBLUE: A Chinese Prompt Tuning Benchmark for the Medical Domain", "abstract": "  Biomedical language understanding benchmarks are the driving forces for\nartificial intelligence applications with large language model (LLM) back-ends.\nHowever, most current benchmarks: (a) are limited to English which makes it\nchallenging to replicate many of the successes in English for other languages,\nor (b) focus on knowledge probing of LLMs and neglect to evaluate how LLMs\napply these knowledge to perform on a wide range of bio-medical tasks, or (c)\nhave become a publicly available corpus and are leaked to LLMs during\npre-training. To facilitate the research in medical LLMs, we re-build the\nChinese Biomedical Language Understanding Evaluation (CBLUE) benchmark into a\nlarge scale prompt-tuning benchmark, PromptCBLUE. Our benchmark is a suitable\ntest-bed and an online platform for evaluating Chinese LLMs' multi-task\ncapabilities on a wide range bio-medical tasks including medical entity\nrecognition, medical text classification, medical natural language inference,\nmedical dialogue understanding and medical content/dialogue generation. To\nestablish evaluation on these tasks, we have experimented and report the\nresults with the current 9 Chinese LLMs fine-tuned with differtent fine-tuning\ntechniques.\n", "date": "2023-10-22", "categories": []}
{"id": "2310.14366", "title": "Bi-Encoders based Species Normalization -- Pairwise Sentence Learning to\n  Rank", "abstract": "  Motivation: Biomedical named-entity normalization involves connecting\nbiomedical entities with distinct database identifiers in order to facilitate\ndata integration across various fields of biology. Existing systems for\nbiomedical named entity normalization heavily rely on dictionaries, manually\ncreated rules, and high-quality representative features such as lexical or\nmorphological characteristics. However, recent research has investigated the\nuse of neural network-based models to reduce dependence on dictionaries,\nmanually crafted rules, and features. Despite these advancements, the\nperformance of these models is still limited due to the lack of sufficiently\nlarge training datasets. These models have a tendency to overfit small training\ncorpora and exhibit poor generalization when faced with previously unseen\nentities, necessitating the redesign of rules and features. Contribution: We\npresent a novel deep learning approach for named entity normalization, treating\nit as a pair-wise learning to rank problem. Our method utilizes the widely-used\ninformation retrieval algorithm Best Matching 25 to generate candidate\nconcepts, followed by the application of bi-directional encoder representation\nfrom the encoder (BERT) to re-rank the candidate list. Notably, our approach\neliminates the need for feature-engineering or rule creation. We conduct\nexperiments on species entity types and evaluate our method against\nstate-of-the-art techniques using LINNAEUS and S800 biomedical corpora. Our\nproposed approach surpasses existing methods in linking entities to the NCBI\ntaxonomy. To the best of our knowledge, there is no existing neural\nnetwork-based approach for species normalization in the literature.\n", "date": "2023-10-22", "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": "2212.10784", "title": "Can NLI Provide Proper Indirect Supervision for Low-resource Biomedical\n  Relation Extraction?", "abstract": "  Two key obstacles in biomedical relation extraction (RE) are the scarcity of\nannotations and the prevalence of instances without explicitly pre-defined\nlabels due to low annotation coverage. Existing approaches, which treat\nbiomedical RE as a multi-class classification task, often result in poor\ngeneralization in low-resource settings and do not have the ability to make\nselective prediction on unknown cases but give a guess from seen relations,\nhindering the applicability of those approaches. We present NBR, which converts\nbiomedical RE as natural language inference formulation through indirect\nsupervision. By converting relations to natural language hypotheses, NBR is\ncapable of exploiting semantic cues to alleviate annotation scarcity. By\nincorporating a ranking-based loss that implicitly calibrates abstinent\ninstances, NBR learns a clearer decision boundary and is instructed to abstain\non uncertain instances. Extensive experiments on three widely-used biomedical\nRE benchmarks, namely ChemProt, DDI and GAD, verify the effectiveness of NBR in\nboth full-set and low-resource regimes. Our analysis demonstrates that indirect\nsupervision benefits biomedical RE even when a domain gap exists, and combining\nNLI knowledge with biomedical knowledge leads to the best performance gains.\n", "date": "2022-12-21", "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": "2305.14480", "title": "BAND: Biomedical Alert News Dataset", "abstract": "  Infectious disease outbreaks continue to pose a significant threat to human\nhealth and well-being. To improve disease surveillance and understanding of\ndisease spread, several surveillance systems have been developed to monitor\ndaily news alerts and social media. However, existing systems lack thorough\nepidemiological analysis in relation to corresponding alerts or news, largely\ndue to the scarcity of well-annotated reports data. To address this gap, we\nintroduce the Biomedical Alert News Dataset (BAND), which includes 1,508\nsamples from existing reported news articles, open emails, and alerts, as well\nas 30 epidemiology-related questions. These questions necessitate the model's\nexpert reasoning abilities, thereby offering valuable insights into the\noutbreak of the disease. The BAND dataset brings new challenges to the NLP\nworld, requiring better disguise capability of the content and the ability to\ninfer important information. We provide several benchmark tasks, including\nNamed Entity Recognition (NER), Question Answering (QA), and Event Extraction\n(EE), to show how existing models are capable of handling these tasks in the\nepidemiology domain. To the best of our knowledge, the BAND corpus is the\nlargest corpus of well-annotated biomedical outbreak alert news with\nelaborately designed questions, making it a valuable resource for\nepidemiologists and NLP researchers alike.\n", "date": "2023-05-23", "categories": []}
{"id": "2307.15343", "title": "Med-HALT: Medical Domain Hallucination Test for Large Language Models", "abstract": "  This research paper focuses on the challenges posed by hallucinations in\nlarge language models (LLMs), particularly in the context of the medical\ndomain. Hallucination, wherein these models generate plausible yet unverified\nor incorrect information, can have serious consequences in healthcare\napplications. We propose a new benchmark and dataset, Med-HALT (Medical Domain\nHallucination Test), designed specifically to evaluate and reduce\nhallucinations. Med-HALT provides a diverse multinational dataset derived from\nmedical examinations across various countries and includes multiple innovative\ntesting modalities. Med-HALT includes two categories of tests reasoning and\nmemory-based hallucination tests, designed to assess LLMs's problem-solving and\ninformation retrieval abilities.\n  Our study evaluated leading LLMs, including Text Davinci, GPT-3.5, LlaMa-2,\nMPT, and Falcon, revealing significant differences in their performance. The\npaper provides detailed insights into the dataset, promoting transparency and\nreproducibility. Through this work, we aim to contribute to the development of\nsafer and more reliable language models in healthcare. Our benchmark can be\nfound at medhalt.github.io\n", "date": "2023-07-28", "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": "2310.10571", "title": "Emerging Challenges in Personalized Medicine: Assessing Demographic\n  Effects on Biomedical Question Answering Systems", "abstract": "  State-of-the-art question answering (QA) models exhibit a variety of social\nbiases (e.g., with respect to sex or race), generally explained by similar\nissues in their training data. However, what has been overlooked so far is that\nin the critical domain of biomedicine, any unjustified change in model output\ndue to patient demographics is problematic: it results in the unfair treatment\nof patients. Selecting only questions on biomedical topics whose answers do not\ndepend on ethnicity, sex, or sexual orientation, we ask the following research\nquestions: (RQ1) Do the answers of QA models change when being provided with\nirrelevant demographic information? (RQ2) Does the answer of RQ1 differ between\nknowledge graph (KG)-grounded and text-based QA systems? We find that\nirrelevant demographic information change up to 15% of the answers of a\nKG-grounded system and up to 23% of the answers of a text-based system,\nincluding changes that affect accuracy. We conclude that unjustified answer\nchanges caused by patient demographics are a frequent phenomenon, which raises\nfairness concerns and should be paid more attention to.\n", "date": "2023-10-16", "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": "2310.02778", "title": "Integrating UMLS Knowledge into Large Language Models for Medical\n  Question Answering", "abstract": "  Large language models (LLMs) have demonstrated powerful text generation\ncapabilities, bringing unprecedented innovation to the healthcare field. While\nLLMs hold immense promise for applications in healthcare, applying them to real\nclinical scenarios presents significant challenges, as these models may\ngenerate content that deviates from established medical facts and even exhibit\npotential biases. In our research, we develop an augmented LLM framework based\non the Unified Medical Language System (UMLS), aiming to better serve the\nhealthcare community. We employ LLaMa2-13b-chat and ChatGPT-3.5 as our\nbenchmark models, and conduct automatic evaluations using the ROUGE Score and\nBERTScore on 104 questions from the LiveQA test set. Additionally, we establish\ncriteria for physician-evaluation based on four dimensions: Factuality,\nCompleteness, Readability and Relevancy. ChatGPT-3.5 is used for physician\nevaluation with 20 questions on the LiveQA test set. Multiple resident\nphysicians conducted blind reviews to evaluate the generated content, and the\nresults indicate that this framework effectively enhances the factuality,\ncompleteness, and relevance of generated content. Our research demonstrates the\neffectiveness of using UMLS-augmented LLMs and highlights the potential\napplication value of LLMs in in medical question-answering.\n", "date": "2023-10-04", "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": "2303.15100", "title": "An Information Extraction Study: Take In Mind the Tokenization!", "abstract": "  Current research on the advantages and trade-offs of using characters,\ninstead of tokenized text, as input for deep learning models, has evolved\nsubstantially. New token-free models remove the traditional tokenization step;\nhowever, their efficiency remains unclear. Moreover, the effect of tokenization\nis relatively unexplored in sequence tagging tasks. To this end, we investigate\nthe impact of tokenization when extracting information from documents and\npresent a comparative study and analysis of subword-based and character-based\nmodels. Specifically, we study Information Extraction (IE) from biomedical\ntexts. The main outcome is twofold: tokenization patterns can introduce\ninductive bias that results in state-of-the-art performance, and the\ncharacter-based models produce promising results; thus, transitioning to\ntoken-free IE models is feasible.\n", "date": "2023-03-27", "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": "2310.04468", "title": "Validating transformers for redaction of text from electronic health\n  records in real-world healthcare", "abstract": "  Protecting patient privacy in healthcare records is a top priority, and\nredaction is a commonly used method for obscuring directly identifiable\ninformation in text. Rule-based methods have been widely used, but their\nprecision is often low causing over-redaction of text and frequently not being\nadaptable enough for non-standardised or unconventional structures of personal\nhealth information. Deep learning techniques have emerged as a promising\nsolution, but implementing them in real-world environments poses challenges due\nto the differences in patient record structure and language across different\ndepartments, hospitals, and countries.\n  In this study, we present AnonCAT, a transformer-based model and a blueprint\non how deidentification models can be deployed in real-world healthcare.\nAnonCAT was trained through a process involving manually annotated redactions\nof real-world documents from three UK hospitals with different electronic\nhealth record systems and 3116 documents. The model achieved high performance\nin all three hospitals with a Recall of 0.99, 0.99 and 0.96.\n  Our findings demonstrate the potential of deep learning techniques for\nimproving the efficiency and accuracy of redaction in global healthcare data\nand highlight the importance of building workflows which not just use these\nmodels but are also able to continually fine-tune and audit the performance of\nthese algorithms to ensure continuing effectiveness in real-world settings.\nThis approach provides a blueprint for the real-world use of de-identifying\nalgorithms through fine-tuning and localisation, the code together with\ntutorials is available on GitHub (https://github.com/CogStack/MedCAT).\n", "date": "2023-10-05", "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": "2209.09631", "title": "De-Identification of French Unstructured Clinical Notes for Machine\n  Learning Tasks", "abstract": "  Unstructured textual data are at the heart of health systems: liaison letters\nbetween doctors, operating reports, coding of procedures according to the\nICD-10 standard, etc. The details included in these documents make it possible\nto get to know the patient better, to better manage him or her, to better study\nthe pathologies, to accurately remunerate the associated medical acts\\ldots All\nthis seems to be (at least partially) within reach of today by artificial\nintelligence techniques. However, for obvious reasons of privacy protection,\nthe designers of these AIs do not have the legal right to access these\ndocuments as long as they contain identifying data. De-identifying these\ndocuments, i.e. detecting and deleting all identifying information present in\nthem, is a legally necessary step for sharing this data between two\ncomplementary worlds. Over the last decade, several proposals have been made to\nde-identify documents, mainly in English. While the detection scores are often\nhigh, the substitution methods are often not very robust to attack. In French,\nvery few methods are based on arbitrary detection and/or substitution rules. In\nthis paper, we propose a new comprehensive de-identification method dedicated\nto French-language medical documents. Both the approach for the detection of\nidentifying elements (based on deep learning) and their substitution (based on\ndifferential privacy) are based on the most proven existing approaches. The\nresult is an approach that effectively protects the privacy of the patients at\nthe heart of these medical documents. The whole approach has been evaluated on\na French language medical dataset of a French public hospital and the results\nare very encouraging.\n", "date": "2022-09-16", "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": "2301.09350", "title": "Large-scale investigation of weakly-supervised deep learning for the\n  fine-grained semantic indexing of biomedical literature", "abstract": "  Objective: Semantic indexing of biomedical literature is usually done at the\nlevel of MeSH descriptors with several related but distinct biomedical concepts\noften grouped together and treated as a single topic. This study proposes a new\nmethod for the automated refinement of subject annotations at the level of MeSH\nconcepts. Methods: Lacking labelled data, we rely on weak supervision based on\nconcept occurrence in the abstract of an article, which is also enhanced by\ndictionary-based heuristics. In addition, we investigate deep learning\napproaches, making design choices to tackle the particular challenges of this\ntask. The new method is evaluated on a large-scale retrospective scenario,\nbased on concepts that have been promoted to descriptors. Results: In our\nexperiments concept occurrence was the strongest heuristic achieving a macro-F1\nscore of about 0.63 across several labels. The proposed method improved it\nfurther by more than 4pp. Conclusion: The results suggest that concept\noccurrence is a strong heuristic for refining the coarse-grained labels at the\nlevel of MeSH concepts and the proposed method improves it further.\n", "date": "2023-01-23", "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": "2310.01299", "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": "2309.16175", "title": "Using Weak Supervision and Data Augmentation in Question Answering", "abstract": "  The onset of the COVID-19 pandemic accentuated the need for access to\nbiomedical literature to answer timely and disease-specific questions. During\nthe early days of the pandemic, one of the biggest challenges we faced was the\nlack of peer-reviewed biomedical articles on COVID-19 that could be used to\ntrain machine learning models for question answering (QA). In this paper, we\nexplore the roles weak supervision and data augmentation play in training deep\nneural network QA models. First, we investigate whether labels generated\nautomatically from the structured abstracts of scholarly papers using an\ninformation retrieval algorithm, BM25, provide a weak supervision signal to\ntrain an extractive QA model. We also curate new QA pairs using information\nretrieval techniques, guided by the clinicaltrials.gov schema and the\nstructured abstracts of articles, in the absence of annotated data from\nbiomedical domain experts. Furthermore, we explore augmenting the training data\nof a deep neural network model with linguistic features from external sources\nsuch as lexical databases to account for variations in word morphology and\nmeaning. To better utilize our training data, we apply curriculum learning to\ndomain adaptation, fine-tuning our QA model in stages based on characteristics\nof the QA pairs. We evaluate our methods in the context of QA models at the\ncore of a system to answer questions about COVID-19.\n", "date": "2023-09-28", "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": "2309.12278", "title": "Inspire the Large Language Model by External Knowledge on BioMedical\n  Named Entity Recognition", "abstract": "  Large language models (LLMs) have demonstrated dominating performance in many\nNLP tasks, especially on generative tasks. However, they often fall short in\nsome information extraction tasks, particularly those requiring domain-specific\nknowledge, such as Biomedical Named Entity Recognition (NER). In this paper,\ninspired by Chain-of-thought, we leverage the LLM to solve the Biomedical NER\nstep-by-step: break down the NER task into entity span extraction and entity\ntype determination. Additionally, for entity type determination, we inject\nentity knowledge to address the problem that LLM's lack of domain knowledge\nwhen predicting entity category. Experimental results show a significant\nimprovement in our two-step BioNER approach compared to previous few-shot LLM\nbaseline. Additionally, the incorporation of external knowledge significantly\nenhances entity category determination performance.\n", "date": "2023-09-21", "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": "2309.09877", "title": "Not Enough Labeled Data? Just Add Semantics: A Data-Efficient Method for\n  Inferring Online Health Texts", "abstract": "  User-generated texts available on the web and social platforms are often long\nand semantically challenging, making them difficult to annotate. Obtaining\nhuman annotation becomes increasingly difficult as problem domains become more\nspecialized. For example, many health NLP problems require domain experts to be\na part of the annotation pipeline. Thus, it is crucial that we develop\nlow-resource NLP solutions able to work with this set of limited-data problems.\nIn this study, we employ Abstract Meaning Representation (AMR) graphs as a\nmeans to model low-resource Health NLP tasks sourced from various online health\nresources and communities. AMRs are well suited to model online health texts as\nthey can represent multi-sentence inputs, abstract away from complex\nterminology, and model long-distance relationships between co-referring tokens.\nAMRs thus improve the ability of pre-trained language models to reason about\nhigh-complexity texts. Our experiments show that we can improve performance on\n6 low-resource health NLP tasks by augmenting text embeddings with semantic\ngraph embeddings. Our approach is task agnostic and easy to merge into any\nstandard text classification pipeline. We experimentally validate that AMRs are\nuseful in the modeling of complex texts by analyzing performance through the\nlens of two textual complexity measures: the Flesch Kincaid Reading Level and\nSyntactic Complexity. Our error analysis shows that AMR-infused language models\nperform better on complex texts and generally show less predictive variance in\nthe presence of changing complexity.\n", "date": "2023-09-18", "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": "2309.02884", "title": "Aligning Large Language Models for Clinical Tasks", "abstract": "  Large Language Models (LLMs) have demonstrated remarkable adaptability,\nshowcasing their capacity to excel in tasks for which they were not explicitly\ntrained. However, despite their impressive natural language processing (NLP)\ncapabilities, effective alignment of LLMs remains a crucial challenge when\ndeploying them for specific clinical applications. The ability to generate\nresponses with factually accurate content and to engage in non-trivial\nreasoning steps are crucial for the LLMs to be eligible for applications in\nclinical medicine. Employing a combination of techniques including\ninstruction-tuning and in-prompt strategies like few-shot and chain-of-thought\nprompting has significantly enhanced the performance of LLMs. Our proposed\nalignment strategy for medical question-answering, known as\n'expand-guess-refine', offers a parameter and data-efficient solution. A\npreliminary analysis of this method demonstrated outstanding performance,\nachieving a score of 70.63% on a subset of questions sourced from the USMLE\ndataset.\n", "date": "2023-09-06", "categories": []}
{"id": "2309.01114", "title": "MedChatZH: a Better Medical Adviser Learns from Better Instructions", "abstract": "  Generative large language models (LLMs) have shown great success in various\napplications, including question-answering (QA) and dialogue systems. However,\nin specialized domains like traditional Chinese medical QA, these models may\nperform unsatisfactorily without fine-tuning on domain-specific datasets. To\naddress this, we introduce MedChatZH, a dialogue model designed specifically\nfor traditional Chinese medical QA. Our model is pre-trained on Chinese\ntraditional medical books and fine-tuned with a carefully curated medical\ninstruction dataset. It outperforms several solid baselines on a real-world\nmedical dialogue dataset. We release our model, code, and dataset on\nhttps://github.com/tyang816/MedChatZH to facilitate further research in the\ndomain of traditional Chinese medicine and LLMs.\n", "date": "2023-09-03", "categories": []}
{"id": "2309.01370", "title": "ReOnto: A Neuro-Symbolic Approach for Biomedical Relation Extraction", "abstract": "  Relation Extraction (RE) is the task of extracting semantic relationships\nbetween entities in a sentence and aligning them to relations defined in a\nvocabulary, which is generally in the form of a Knowledge Graph (KG) or an\nontology. Various approaches have been proposed so far to address this task.\nHowever, applying these techniques to biomedical text often yields\nunsatisfactory results because it is hard to infer relations directly from\nsentences due to the nature of the biomedical relations. To address these\nissues, we present a novel technique called ReOnto, that makes use of neuro\nsymbolic knowledge for the RE task. ReOnto employs a graph neural network to\nacquire the sentence representation and leverages publicly accessible\nontologies as prior knowledge to identify the sentential relation between two\nentities. The approach involves extracting the relation path between the two\nentities from the ontology. We evaluate the effect of using symbolic knowledge\nfrom ontologies with graph neural networks. Experimental results on two public\nbiomedical datasets, BioRel and ADE, show that our method outperforms all the\nbaselines (approximately by 3\\%).\n", "date": "2023-09-04", "categories": []}
{"id": "2309.01812", "title": "Into the Single Cell Multiverse: an End-to-End Dataset for Procedural\n  Knowledge Extraction in Biomedical Texts", "abstract": "  Many of the most commonly explored natural language processing (NLP)\ninformation extraction tasks can be thought of as evaluations of declarative\nknowledge, or fact-based information extraction. Procedural knowledge\nextraction, i.e., breaking down a described process into a series of steps, has\nreceived much less attention, perhaps in part due to the lack of structured\ndatasets that capture the knowledge extraction process from end-to-end. To\naddress this unmet need, we present FlaMB\\'e (Flow annotations for Multiverse\nBiological entities), a collection of expert-curated datasets across a series\nof complementary tasks that capture procedural knowledge in biomedical texts.\nThis dataset is inspired by the observation that one ubiquitous source of\nprocedural knowledge that is described as unstructured text is within academic\npapers describing their methodology. The workflows annotated in FlaMB\\'e are\nfrom texts in the burgeoning field of single cell research, a research area\nthat has become notorious for the number of software tools and complexity of\nworkflows used. Additionally, FlaMB\\'e provides, to our knowledge, the largest\nmanually curated named entity recognition (NER) and disambiguation (NED)\ndatasets for tissue/cell type, a fundamental biological entity that is critical\nfor knowledge extraction in the biomedical research domain. Beyond providing a\nvaluable dataset to enable further development of NLP models for procedural\nknowledge extraction, automating the process of workflow mining also has\nimportant implications for advancing reproducibility in biomedical research.\n", "date": "2023-09-04", "categories": []}
{"id": "2309.02188", "title": "Incorporating Dictionaries into a Neural Network Architecture to Extract\n  COVID-19 Medical Concepts From Social Media", "abstract": "  We investigate the potential benefit of incorporating dictionary information\ninto a neural network architecture for natural language processing. In\nparticular, we make use of this architecture to extract several concepts\nrelated to COVID-19 from an on-line medical forum. We use a sample from the\nforum to manually curate one dictionary for each concept. In addition, we use\nMetaMap, which is a tool for extracting biomedical concepts, to identify a\nsmall number of semantic concepts. For a supervised concept extraction task on\nthe forum data, our best model achieved a macro $F_1$ score of 90\\%. A major\ndifficulty in medical concept extraction is obtaining labelled data from which\nto build supervised models. We investigate the utility of our models to\ntransfer to data derived from a different source in two ways. First for\nproducing labels via weak learning and second to perform concept extraction.\nThe dataset we use in this case comprises COVID-19 related tweets and we\nachieve an $F_1$ score 81\\% for symptom concept extraction trained on weakly\nlabelled data. The utility of our dictionaries is compared with a COVID-19\nsymptom dictionary that was constructed directly from Twitter. Further\nexperiments that incorporate BERT and a COVID-19 version of BERTweet\ndemonstrate that the dictionaries provide a commensurate result. Our results\nshow that incorporating small domain dictionaries to deep learning models can\nimprove concept extraction tasks. Moreover, models built using dictionaries\ngeneralize well and are transferable to different datasets on a similar task.\n", "date": "2023-09-05", "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": "2308.14429", "title": "Biomedical Entity Linking with Triple-aware Pre-Training", "abstract": "  Linking biomedical entities is an essential aspect in biomedical natural\nlanguage processing tasks, such as text mining and question answering. However,\na difficulty of linking the biomedical entities using current large language\nmodels (LLM) trained on a general corpus is that biomedical entities are\nscarcely distributed in texts and therefore have been rarely seen during\ntraining by the LLM. At the same time, those LLMs are not aware of high level\nsemantic connection between different biomedical entities, which are useful in\nidentifying similar concepts in different textual contexts. To cope with\naforementioned problems, some recent works focused on injecting knowledge graph\ninformation into LLMs. However, former methods either ignore the relational\nknowledge of the entities or lead to catastrophic forgetting. Therefore, we\npropose a novel framework to pre-train the powerful generative LLM by a corpus\nsynthesized from a KG. In the evaluations we are unable to confirm the benefit\nof including synonym, description or relational information.\n", "date": "2023-08-28", "categories": []}
{"id": "2207.08162", "title": "Natural language processing for clusterization of genes according to\n  their functions", "abstract": "  There are hundreds of methods for analysis of data obtained in\nmRNA-sequencing. The most of them are focused on small number of genes. In this\nstudy, we propose an approach that reduces the analysis of several thousand\ngenes to analysis of several clusters. The list of genes is enriched with\ninformation from open databases. Then, the descriptions are encoded as vectors\nusing the pretrained language model (BERT) and some text processing approaches.\nThe encoded gene function pass through the dimensionality reduction and\nclusterization. Aiming to find the most efficient pipeline, 180 cases of\npipeline with different methods in the major pipeline steps were analyzed. The\nperformance was evaluated with clusterization indexes and expert review of the\nresults.\n", "date": "2022-07-17", "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": "2308.11537", "title": "BELB: a Biomedical Entity Linking Benchmark", "abstract": "  Biomedical entity linking (BEL) is the task of grounding entity mentions to a\nknowledge base. It plays a vital role in information extraction pipelines for\nthe life sciences literature. We review recent work in the field and find that,\nas the task is absent from existing benchmarks for biomedical text mining,\ndifferent studies adopt different experimental setups making comparisons based\non published numbers problematic. Furthermore, neural systems are tested\nprimarily on instances linked to the broad coverage knowledge base UMLS,\nleaving their performance to more specialized ones, e.g. genes or variants,\nunderstudied. We therefore developed BELB, a Biomedical Entity Linking\nBenchmark, providing access in a unified format to 11 corpora linked to 7\nknowledge bases and spanning six entity types: gene, disease, chemical,\nspecies, cell line and variant. BELB greatly reduces preprocessing overhead in\ntesting BEL systems on multiple corpora offering a standardized testbed for\nreproducible experiments. Using BELB we perform an extensive evaluation of six\nrule-based entity-specific systems and three recent neural approaches\nleveraging pre-trained language models. Our results reveal a mixed picture\nshowing that neural approaches fail to perform consistently across entity\ntypes, highlighting the need of further studies towards entity-agnostic models.\n", "date": "2023-08-22", "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": "2308.08625", "title": "BIOptimus: Pre-training an Optimal Biomedical Language Model with\n  Curriculum Learning for Named Entity Recognition", "abstract": "  Using language models (LMs) pre-trained in a self-supervised setting on large\ncorpora and then fine-tuning for a downstream task has helped to deal with the\nproblem of limited label data for supervised learning tasks such as Named\nEntity Recognition (NER). Recent research in biomedical language processing has\noffered a number of biomedical LMs pre-trained using different methods and\ntechniques that advance results on many BioNLP tasks, including NER. However,\nthere is still a lack of a comprehensive comparison of pre-training approaches\nthat would work more optimally in the biomedical domain. This paper aims to\ninvestigate different pre-training methods, such as pre-training the biomedical\nLM from scratch and pre-training it in a continued fashion. We compare existing\nmethods with our proposed pre-training method of initializing weights for new\ntokens by distilling existing weights from the BERT model inside the context\nwhere the tokens were found. The method helps to speed up the pre-training\nstage and improve performance on NER. In addition, we compare how masking rate,\ncorruption strategy, and masking strategies impact the performance of the\nbiomedical LM. Finally, using the insights from our experiments, we introduce a\nnew biomedical LM (BIOptimus), which is pre-trained using Curriculum Learning\n(CL) and contextualized weight distillation method. Our model sets new states\nof the art on several biomedical Named Entity Recognition (NER) tasks. We\nrelease our code and all pre-trained models\n", "date": "2023-08-16", "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": "2308.06546", "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": "2308.06610", "title": "Bio-SIEVE: Exploring Instruction Tuning Large Language Models for\n  Systematic Review Automation", "abstract": "  Medical systematic reviews can be very costly and resource intensive. We\nexplore how Large Language Models (LLMs) can support and be trained to perform\nliterature screening when provided with a detailed set of selection criteria.\nSpecifically, we instruction tune LLaMA and Guanaco models to perform abstract\nscreening for medical systematic reviews. Our best model, Bio-SIEVE,\noutperforms both ChatGPT and trained traditional approaches, and generalises\nbetter across medical domains. However, there remains the challenge of adapting\nthe model to safety-first scenarios. We also explore the impact of multi-task\ntraining with Bio-SIEVE-Multi, including tasks such as PICO extraction and\nexclusion reasoning, but find that it is unable to match single-task\nBio-SIEVE's performance. We see Bio-SIEVE as an important step towards\nspecialising LLMs for the biomedical systematic review process and explore its\nfuture developmental opportunities. We release our models, code and a list of\nDOIs to reconstruct our dataset for reproducibility.\n", "date": "2023-08-12", "categories": []}
{"id": "2308.05609", "title": "LASIGE and UNICAGE solution to the NASA LitCoin NLP Competition", "abstract": "  Biomedical Natural Language Processing (NLP) tends to become cumbersome for\nmost researchers, frequently due to the amount and heterogeneity of text to be\nprocessed. To address this challenge, the industry is continuously developing\nhighly efficient tools and creating more flexible engineering solutions. This\nwork presents the integration between industry data engineering solutions for\nefficient data processing and academic systems developed for Named Entity\nRecognition (LasigeUnicage\\_NER) and Relation Extraction (BiOnt). Our design\nreflects an integration of those components with external knowledge in the form\nof additional training data from other datasets and biomedical ontologies. We\nused this pipeline in the 2022 LitCoin NLP Challenge, where our team\nLasigeUnicage was awarded the 7th Prize out of approximately 200 participating\nteams, reflecting a successful collaboration between the academia (LASIGE) and\nthe industry (Unicage). The software supporting this work is available at\n\\url{https://github.com/lasigeBioTM/Litcoin-Lasige_Unicage}.\n", "date": "2023-08-10", "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": "2308.04028", "title": "Top K Relevant Passage Retrieval for Biomedical Question Answering", "abstract": "  Question answering is a task that answers factoid questions using a large\ncollection of documents. It aims to provide precise answers in response to the\nuser's questions in natural language. Question answering relies on efficient\npassage retrieval to select candidate contexts, where traditional sparse vector\nspace models, such as TF-IDF or BM25, are the de facto method. On the web,\nthere is no single article that could provide all the possible answers\navailable on the internet to the question of the problem asked by the user. The\nexisting Dense Passage Retrieval model has been trained on Wikipedia dump from\nDec. 20, 2018, as the source documents for answering questions. Question\nanswering (QA) has made big strides with several open-domain and machine\ncomprehension systems built using large-scale annotated datasets. However, in\nthe clinical domain, this problem remains relatively unexplored. According to\nmultiple surveys, Biomedical Questions cannot be answered correctly from\nWikipedia Articles. In this work, we work on the existing DPR framework for the\nbiomedical domain and retrieve answers from the Pubmed articles which is a\nreliable source to answer medical questions. When evaluated on a BioASQ QA\ndataset, our fine-tuned dense retriever results in a 0.81 F1 score.\n", "date": "2023-08-08", "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": "2308.02569", "title": "BioBERT Based SNP-traits Associations Extraction from Biomedical\n  Literature", "abstract": "  Scientific literature contains a considerable amount of information that\nprovides an excellent opportunity for developing text mining methods to extract\nbiomedical relationships. An important type of information is the relationship\nbetween singular nucleotide polymorphisms (SNP) and traits. In this paper, we\npresent a BioBERT-GRU method to identify SNP- traits associations. Based on the\nevaluation of our method on the SNPPhenA dataset, it is concluded that this new\nmethod performs better than previous machine learning and deep learning based\nmethods. BioBERT-GRU achieved the result a precision of 0.883, recall of 0.882\nand F1-score of 0.881.\n", "date": "2023-08-03", "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": "2308.01727", "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": "2305.07804", "title": "Improving Small Language Models on PubMedQA via Generative Data\n  Augmentation", "abstract": "  Large Language Models (LLMs) have made remarkable advancements in the field\nof natural language processing. However, their increasing size poses challenges\nin terms of computational cost. On the other hand, Small Language Models (SLMs)\nare known for their efficiency, but they often struggle with limited capacity\nand training data, especially in specific domains. In this paper, we introduce\na novel method aimed at improving SLMs in the medical domain using LLM-based\ngenerative data augmentation. The objective of our approach is to develop more\nefficient and capable models that are specifically tailored for specialized\napplications. Through experiments conducted on the PubMedQA dataset, we\ndemonstrate the effectiveness of LLMs in refining and diversifying existing\nquestion-answer pairs. This refinement process leads to improved performance in\na significantly smaller model after fine-tuning. Notably, our best SLM, with\nunder 1.6 billion parameters, outperforms the few-shot GPT-4 on the PubMedQA\ndataset. Our code and generated data are publicly available to facilitate\nfurther explorations.\n", "date": "2023-05-12", "categories": []}
{"id": "2305.18624", "title": "W-procer: Weighted Prototypical Contrastive Learning for Medical\n  Few-Shot Named Entity Recognition", "abstract": "  Contrastive learning has become a popular solution for few-shot Name Entity\nRecognization (NER). The conventional configuration strives to reduce the\ndistance between tokens with the same labels and increase the distance between\ntokens with different labels. The effect of this setup may, however, in the\nmedical domain, there are a lot of entities annotated as OUTSIDE (O), and they\nare undesirably pushed apart to other entities that are not labeled as OUTSIDE\n(O) by the current contrastive learning method end up with a noisy prototype\nfor the semantic representation of the label, though there are many OUTSIDE (O)\nlabeled entities are relevant to the labeled entities. To address this\nchallenge, we propose a novel method named Weighted Prototypical Contrastive\nLearning for Medical Few Shot Named Entity Recognization (W-PROCER). Our\napproach primarily revolves around constructing the prototype-based contractive\nloss and weighting network. These components play a crucial role in assisting\nthe model in differentiating the negative samples from OUTSIDE (O) tokens and\nenhancing the discrimination ability of contrastive learning. Experimental\nresults show that our proposed W-PROCER framework significantly outperforms the\nstrong baselines on the three medical benchmark datasets.\n", "date": "2023-05-29", "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": "2308.00157", "title": "Boosting Adverse Drug Event Normalization on Social Media:\n  General-Purpose Model Initialization and Biomedical Semantic Text Similarity\n  Benefit Zero-Shot Linking in Informal Contexts", "abstract": "  Biomedical entity linking, also known as biomedical concept normalization,\nhas recently witnessed the rise to prominence of zero-shot contrastive models.\nHowever, the pre-training material used for these models has, until now,\nlargely consisted of specialist biomedical content such as MIMIC-III clinical\nnotes (Johnson et al., 2016) and PubMed papers (Sayers et al., 2021; Gao et\nal., 2020). While the resulting in-domain models have shown promising results\nfor many biomedical tasks, adverse drug event normalization on social media\ntexts has so far remained challenging for them (Portelli et al., 2022). In this\npaper, we propose a new approach for adverse drug event normalization on social\nmedia relying on general-purpose model initialization via BioLORD (Remy et al.,\n2022) and a semantic-text-similarity fine-tuning named STS. Our experimental\nresults on several social media datasets demonstrate the effectiveness of our\nproposed approach, by achieving state-of-the-art performance. Based on its\nstrong performance across all the tested datasets, we believe this work could\nemerge as a turning point for the task of adverse drug event normalization on\nsocial media and has the potential to serve as a benchmark for future research\nin the field.\n", "date": "2023-07-31", "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": "2307.13631", "title": "Contributions to the Improvement of Question Answering Systems in the\n  Biomedical Domain", "abstract": "  This thesis work falls within the framework of question answering (QA) in the\nbiomedical domain where several specific challenges are addressed, such as\nspecialized lexicons and terminologies, the types of treated questions, and the\ncharacteristics of targeted documents. We are particularly interested in\nstudying and improving methods that aim at finding accurate and short answers\nto biomedical natural language questions from a large scale of biomedical\ntextual documents in English. QA aims at providing inquirers with direct, short\nand precise answers to their natural language questions. In this Ph.D. thesis,\nwe propose four contributions to improve the performance of QA in the\nbiomedical domain. In our first contribution, we propose a machine\nlearning-based method for question type classification to determine the types\nof given questions which enable to a biomedical QA system to use the\nappropriate answer extraction method. We also propose an another machine\nlearning-based method to assign one or more topics (e.g., pharmacological,\ntest, treatment, etc.) to given questions in order to determine the semantic\ntypes of the expected answers which are very useful in generating specific\nanswer retrieval strategies. In the second contribution, we first propose a\ndocument retrieval method to retrieve a set of relevant documents that are\nlikely to contain the answers to biomedical questions from the MEDLINE\ndatabase. We then present a passage retrieval method to retrieve a set of\nrelevant passages to questions. In the third contribution, we propose specific\nanswer extraction methods to generate both exact and ideal answers. Finally, in\nthe fourth contribution, we develop a fully automated semantic biomedical QA\nsystem called SemBioNLQA which is able to deal with a variety of natural\nlanguage questions and to generate appropriate answers by providing both exact\nand ideal answers.\n", "date": "2023-07-25", "categories": []}
{"id": "2306.16108", "title": "Is ChatGPT a Biomedical Expert? -- Exploring the Zero-Shot Performance\n  of Current GPT Models in Biomedical Tasks", "abstract": "  We assessed the performance of commercial Large Language Models (LLMs)\nGPT-3.5-Turbo and GPT-4 on tasks from the 2023 BioASQ challenge. In Task 11b\nPhase B, which is focused on answer generation, both models demonstrated\ncompetitive abilities with leading systems. Remarkably, they achieved this with\nsimple zero-shot learning, grounded with relevant snippets. Even without\nrelevant snippets, their performance was decent, though not on par with the\nbest systems. Interestingly, the older and cheaper GPT-3.5-Turbo system was\nable to compete with GPT-4 in the grounded Q&A setting on factoid and list\nanswers. In Task 11b Phase A, focusing on retrieval, query expansion through\nzero-shot learning improved performance, but the models fell short compared to\nother systems. The code needed to rerun these experiments is available through\nGitHub.\n", "date": "2023-06-28", "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": "2305.01146", "title": "RadAdapt: Radiology Report Summarization via Lightweight Domain\n  Adaptation of Large Language Models", "abstract": "  We systematically investigate lightweight strategies to adapt large language\nmodels (LLMs) for the task of radiology report summarization (RRS).\nSpecifically, we focus on domain adaptation via pretraining (on natural\nlanguage, biomedical text, or clinical text) and via discrete prompting or\nparameter-efficient fine-tuning. Our results consistently achieve best\nperformance by maximally adapting to the task via pretraining on clinical text\nand fine-tuning on RRS examples. Importantly, this method fine-tunes a mere\n0.32% of parameters throughout the model, in contrast to end-to-end fine-tuning\n(100% of parameters). Additionally, we study the effect of in-context examples\nand out-of-distribution (OOD) training before concluding with a radiologist\nreader study and qualitative analysis. Our findings highlight the importance of\ndomain adaptation in RRS and provide valuable insights toward developing\neffective natural language processing solutions for clinical tasks.\n", "date": "2023-05-02", "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": "2307.08290", "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": "2304.08763", "title": "A Survey for Biomedical Text Summarization: From Pre-trained to Large\n  Language Models", "abstract": "  The exponential growth of biomedical texts such as biomedical literature and\nelectronic health records (EHRs), poses a significant challenge for clinicians\nand researchers to access clinical information efficiently. To tackle this\nchallenge, biomedical text summarization (BTS) has been proposed as a solution\nto support clinical information retrieval and management. BTS aims at\ngenerating concise summaries that distill key information from single or\nmultiple biomedical documents. In recent years, the rapid advancement of\nfundamental natural language processing (NLP) techniques, from pre-trained\nlanguage models (PLMs) to large language models (LLMs), has greatly facilitated\nthe progress of BTS. This growth has led to numerous proposed summarization\nmethods, datasets, and evaluation metrics, raising the need for a comprehensive\nand up-to-date survey for BTS. In this paper, we present a systematic review of\nrecent advancements in BTS, leveraging cutting-edge NLP techniques from PLMs to\nLLMs, to help understand the latest progress, challenges, and future\ndirections. We begin by introducing the foundational concepts of BTS, PLMs and\nLLMs, followed by an in-depth review of available datasets, recent approaches,\nand evaluation metrics in BTS. We finally discuss existing challenges and\npromising future directions in the era of LLMs. To facilitate the research\ncommunity, we line up open resources including available datasets, recent\napproaches, codes, evaluation metrics, and the leaderboard in a public project:\nhttps://github.com/KenZLuo/Biomedical-Text-Summarization-Survey/tree/master. We\nbelieve that this survey will be a useful resource to researchers, allowing\nthem to quickly track recent advancements and provide guidelines for future BTS\nresearch within the research community.\n", "date": "2023-04-18", "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": "2307.06439", "title": "Distilling Large Language Models for Biomedical Knowledge Extraction: A\n  Case Study on Adverse Drug Events", "abstract": "  Large language models (LLMs), such as GPT-4, have demonstrated remarkable\ncapabilities across a wide range of tasks, including health applications. In\nthis paper, we study how LLMs can be used to scale biomedical knowledge\ncuration. We find that while LLMs already possess decent competency in\nstructuring biomedical text, by distillation into a task-specific student model\nthrough self-supervised learning, substantial gains can be attained over\nout-of-box LLMs, with additional advantages such as cost, efficiency, and\nwhite-box model access.\n  We conduct a case study on adverse drug event (ADE) extraction, which is an\nimportant area for improving care. On standard ADE extraction evaluation, a\nGPT-3.5 distilled PubMedBERT model attained comparable accuracy as supervised\nstate-of-the-art models without using any labeled data. Despite being over\n1,000 times smaller, the distilled model outperformed its teacher GPT-3.5 by\nover 6 absolute points in F1 and GPT-4 by over 5 absolute points.\n  Ablation studies on distillation model choice (e.g., PubMedBERT vs BioGPT)\nand ADE extraction architecture shed light on best practice for biomedical\nknowledge extraction. Similar gains were attained by distillation for other\nstandard biomedical knowledge extraction tasks such as gene-disease\nassociations and protected health information, further illustrating the promise\nof this approach.\n", "date": "2023-07-12", "categories": []}
{"id": "2212.10422", "title": "Localising In-Domain Adaptation of Transformer-Based Biomedical Language\n  Models", "abstract": "  In the era of digital healthcare, the huge volumes of textual information\ngenerated every day in hospitals constitute an essential but underused asset\nthat could be exploited with task-specific, fine-tuned biomedical language\nrepresentation models, improving patient care and management. For such\nspecialized domains, previous research has shown that fine-tuning models\nstemming from broad-coverage checkpoints can largely benefit additional\ntraining rounds over large-scale in-domain resources. However, these resources\nare often unreachable for less-resourced languages like Italian, preventing\nlocal medical institutions to employ in-domain adaptation. In order to reduce\nthis gap, our work investigates two accessible approaches to derive biomedical\nlanguage models in languages other than English, taking Italian as a concrete\nuse-case: one based on neural machine translation of English resources,\nfavoring quantity over quality; the other based on a high-grade, narrow-scoped\ncorpus natively written in Italian, thus preferring quality over quantity. Our\nstudy shows that data quantity is a harder constraint than data quality for\nbiomedical adaptation, but the concatenation of high-quality data can improve\nmodel performance even when dealing with relatively size-limited corpora. The\nmodels published from our investigations have the potential to unlock important\nresearch opportunities for Italian hospitals and academia. Finally, the set of\nlessons learned from the study constitutes valuable insights towards a solution\nto build biomedical language models that are generalizable to other\nless-resourced languages and different domain settings.\n", "date": "2022-12-20", "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": "2306.17649", "title": "Biomedical Language Models are Robust to Sub-optimal Tokenization", "abstract": "  As opposed to general English, many concepts in biomedical terminology have\nbeen designed in recent history by biomedical professionals with the goal of\nbeing precise and concise. This is often achieved by concatenating meaningful\nbiomedical morphemes to create new semantic units. Nevertheless, most modern\nbiomedical language models (LMs) are pre-trained using standard domain-specific\ntokenizers derived from large scale biomedical corpus statistics without\nexplicitly leveraging the agglutinating nature of biomedical language. In this\nwork, we first find that standard open-domain and biomedical tokenizers are\nlargely unable to segment biomedical terms into meaningful components.\nTherefore, we hypothesize that using a tokenizer which segments biomedical\nterminology more accurately would enable biomedical LMs to improve their\nperformance on downstream biomedical NLP tasks, especially ones which involve\nbiomedical terms directly such as named entity recognition (NER) and entity\nlinking. Surprisingly, we find that pre-training a biomedical LM using a more\naccurate biomedical tokenizer does not improve the entity representation\nquality of a language model as measured by several intrinsic and extrinsic\nmeasures such as masked language modeling prediction (MLM) accuracy as well as\nNER and entity linking performance. These quantitative findings, along with a\ncase study which explores entity representation quality more directly, suggest\nthat the biomedical pre-training process is quite robust to instances of\nsub-optimal tokenization.\n", "date": "2023-06-30", "categories": []}
{"id": "2307.03859", "title": "MDACE: MIMIC Documents Annotated with Code Evidence", "abstract": "  We introduce a dataset for evidence/rationale extraction on an extreme\nmulti-label classification task over long medical documents. One such task is\nComputer-Assisted Coding (CAC) which has improved significantly in recent\nyears, thanks to advances in machine learning technologies. Yet simply\npredicting a set of final codes for a patient encounter is insufficient as CAC\nsystems are required to provide supporting textual evidence to justify the\nbilling codes. A model able to produce accurate and reliable supporting\nevidence for each code would be a tremendous benefit. However, a human\nannotated code evidence corpus is extremely difficult to create because it\nrequires specialized knowledge. In this paper, we introduce MDACE, the first\npublicly available code evidence dataset, which is built on a subset of the\nMIMIC-III clinical records. The dataset -- annotated by professional medical\ncoders -- consists of 302 Inpatient charts with 3,934 evidence spans and 52\nProfee charts with 5,563 evidence spans. We implemented several evidence\nextraction methods based on the EffectiveCAN model (Liu et al., 2021) to\nestablish baseline performance on this dataset. MDACE can be used to evaluate\ncode evidence extraction methods for CAC systems, as well as the accuracy and\ninterpretability of deep learning models for multi-label classification. We\nbelieve that the release of MDACE will greatly improve the understanding and\napplication of deep learning technologies for medical coding and document\nclassification.\n", "date": "2023-07-07", "categories": []}
{"id": "2307.04412", "title": "Enhancing Biomedical Text Summarization and Question-Answering: On the\n  Utility of Domain-Specific Pre-Training", "abstract": "  Biomedical summarization requires large datasets to train for text\ngeneration. We show that while transfer learning offers a viable option for\naddressing this challenge, an in-domain pre-training does not always offer\nadvantages in a BioASQ summarization task. We identify a suitable model\narchitecture and use it to show a benefit of a general-domain pre-training\nfollowed by a task-specific fine-tuning in the context of a BioASQ\nsummarization task, leading to a novel three-step fine-tuning approach that\nworks with only a thousand in-domain examples. Our results indicate that a\nLarge Language Model without domain-specific pre-training can have a\nsignificant edge in some domain-specific biomedical text generation tasks.\n", "date": "2023-07-10", "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": "2306.15736", "title": "DMNER: Biomedical Entity Recognition by Detection and Matching", "abstract": "  Biomedical named entity recognition (BNER) serves as the foundation for\nnumerous biomedical text mining tasks. Unlike general NER, BNER require a\ncomprehensive grasp of the domain, and incorporating external knowledge beyond\ntraining data poses a significant challenge. In this study, we propose a novel\nBNER framework called DMNER. By leveraging existing entity representation\nmodels SAPBERT, we tackle BNER as a two-step process: entity boundary detection\nand biomedical entity matching. DMNER exhibits applicability across multiple\nNER scenarios: 1) In supervised NER, we observe that DMNER effectively\nrectifies the output of baseline NER models, thereby further enhancing\nperformance. 2) In distantly supervised NER, combining MRC and AutoNER as span\nboundary detectors enables DMNER to achieve satisfactory results. 3) For\ntraining NER by merging multiple datasets, we adopt a framework similar to\nDS-NER but additionally leverage ChatGPT to obtain high-quality phrases in the\ntraining. Through extensive experiments conducted on 10 benchmark datasets, we\ndemonstrate the versatility and effectiveness of DMNER.\n", "date": "2023-06-27", "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": "2307.01137", "title": "Exploring the In-context Learning Ability of Large Language Model for\n  Biomedical Concept Linking", "abstract": "  The biomedical field relies heavily on concept linking in various areas such\nas literature mining, graph alignment, information retrieval,\nquestion-answering, data, and knowledge integration. Although large language\nmodels (LLMs) have made significant strides in many natural language processing\ntasks, their effectiveness in biomedical concept mapping is yet to be fully\nexplored. This research investigates a method that exploits the in-context\nlearning (ICL) capabilities of large models for biomedical concept linking. The\nproposed approach adopts a two-stage retrieve-and-rank framework. Initially,\nbiomedical concepts are embedded using language models, and then embedding\nsimilarity is utilized to retrieve the top candidates. These candidates'\ncontextual information is subsequently incorporated into the prompt and\nprocessed by a large language model to re-rank the concepts. This approach\nachieved an accuracy of 90.% in BC5CDR disease entity normalization and 94.7%\nin chemical entity normalization, exhibiting a competitive performance relative\nto supervised learning methods. Further, it showed a significant improvement,\nwith an over 20-point absolute increase in F1 score on an oncology matching\ndataset. Extensive qualitative assessments were conducted, and the benefits and\npotential shortcomings of using large language models within the biomedical\ndomain were discussed. were discussed.\n", "date": "2023-07-03", "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": "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": "2306.16275", "title": "Leveraging GPT-4 for Food Effect Summarization to Enhance\n  Product-Specific Guidance Development via Iterative Prompting", "abstract": "  Food effect summarization from New Drug Application (NDA) is an essential\ncomponent of product-specific guidance (PSG) development and assessment.\nHowever, manual summarization of food effect from extensive drug application\nreview documents is time-consuming, which arouses a need to develop automated\nmethods. Recent advances in large language models (LLMs) such as ChatGPT and\nGPT-4, have demonstrated great potential in improving the effectiveness of\nautomated text summarization, but its ability regarding the accuracy in\nsummarizing food effect for PSG assessment remains unclear. In this study, we\nintroduce a simple yet effective approach, iterative prompting, which allows\none to interact with ChatGPT or GPT-4 more effectively and efficiently through\nmulti-turn interaction. Specifically, we propose a three-turn iterative\nprompting approach to food effect summarization in which the keyword-focused\nand length-controlled prompts are respectively provided in consecutive turns to\nrefine the quality of the generated summary. We conduct a series of extensive\nevaluations, ranging from automated metrics to FDA professionals and even\nevaluation by GPT-4, on 100 NDA review documents selected over the past five\nyears. We observe that the summary quality is progressively improved throughout\nthe process. Moreover, we find that GPT-4 performs better than ChatGPT, as\nevaluated by FDA professionals (43% vs. 12%) and GPT-4 (64% vs. 35%).\nImportantly, all the FDA professionals unanimously rated that 85% of the\nsummaries generated by GPT-4 are factually consistent with the golden reference\nsummary, a finding further supported by GPT-4 rating of 72% consistency. These\nresults strongly suggest a great potential for GPT-4 to draft food effect\nsummaries that could be reviewed by FDA professionals, thereby improving the\nefficiency of PSG assessment cycle and promoting the generic drug product\ndevelopment.\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": "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": "2306.11189", "title": "BioREx: Improving Biomedical Relation Extraction by Leveraging\n  Heterogeneous Datasets", "abstract": "  Biomedical relation extraction (RE) is the task of automatically identifying\nand characterizing relations between biomedical concepts from free text. RE is\na central task in biomedical natural language processing (NLP) research and\nplays a critical role in many downstream applications, such as literature-based\ndiscovery and knowledge graph construction. State-of-the-art methods were used\nprimarily to train machine learning models on individual RE datasets, such as\nprotein-protein interaction and chemical-induced disease relation. Manual\ndataset annotation, however, is highly expensive and time-consuming, as it\nrequires domain knowledge. Existing RE datasets are usually domain-specific or\nsmall, which limits the development of generalized and high-performing RE\nmodels. In this work, we present a novel framework for systematically\naddressing the data heterogeneity of individual datasets and combining them\ninto a large dataset. Based on the framework and dataset, we report on BioREx,\na data-centric approach for extracting relations. Our evaluation shows that\nBioREx achieves significantly higher performance than the benchmark system\ntrained on the individual dataset, setting a new SOTA from 74.4% to 79.6% in\nF-1 measure on the recently released BioRED corpus. We further demonstrate that\nthe combined dataset can improve performance for five different RE tasks. In\naddition, we show that on average BioREx compares favorably to current\nbest-performing methods such as transfer learning and multi-task learning.\nFinally, we demonstrate BioREx's robustness and generalizability in two\nindependent RE tasks not previously seen in training data: drug-drug N-ary\ncombination and document-level gene-disease RE. The integrated dataset and\noptimized method have been packaged as a stand-alone tool available at\nhttps://github.com/ncbi/BioREx.\n", "date": "2023-06-19", "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": "2306.08403", "title": "Building a Corpus for Biomedical Relation Extraction of Species Mentions", "abstract": "  We present a manually annotated corpus, Species-Species Interaction, for\nextracting meaningful binary relations between species, in biomedical texts, at\nsentence level, with a focus on the gut microbiota. The corpus leverages\nPubTator to annotate species in full-text articles after evaluating different\nNamed Entity Recognition species taggers. Our first results are promising for\nextracting relations between species using BERT and its biomedical variants.\n", "date": "2023-06-14", "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": "2306.04926", "title": "covLLM: Large Language Models for COVID-19 Biomedical Literature", "abstract": "  The COVID-19 pandemic led to 1.1 million deaths in the United States, despite\nthe explosion of coronavirus research. These new findings are slow to translate\nto clinical interventions, leading to poorer patient outcomes and unnecessary\ndeaths. One reason is that clinicians, overwhelmed by patients, struggle to\nkeep pace with the rate of new coronavirus literature. A potential solution is\ndeveloping a tool for evaluating coronavirus literature using large language\nmodels (LLMs) -- neural networks that are deployed for natural language\nprocessing. LLMs can be used to summarize and extract user-specified\ninformation. The greater availability and advancement of LLMs and pre-processed\ncoronavirus literature databases provide the opportunity to assist clinicians\nin evaluating coronavirus literature through a coronavirus literature specific\nLLM (covLLM), a tool that directly takes an inputted research article and a\nuser query to return an answer. Using the COVID-19 Open Research Dataset\n(CORD-19), we produced two datasets: (1) synCovid, which uses a combination of\nhandwritten prompts and synthetic prompts generated using OpenAI, and (2) real\nabstracts, which contains abstract and title pairs. covLLM was trained with\nLLaMA 7B as a baseline model to produce three models trained on (1) the Alpaca\nand synCovid datasets, (2) the synCovid dataset, and (3) the synCovid and real\nabstract datasets. These models were evaluated by two human evaluators and\nChatGPT. Results demonstrate that training covLLM on the synCovid and abstract\npairs datasets performs competitively with ChatGPT and outperforms covLLM\ntrained primarily using the Alpaca dataset.\n", "date": "2023-06-08", "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": "2306.05317", "title": "CUED at ProbSum 2023: Hierarchical Ensemble of Summarization Models", "abstract": "  In this paper, we consider the challenge of summarizing patients' medical\nprogress notes in a limited data setting. For the Problem List Summarization\n(shared task 1A) at the BioNLP Workshop 2023, we demonstrate that Clinical-T5\nfine-tuned to 765 medical clinic notes outperforms other extractive,\nabstractive and zero-shot baselines, yielding reasonable baseline systems for\nmedical note summarization. Further, we introduce Hierarchical Ensemble of\nSummarization Models (HESM), consisting of token-level ensembles of diverse\nfine-tuned Clinical-T5 models, followed by Minimum Bayes Risk (MBR) decoding.\nOur HESM approach lead to a considerable summarization performance boost, and\nwhen evaluated on held-out challenge data achieved a ROUGE-L of 32.77, which\nwas the best-performing system at the top of the shared task leaderboard.\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": "2305.03501", "title": "Improving Cancer Hallmark Classification with BERT-based Deep Learning\n  Approach", "abstract": "  This paper presents a novel approach to accurately classify the hallmarks of\ncancer, which is a crucial task in cancer research. Our proposed method\nutilizes the Bidirectional Encoder Representations from Transformers (BERT)\narchitecture, which has shown exceptional performance in various downstream\napplications. By applying transfer learning, we fine-tuned the pre-trained BERT\nmodel on a small corpus of biomedical text documents related to cancer. The\noutcomes of our experimental investigations demonstrate that our approach\nattains a noteworthy accuracy of 94.45%, surpassing almost all prior findings\nwith a substantial increase of at least 8.04% as reported in the literature.\nThese findings highlight the effectiveness of our proposed model in accurately\nclassifying and comprehending text documents for cancer research, thus\ncontributing significantly to the field. As cancer remains one of the top ten\nleading causes of death globally, our approach holds great promise in advancing\ncancer research and improving patient outcomes.\n", "date": "2023-05-02", "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": "2303.10330", "title": "Exploring Partial Knowledge Base Inference in Biomedical Entity Linking", "abstract": "  Biomedical entity linking (EL) consists of named entity recognition (NER) and\nnamed entity disambiguation (NED). EL models are trained on corpora labeled by\na predefined KB. However, it is a common scenario that only entities within a\nsubset of the KB are precious to stakeholders. We name this scenario partial\nknowledge base inference: training an EL model with one KB and inferring on the\npart of it without further training. In this work, we give a detailed\ndefinition and evaluation procedures for this practically valuable but\nsignificantly understudied scenario and evaluate methods from three\nrepresentative EL paradigms. We construct partial KB inference benchmarks and\nwitness a catastrophic degradation in EL performance due to dramatically\nprecision drop. Our findings reveal these EL paradigms can not correctly handle\nunlinkable mentions (NIL), so they are not robust to partial KB inference. We\nalso propose two simple-and-effective redemption methods to combat the NIL\nissue with little computational overhead. Codes are released at\nhttps://github.com/Yuanhy1997/PartialKB-EL.\n", "date": "2023-03-18", "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": "2306.02042", "title": "Impact of translation on biomedical information extraction from\n  real-life clinical notes", "abstract": "  The objective of our study is to determine whether using English tools to\nextract and normalize French medical concepts on translations provides\ncomparable performance to French models trained on a set of annotated French\nclinical notes. We compare two methods: a method involving French language\nmodels and a method involving English language models. For the native French\nmethod, the Named Entity Recognition (NER) and normalization steps are\nperformed separately. For the translated English method, after the first\ntranslation step, we compare a two-step method and a terminology-oriented\nmethod that performs extraction and normalization at the same time. We used\nFrench, English and bilingual annotated datasets to evaluate all steps (NER,\nnormalization and translation) of our algorithms. Concerning the results, the\nnative French method performs better than the translated English one with a\nglobal f1 score of 0.51 [0.47;0.55] against 0.39 [0.34;0.44] and 0.38\n[0.36;0.40] for the two English methods tested. In conclusion, despite the\nrecent improvement of the translation models, there is a significant\nperformance difference between the two approaches in favor of the native French\nmethod which is more efficient on French medical texts, even with few annotated\ndocuments.\n", "date": "2023-06-03", "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": "2306.01245", "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": "2306.00005", "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": "2305.19120", "title": "Comparing and combining some popular NER approaches on Biomedical tasks", "abstract": "  We compare three simple and popular approaches for NER: 1) SEQ\n(sequence-labeling with a linear token classifier) 2) SeqCRF (sequence-labeling\nwith Conditional Random Fields), and 3) SpanPred (span-prediction with boundary\ntoken embeddings). We compare the approaches on 4 biomedical NER tasks: GENIA,\nNCBI-Disease, LivingNER (Spanish), and SocialDisNER (Spanish). The SpanPred\nmodel demonstrates state-of-the-art performance on LivingNER and SocialDisNER,\nimproving F1 by 1.3 and 0.6 F1 respectively. The SeqCRF model also demonstrates\nstate-of-the-art performance on LivingNER and SocialDisNER, improving F1 by 0.2\nF1 and 0.7 respectively. The SEQ model is competitive with the state-of-the-art\non the LivingNER dataset. We explore some simple ways of combining the three\napproaches. We find that majority voting consistently gives high precision and\nhigh F1 across all 4 datasets. Lastly, we implement a system that learns to\ncombine the predictions of SEQ and SpanPred, generating systems that\nconsistently give high recall and high F1 across all 4 datasets. On the GENIA\ndataset, we find that our learned combiner system significantly boosts F1(+1.2)\nand recall(+2.1) over the systems being combined. We release all the\nwell-documented code necessary to reproduce all systems at\nhttps://github.com/flyingmothman/bionlp.\n", "date": "2023-05-30", "categories": []}
{"id": "2305.17364", "title": "An Investigation of Evaluation Metrics for Automated Medical Note\n  Generation", "abstract": "  Recent studies on automatic note generation have shown that doctors can save\nsignificant amounts of time when using automatic clinical note generation\n(Knoll et al., 2022). Summarization models have been used for this task to\ngenerate clinical notes as summaries of doctor-patient conversations (Krishna\net al., 2021; Cai et al., 2022). However, assessing which model would best\nserve clinicians in their daily practice is still a challenging task due to the\nlarge set of possible correct summaries, and the potential limitations of\nautomatic evaluation metrics. In this paper, we study evaluation methods and\nmetrics for the automatic generation of clinical notes from medical\nconversations. In particular, we propose new task-specific metrics and we\ncompare them to SOTA evaluation metrics in text summarization and generation,\nincluding: (i) knowledge-graph embedding-based metrics, (ii) customized\nmodel-based metrics, (iii) domain-adapted/fine-tuned metrics, and (iv) ensemble\nmetrics. To study the correlation between the automatic metrics and manual\njudgments, we evaluate automatic notes/summaries by comparing the system and\nreference facts and computing the factual correctness, and the hallucination\nand omission rates for critical medical facts. This study relied on seven\ndatasets manually annotated by domain experts. Our experiments show that\nautomatic evaluation metrics can have substantially different behaviors on\ndifferent types of clinical notes datasets. However, the results highlight one\nstable subset of metrics as the most correlated with human judgments with a\nrelevant aggregation of different evaluation criteria.\n", "date": "2023-05-27", "categories": []}
{"id": "2305.18152", "title": "Extrinsic Factors Affecting the Accuracy of Biomedical NER", "abstract": "  Biomedical named entity recognition (NER) is a critial task that aims to\nidentify structured information in clinical text, which is often replete with\ncomplex, technical terms and a high degree of variability. Accurate and\nreliable NER can facilitate the extraction and analysis of important biomedical\ninformation, which can be used to improve downstream applications including the\nhealthcare system. However, NER in the biomedical domain is challenging due to\nlimited data availability, as the high expertise, time, and expenses are\nrequired to annotate its data. In this paper, by using the limited data, we\nexplore various extrinsic factors including the corpus annotation scheme, data\naugmentation techniques, semi-supervised learning and Brill transformation, to\nimprove the performance of a NER model on a clinical text dataset (i2b2 2012,\n\\citet{sun-rumshisky-uzuner:2013}). Our experiments demonstrate that these\napproaches can significantly improve the model's F1 score from original 73.74\nto 77.55. Our findings suggest that considering different extrinsic factors and\ncombining these techniques is a promising approach for improving NER\nperformance in the biomedical domain where the size of data is limited.\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": "2305.14645", "title": "Iteratively Improving Biomedical Entity Linking and Event Extraction via\n  Hard Expectation-Maximization", "abstract": "  Biomedical entity linking and event extraction are two crucial tasks to\nsupport text understanding and retrieval in the biomedical domain. These two\ntasks intrinsically benefit each other: entity linking disambiguates the\nbiomedical concepts by referring to external knowledge bases and the domain\nknowledge further provides additional clues to understand and extract the\nbiological processes, while event extraction identifies a key trigger and\nentities involved to describe each biological process which also captures the\nstructural context to better disambiguate the biomedical entities. However,\nprevious research typically solves these two tasks separately or in a pipeline,\nleading to error propagation. What's more, it's even more challenging to solve\nthese two tasks together as there is no existing dataset that contains\nannotations for both tasks. To solve these challenges, we propose joint\nbiomedical entity linking and event extraction by regarding the event\nstructures and entity references in knowledge bases as latent variables and\nupdating the two task-specific models in a hard Expectation-Maximization (EM)\nfashion: (1) predicting the missing variables for each partially annotated\ndataset based on the current two task-specific models, and (2) updating the\nparameters of each model on the corresponding pseudo completed dataset.\nExperimental results on two benchmark datasets: Genia 2011 for event extraction\nand BC4GO for entity linking, show that our joint framework significantly\nimproves the model for each individual task and outperforms the strong\nbaselines for both tasks. We will make the code and model checkpoints publicly\navailable once the paper is accepted.\n", "date": "2023-05-24", "categories": []}
{"id": "2305.15010", "title": "Injecting Knowledge into Biomedical Pre-trained Models via Polymorphism\n  and Synonymous Substitution", "abstract": "  Pre-trained language models (PLMs) were considered to be able to store\nrelational knowledge present in the training data. However, some relational\nknowledge seems to be discarded unsafely in PLMs due to \\textbf{report bias}:\nlow-frequency relational knowledge might be underexpressed compared to\nhigh-frequency one in PLMs. This gives us a hint that relational knowledge\nmight not be redundant to the stored knowledge of PLMs, but rather be\ncomplementary. To additionally inject relational knowledge into PLMs, we\npropose a simple-yet-effective approach to inject relational knowledge into\nPLMs, which is inspired by three observations (namely, polymorphism, synonymous\nsubstitution, and association). In particular, we switch entities in the\ntraining corpus to related entities (either hypernyms/hyponyms/synonyms, or\narbitrarily-related concepts). Experimental results show that the proposed\napproach could not only better capture relational knowledge, but also improve\nthe performance in various biomedical downstream tasks. Our model is available\nin \\url{https://github.com/StevenZHB/BioPLM_InjectingKnowledge}.\n", "date": "2023-05-24", "categories": []}
{"id": "2305.15222", "title": "Neural Summarization of Electronic Health Records", "abstract": "  Hospital discharge documentation is among the most essential, yet\ntime-consuming documents written by medical practitioners. The objective of\nthis study was to automatically generate hospital discharge summaries using\nneural network summarization models. We studied various data preparation and\nneural network training techniques that generate discharge summaries. Using\nnursing notes and discharge summaries from the MIMIC-III dataset, we studied\nthe viability of the automatic generation of various sections of a discharge\nsummary using four state-of-the-art neural network summarization models (BART,\nT5, Longformer and FLAN-T5). Our experiments indicated that training\nenvironments including nursing notes as the source, and discrete sections of\nthe discharge summary as the target output (e.g. \"History of Present Illness\")\nimprove language model efficiency and text quality. According to our findings,\nthe fine-tuned BART model improved its ROUGE F1 score by 43.6% against its\nstandard off-the-shelf version. We also found that fine-tuning the baseline\nBART model with other setups caused different degrees of improvement (up to 80%\nrelative improvement). We also observed that a fine-tuned T5 generally achieves\nhigher ROUGE F1 scores than other fine-tuned models and a fine-tuned FLAN-T5\nachieves the highest ROUGE score overall, i.e., 45.6. For majority of the\nfine-tuned language models, summarizing discharge summary report sections\nseparately outperformed the summarization the entire report quantitatively. On\nthe other hand, fine-tuning language models that were previously instruction\nfine-tuned showed better performance in summarizing entire reports. This study\nconcludes that a focused dataset designed for the automatic generation of\ndischarge summaries by a language model can produce coherent Discharge Summary\nsections.\n", "date": "2023-05-24", "categories": []}
{"id": "2305.13317", "title": "A Novel Dataset Towards Extracting Virus-Host Interactions", "abstract": "  We describe a novel dataset for the automated recognition of named taxonomic\nand other entities relevant to the association of viruses with their hosts. We\nfurther describe some initial results using pre-trained models on the\nnamed-entity recognition (NER) task on this novel dataset. We propose that our\ndataset of manually annotated abstracts now offers a Gold Standard Corpus for\ntraining future NER models in the automated extraction of host-pathogen\ndetection methods from scientific publications, and further explain how our\nwork makes first steps towards predicting the important human health-related\nconcept of viral spillover risk automatically from the scientific literature.\n", "date": "2023-05-11", "categories": []}
{"id": "2305.13693", "title": "Automated Metrics for Medical Multi-Document Summarization Disagree with\n  Human Evaluations", "abstract": "  Evaluating multi-document summarization (MDS) quality is difficult. This is\nespecially true in the case of MDS for biomedical literature reviews, where\nmodels must synthesize contradicting evidence reported across different\ndocuments. Prior work has shown that rather than performing the task, models\nmay exploit shortcuts that are difficult to detect using standard n-gram\nsimilarity metrics such as ROUGE. Better automated evaluation metrics are\nneeded, but few resources exist to assess metrics when they are proposed.\nTherefore, we introduce a dataset of human-assessed summary quality facets and\npairwise preferences to encourage and support the development of better\nautomated evaluation methods for literature review MDS. We take advantage of\ncommunity submissions to the Multi-document Summarization for Literature Review\n(MSLR) shared task to compile a diverse and representative sample of generated\nsummaries. We analyze how automated summarization evaluation metrics correlate\nwith lexical features of generated summaries, to other automated metrics\nincluding several we propose in this work, and to aspects of human-assessed\nsummary quality. We find that not only do automated metrics fail to capture\naspects of quality as assessed by humans, in many cases the system rankings\nproduced by these metrics are anti-correlated with rankings according to human\nannotators.\n", "date": "2023-05-23", "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": "2305.10647", "title": "BioAug: Conditional Generation based Data Augmentation for Low-Resource\n  Biomedical NER", "abstract": "  Biomedical Named Entity Recognition (BioNER) is the fundamental task of\nidentifying named entities from biomedical text. However, BioNER suffers from\nsevere data scarcity and lacks high-quality labeled data due to the highly\nspecialized and expert knowledge required for annotation. Though data\naugmentation has shown to be highly effective for low-resource NER in general,\nexisting data augmentation techniques fail to produce factual and diverse\naugmentations for BioNER. In this paper, we present BioAug, a novel data\naugmentation framework for low-resource BioNER. BioAug, built on BART, is\ntrained to solve a novel text reconstruction task based on selective masking\nand knowledge augmentation. Post training, we perform conditional generation\nand generate diverse augmentations conditioning BioAug on selectively corrupted\ntext similar to the training stage. We demonstrate the effectiveness of BioAug\non 5 benchmark BioNER datasets and show that BioAug outperforms all our\nbaselines by a significant margin (1.5%-21.5% absolute improvement) and is able\nto generate augmentations that are both more factual and diverse. Code:\nhttps://github.com/Sreyan88/BioAug.\n", "date": "2023-05-18", "categories": []}
{"id": "2211.16944", "title": "AIONER: All-in-one scheme-based biomedical named entity recognition\n  using deep learning", "abstract": "  Biomedical named entity recognition (BioNER) seeks to automatically recognize\nbiomedical entities in natural language text, serving as a necessary foundation\nfor downstream text mining tasks and applications such as information\nextraction and question answering. Manually labeling training data for the\nBioNER task is costly, however, due to the significant domain expertise\nrequired for accurate annotation. The resulting data scarcity causes current\nBioNER approaches to be prone to overfitting, to suffer from limited\ngeneralizability, and to address a single entity type at a time (e.g., gene or\ndisease). We therefore propose a novel all-in-one (AIO) scheme that uses\nexternal data from existing annotated resources to enhance the accuracy and\nstability of BioNER models. We further present AIONER, a general-purpose BioNER\ntool based on cutting-edge deep learning and our AIO schema. We evaluate AIONER\non 14 BioNER benchmark tasks and show that AIONER is effective, robust, and\ncompares favorably to other state-of-the-art approaches such as multi-task\nlearning. We further demonstrate the practical utility of AIONER in three\nindependent tasks to recognize entity types not previously seen in training\ndata, as well as the advantages of AIONER over existing methods for processing\nbiomedical text at a large scale (e.g., the entire PubMed data).\n", "date": "2022-11-30", "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": "2305.08787", "title": "Comparing Variation in Tokenizer Outputs Using a Series of Problematic\n  and Challenging Biomedical Sentences", "abstract": "  Background & Objective: Biomedical text data are increasingly available for\nresearch. Tokenization is an initial step in many biomedical text mining\npipelines. Tokenization is the process of parsing an input biomedical sentence\n(represented as a digital character sequence) into a discrete set of word/token\nsymbols, which convey focused semantic/syntactic meaning. The objective of this\nstudy is to explore variation in tokenizer outputs when applied across a series\nof challenging biomedical sentences.\n  Method: Diaz [2015] introduce 24 challenging example biomedical sentences for\ncomparing tokenizer performance. In this study, we descriptively explore\nvariation in outputs of eight tokenizers applied to each example biomedical\nsentence. The tokenizers compared in this study are the NLTK white space\ntokenizer, the NLTK Penn Tree Bank tokenizer, Spacy and SciSpacy tokenizers,\nStanza/Stanza-Craft tokenizers, the UDPipe tokenizer, and R-tokenizers.\n  Results: For many examples, tokenizers performed similarly effectively;\nhowever, for certain examples, there were meaningful variation in returned\noutputs. The white space tokenizer often performed differently than other\ntokenizers. We observed performance similarities for tokenizers implementing\nrule-based systems (e.g. pattern matching and regular expressions) and\ntokenizers implementing neural architectures for token classification.\nOftentimes, the challenging tokens resulting in the greatest variation in\noutputs, are those words which convey substantive and focused\nbiomedical/clinical meaning (e.g. x-ray, IL-10, TCR/CD3, CD4+ CD8+, and\n(Ca2+)-regulated).\n  Conclusion: When state-of-the-art, open-source tokenizers from Python and R\nwere applied to a series of challenging biomedical example sentences, we\nobserved subtle variation in the returned outputs.\n", "date": "2023-05-15", "categories": []}
{"id": "2305.07468", "title": "BactInt: A domain driven transfer learning approach and a corpus for\n  extracting inter-bacterial interactions from biomedical text", "abstract": "  The community of different types of microbes present in a biological niche\nplays a very important role in functioning of the system. The crosstalk or\ninteractions among the different microbes contributes to the building blocks of\nsuch microbial community structures. Evidence reported in biomedical text\nserves as a reliable source for predicting such interactions. However, going\nthrough the vast and ever-increasing volume of biomedical literature is an\nintimidating and time consuming process. This necessitates development of\nautomated methods capable of accurately extracting bacterial relations reported\nin biomedical literature. In this paper, we introduce a method for automated\nextraction of microbial interactions (specifically between bacteria) from\nbiomedical literature along with ways of using transfer learning to improve its\naccuracy. We also describe a pipeline using which relations among specific\nbacteria groups can be mined. Additionally, we introduce the first publicly\navailable dataset which can be used to develop bacterial interaction extraction\nmethods.\n", "date": "2023-04-27", "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": "2305.06299", "title": "Summarizing, Simplifying, and Synthesizing Medical Evidence Using GPT-3\n  (with Varying Success)", "abstract": "  Large language models, particularly GPT-3, are able to produce high quality\nsummaries of general domain news articles in few- and zero-shot settings.\nHowever, it is unclear if such models are similarly capable in more\nspecialized, high-stakes domains such as biomedicine. In this paper, we enlist\ndomain experts (individuals with medical training) to evaluate summaries of\nbiomedical articles generated by GPT-3, given zero supervision. We consider\nboth single- and multi-document settings. In the former, GPT-3 is tasked with\ngenerating regular and plain-language summaries of articles describing\nrandomized controlled trials; in the latter, we assess the degree to which\nGPT-3 is able to \\emph{synthesize} evidence reported across a collection of\narticles. We design an annotation scheme for evaluating model outputs, with an\nemphasis on assessing the factual accuracy of generated summaries. We find that\nwhile GPT-3 is able to summarize and simplify single biomedical articles\nfaithfully, it struggles to provide accurate aggregations of findings over\nmultiple documents. We release all data and annotations used in this work.\n", "date": "2023-05-10", "categories": []}
{"id": "2305.06416", "title": "A Method to Automate the Discharge Summary Hospital Course for Neurology\n  Patients", "abstract": "  Generation of automated clinical notes have been posited as a strategy to\nmitigate physician burnout. In particular, an automated narrative summary of a\npatient's hospital stay could supplement the hospital course section of the\ndischarge summary that inpatient physicians document in electronic health\nrecord (EHR) systems. In the current study, we developed and evaluated an\nautomated method for summarizing the hospital course section using\nencoder-decoder sequence-to-sequence transformer models. We fine tuned BERT and\nBART models and optimized for factuality through constraining beam search,\nwhich we trained and tested using EHR data from patients admitted to the\nneurology unit of an academic medical center. The approach demonstrated good\nROUGE scores with an R-2 of 13.76. In a blind evaluation, two board-certified\nphysicians rated 62% of the automated summaries as meeting the standard of\ncare, which suggests the method may be useful clinically. To our knowledge,\nthis study is among the first to demonstrate an automated method for generating\na discharge summary hospital course that approaches a quality level of what a\nphysician would write.\n", "date": "2023-05-10", "categories": []}
{"id": "2305.06801", "title": "Detecting Idiomatic Multiword Expressions in Clinical Terminology using\n  Definition-Based Representation Learning", "abstract": "  This paper shines a light on the potential of definition-based semantic\nmodels for detecting idiomatic and semi-idiomatic multiword expressions (MWEs)\nin clinical terminology. Our study focuses on biomedical entities defined in\nthe UMLS ontology and aims to help prioritize the translation efforts of these\nentities. In particular, we develop an effective tool for scoring the\nidiomaticity of biomedical MWEs based on the degree of similarity between the\nsemantic representations of those MWEs and a weighted average of the\nrepresentation of their constituents. We achieve this using a biomedical\nlanguage model trained to produce similar representations for entity names and\ntheir definitions, called BioLORD. The importance of this definition-based\napproach is highlighted by comparing the BioLORD model to two other\nstate-of-the-art biomedical language models based on Transformer: SapBERT and\nCODER. Our results show that the BioLORD model has a strong ability to identify\nidiomatic MWEs, not replicated in other models. Our corpus-free idiomaticity\nestimation helps ontology translators to focus on more challenging MWEs.\n", "date": "2023-05-11", "categories": []}
{"id": "2305.05982", "title": "Generating medically-accurate summaries of patient-provider dialogue: A\n  multi-stage approach using large language models", "abstract": "  A medical provider's summary of a patient visit serves several critical\npurposes, including clinical decision-making, facilitating hand-offs between\nproviders, and as a reference for the patient. An effective summary is required\nto be coherent and accurately capture all the medically relevant information in\nthe dialogue, despite the complexity of patient-generated language. Even minor\ninaccuracies in visit summaries (for example, summarizing \"patient does not\nhave a fever\" when a fever is present) can be detrimental to the outcome of\ncare for the patient.\n  This paper tackles the problem of medical conversation summarization by\ndiscretizing the task into several smaller dialogue-understanding tasks that\nare sequentially built upon. First, we identify medical entities and their\naffirmations within the conversation to serve as building blocks. We study\ndynamically constructing few-shot prompts for tasks by conditioning on relevant\npatient information and use GPT-3 as the backbone for our experiments. We also\ndevelop GPT-derived summarization metrics to measure performance against\nreference summaries quantitatively. Both our human evaluation study and metrics\nfor medical correctness show that summaries generated using this approach are\nclinically accurate and outperform the baseline approach of summarizing the\ndialog in a zero-shot, single-prompt setting.\n", "date": "2023-05-10", "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": "2305.04344", "title": "Empowering Language Model with Guided Knowledge Fusion for Biomedical\n  Document Re-ranking", "abstract": "  Pre-trained language models (PLMs) have proven to be effective for document\nre-ranking task. However, they lack the ability to fully interpret the\nsemantics of biomedical and health-care queries and often rely on simplistic\npatterns for retrieving documents. To address this challenge, we propose an\napproach that integrates knowledge and the PLMs to guide the model toward\neffectively capturing information from external sources and retrieving the\ncorrect documents. We performed comprehensive experiments on two biomedical and\nopen-domain datasets that show that our approach significantly improves vanilla\nPLMs and other existing approaches for document re-ranking task.\n", "date": "2023-05-07", "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": "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": "2304.11794", "title": "FineEHR: Refine Clinical Note Representations to Improve Mortality\n  Prediction", "abstract": "  Monitoring the health status of patients in the Intensive Care Unit (ICU) is\na critical aspect of providing superior care and treatment. The availability of\nlarge-scale electronic health records (EHR) provides machine learning models\nwith an abundance of clinical text and vital sign data, enabling them to make\nhighly accurate predictions. Despite the emergence of advanced Natural Language\nProcessing (NLP) algorithms for clinical note analysis, the complex textual\nstructure and noise present in raw clinical data have posed significant\nchallenges. Coarse embedding approaches without domain-specific refinement have\nlimited the accuracy of these algorithms. To address this issue, we propose\nFINEEHR, a system that utilizes two representation learning techniques, namely\nmetric learning and fine-tuning, to refine clinical note embeddings, while\nleveraging the intrinsic correlations among different health statuses and note\ncategories. We evaluate the performance of FINEEHR using two metrics, namely\nArea Under the Curve (AUC) and AUC-PR, on a real-world MIMIC III dataset. Our\nexperimental results demonstrate that both refinement approaches improve\nprediction accuracy, and their combination yields the best results. Moreover,\nour proposed method outperforms prior works, with an AUC improvement of over\n10%, achieving an average AUC of 96.04% and an average AUC-PR of 96.48% across\nvarious classifiers.\n", "date": "2023-04-24", "categories": []}
{"id": "2305.02104", "title": "Background Knowledge Grounding for Readable, Relevant, and Factual\n  Biomedical Lay Summaries", "abstract": "  Communication of scientific findings to the public is important for keeping\nnon-experts informed of developments such as life-saving medical treatments.\nHowever, generating readable lay summaries from scientific documents is\nchallenging, and currently, these summaries suffer from critical factual\nerrors. One popular intervention for improving factuality is using additional\nexternal knowledge to provide factual grounding. However, it is unclear how\nthese grounding sources should be retrieved, selected, or integrated, and how\nsupplementary grounding documents might affect the readability or relevance of\nthe generated summaries. We develop a simple method for selecting grounding\nsources and integrating them with source documents. We then use the BioLaySum\nsummarization dataset to evaluate the effects of different grounding sources on\nsummary quality. We found that grounding source documents improves the\nrelevance and readability of lay summaries but does not improve factuality of\nlay summaries. This continues to be true in zero-shot summarization settings\nwhere we hypothesized that grounding might be even more important for factual\nlay summaries.\n", "date": "2023-05-03", "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": "2305.01526", "title": "Huatuo-26M, a Large-scale Chinese Medical QA Dataset", "abstract": "  In this paper, we release a largest ever medical Question Answering (QA)\ndataset with 26 million QA pairs. We benchmark many existing approaches in our\ndataset in terms of both retrieval and generation. Experimental results show\nthat the existing models perform far lower than expected and the released\ndataset is still challenging in the pre-trained language model era. Moreover,\nwe also experimentally show the benefit of the proposed dataset in many\naspects: (i) trained models for other QA datasets in a zero-shot fashion; and\n(ii) as external knowledge for retrieval-augmented generation (RAG); and (iii)\nimproving existing pre-trained language models by using the QA pairs as a\npre-training corpus in continued training manner. We believe that this dataset\nwill not only contribute to medical research but also facilitate both the\npatients and clinical doctors. See\n\\url{https://github.com/FreedomIntelligence/Huatuo-26M}.\n", "date": "2023-05-02", "categories": []}
{"id": "2210.04705", "title": "Readability Controllable Biomedical Document Summarization", "abstract": "  Different from general documents, it is recognised that the ease with which\npeople can understand a biomedical text is eminently varied, owing to the\nhighly technical nature of biomedical documents and the variance of readers'\ndomain knowledge. However, existing biomedical document summarization systems\nhave paid little attention to readability control, leaving users with summaries\nthat are incompatible with their levels of expertise. In recognition of this\nurgent demand, we introduce a new task of readability controllable\nsummarization for biomedical documents, which aims to recognise users'\nreadability demands and generate summaries that better suit their needs:\ntechnical summaries for experts and plain language summaries (PLS) for laymen.\nTo establish this task, we construct a corpus consisting of biomedical papers\nwith technical summaries and PLSs written by the authors, and benchmark\nmultiple advanced controllable abstractive and extractive summarization models\nbased on pre-trained language models (PLMs) with prevalent controlling and\ngeneration techniques. Moreover, we propose a novel masked language model (MLM)\nbased metric and its variant to effectively evaluate the readability\ndiscrepancy between lay and technical summaries. Experimental results from\nautomated and human evaluations show that though current control techniques\nallow for a certain degree of readability adjustment during generation, the\nperformance of existing controllable summarization methods is far from\ndesirable in this task.\n", "date": "2022-10-10", "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": "2304.10909", "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": "2304.10996", "title": "BERT Based Clinical Knowledge Extraction for Biomedical Knowledge Graph\n  Construction and Analysis", "abstract": "  Background : Knowledge is evolving over time, often as a result of new\ndiscoveries or changes in the adopted methods of reasoning. Also, new facts or\nevidence may become available, leading to new understandings of complex\nphenomena. This is particularly true in the biomedical field, where scientists\nand physicians are constantly striving to find new methods of diagnosis,\ntreatment and eventually cure. Knowledge Graphs (KGs) offer a real way of\norganizing and retrieving the massive and growing amount of biomedical\nknowledge.\n  Objective : We propose an end-to-end approach for knowledge extraction and\nanalysis from biomedical clinical notes using the Bidirectional Encoder\nRepresentations from Transformers (BERT) model and Conditional Random Field\n(CRF) layer.\n  Methods : The approach is based on knowledge graphs, which can effectively\nprocess abstract biomedical concepts such as relationships and interactions\nbetween medical entities. Besides offering an intuitive way to visualize these\nconcepts, KGs can solve more complex knowledge retrieval problems by\nsimplifying them into simpler representations or by transforming the problems\ninto representations from different perspectives. We created a biomedical\nKnowledge Graph using using Natural Language Processing models for named entity\nrecognition and relation extraction. The generated biomedical knowledge graphs\n(KGs) are then used for question answering.\n  Results : The proposed framework can successfully extract relevant structured\ninformation with high accuracy (90.7% for Named-entity recognition (NER), 88%\nfor relation extraction (RE)), according to experimental findings based on\nreal-world 505 patient biomedical unstructured clinical notes.\n  Conclusions : In this paper, we propose a novel end-to-end system for the\nconstruction of a biomedical knowledge graph from clinical textual using a\nvariation of BERT models.\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": "2304.06975", "title": "HuaTuo: Tuning LLaMA Model with Chinese Medical Knowledge", "abstract": "  Large Language Models (LLMs), such as the LLaMA model, have demonstrated\ntheir effectiveness in various general-domain natural language processing (NLP)\ntasks. Nevertheless, LLMs have not yet performed optimally in biomedical domain\ntasks due to the need for medical expertise in the responses. In response to\nthis challenge, we propose HuaTuo, a LLaMA-based model that has been\nsupervised-fine-tuned with generated QA (Question-Answer) instances. The\nexperimental results demonstrate that HuaTuo generates responses that possess\nmore reliable medical knowledge. Our proposed HuaTuo model is accessible at\nhttps://github.com/SCIR-HI/Huatuo-Llama-Med-Chinese.\n", "date": "2023-04-14", "categories": []}
{"id": "2303.13375", "title": "Capabilities of GPT-4 on Medical Challenge Problems", "abstract": "  Large language models (LLMs) have demonstrated remarkable capabilities in\nnatural language understanding and generation across various domains, including\nmedicine. We present a comprehensive evaluation of GPT-4, a state-of-the-art\nLLM, on medical competency examinations and benchmark datasets. GPT-4 is a\ngeneral-purpose model that is not specialized for medical problems through\ntraining or engineered to solve clinical tasks. Our analysis covers two sets of\nofficial practice materials for the USMLE, a three-step examination program\nused to assess clinical competency and grant licensure in the United States. We\nalso evaluate performance on the MultiMedQA suite of benchmark datasets. Beyond\nmeasuring model performance, experiments were conducted to investigate the\ninfluence of test questions containing both text and images on model\nperformance, probe for memorization of content during training, and study\nprobability calibration, which is of critical importance in high-stakes\napplications like medicine. Our results show that GPT-4, without any\nspecialized prompt crafting, exceeds the passing score on USMLE by over 20\npoints and outperforms earlier general-purpose models (GPT-3.5) as well as\nmodels specifically fine-tuned on medical knowledge (Med-PaLM, a prompt-tuned\nversion of Flan-PaLM 540B). In addition, GPT-4 is significantly better\ncalibrated than GPT-3.5, demonstrating a much-improved ability to predict the\nlikelihood that its answers are correct. We also explore the behavior of the\nmodel qualitatively through a case study that shows the ability of GPT-4 to\nexplain medical reasoning, personalize explanations to students, and\ninteractively craft new counterfactual scenarios around a medical case.\nImplications of the findings are discussed for potential uses of GPT-4 in\nmedical education, assessment, and clinical practice, with appropriate\nattention to challenges of accuracy and safety.\n", "date": "2023-03-20", "categories": []}
{"id": "2303.04360", "title": "Does Synthetic Data Generation of LLMs Help Clinical Text Mining?", "abstract": "  Recent advancements in large language models (LLMs) have led to the\ndevelopment of highly potent models like OpenAI's ChatGPT. These models have\nexhibited exceptional performance in a variety of tasks, such as question\nanswering, essay composition, and code generation. However, their effectiveness\nin the healthcare sector remains uncertain. In this study, we seek to\ninvestigate the potential of ChatGPT to aid in clinical text mining by\nexamining its ability to extract structured information from unstructured\nhealthcare texts, with a focus on biological named entity recognition and\nrelation extraction. However, our preliminary results indicate that employing\nChatGPT directly for these tasks resulted in poor performance and raised\nprivacy concerns associated with uploading patients' information to the ChatGPT\nAPI. To overcome these limitations, we propose a new training paradigm that\ninvolves generating a vast quantity of high-quality synthetic data with labels\nutilizing ChatGPT and fine-tuning a local model for the downstream task. Our\nmethod has resulted in significant improvements in the performance of\ndownstream tasks, improving the F1-score from 23.37% to 63.99% for the named\nentity recognition task and from 75.86% to 83.59% for the relation extraction\ntask. Furthermore, generating data using ChatGPT can significantly reduce the\ntime and effort required for data collection and labeling, as well as mitigate\ndata privacy concerns. In summary, the proposed framework presents a promising\nsolution to enhance the applicability of LLM models to clinical text mining.\n", "date": "2023-03-08", "categories": []}
{"id": "2304.05268", "title": "An Entity-based Claim Extraction Pipeline for Real-world Biomedical\n  Fact-checking", "abstract": "  Existing fact-checking models for biomedical claims are typically trained on\nsynthetic or well-worded data and hardly transfer to social media content. This\nmismatch can be mitigated by adapting the social media input to mimic the\nfocused nature of common training claims. To do so, Wuehrl & Klinger (2022)\npropose to extract concise claims based on medical entities in the text.\nHowever, their study has two limitations: First, it relies on gold-annotated\nentities. Therefore, its feasibility for a real-world application cannot be\nassessed since this requires detecting relevant entities automatically. Second,\nthey represent claim entities with the original tokens. This constitutes a\nterminology mismatch which potentially limits the fact-checking performance. To\nunderstand both challenges, we propose a claim extraction pipeline for medical\ntweets that incorporates named entity recognition and terminology normalization\nvia entity linking. We show that automatic NER does lead to a performance drop\nin comparison to using gold annotations but the fact-checking performance still\nimproves considerably over inputting the unchanged tweets. Normalizing entities\nto their canonical forms does, however, not improve the performance.\n", "date": "2023-04-11", "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": "2304.04280", "title": "FrenchMedMCQA: A French Multiple-Choice Question Answering Dataset for\n  Medical domain", "abstract": "  This paper introduces FrenchMedMCQA, the first publicly available\nMultiple-Choice Question Answering (MCQA) dataset in French for medical domain.\nIt is composed of 3,105 questions taken from real exams of the French medical\nspecialization diploma in pharmacy, mixing single and multiple answers. Each\ninstance of the dataset contains an identifier, a question, five possible\nanswers and their manual correction(s). We also propose first baseline models\nto automatically process this MCQA task in order to report on the current\nperformances and to highlight the difficulty of the task. A detailed analysis\nof the results showed that it is necessary to have representations adapted to\nthe medical domain or to the MCQA task: in our case, English specialized models\nyielded better results than generic French ones, even though FrenchMedMCQA is\nin French. Corpus, models and tools are available online.\n", "date": "2023-04-09", "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": "2304.01344", "title": "End-to-End Models for Chemical-Protein Interaction Extraction: Better\n  Tokenization and Span-Based Pipeline Strategies", "abstract": "  End-to-end relation extraction (E2ERE) is an important task in information\nextraction, more so for biomedicine as scientific literature continues to grow\nexponentially. E2ERE typically involves identifying entities (or named entity\nrecognition (NER)) and associated relations, while most RE tasks simply assume\nthat the entities are provided upfront and end up performing relation\nclassification. E2ERE is inherently more difficult than RE alone given the\npotential snowball effect of errors from NER leading to more errors in RE. A\ncomplex dataset in biomedical E2ERE is the ChemProt dataset (BioCreative VI,\n2017) that identifies relations between chemical compounds and genes/proteins\nin scientific literature. ChemProt is included in all recent biomedical natural\nlanguage processing benchmarks including BLUE, BLURB, and BigBio. However, its\ntreatment in these benchmarks and in other separate efforts is typically not\nend-to-end, with few exceptions. In this effort, we employ a span-based\npipeline approach to produce a new state-of-the-art E2ERE performance on the\nChemProt dataset, resulting in $> 4\\%$ improvement in F1-score over the prior\nbest effort. Our results indicate that a straightforward fine-grained\ntokenization scheme helps span-based approaches excel in E2ERE, especially with\nregards to handling complex named entities. Our error analysis also identifies\na few key failure modes in E2ERE for ChemProt.\n", "date": "2023-04-03", "categories": []}
{"id": "2210.10341", "title": "BioGPT: Generative Pre-trained Transformer for Biomedical Text\n  Generation and Mining", "abstract": "  Pre-trained language models have attracted increasing attention in the\nbiomedical domain, inspired by their great success in the general natural\nlanguage domain. Among the two main branches of pre-trained language models in\nthe general language domain, i.e., BERT (and its variants) and GPT (and its\nvariants), the first one has been extensively studied in the biomedical domain,\nsuch as BioBERT and PubMedBERT. While they have achieved great success on a\nvariety of discriminative downstream biomedical tasks, the lack of generation\nability constrains their application scope. In this paper, we propose BioGPT, a\ndomain-specific generative Transformer language model pre-trained on large\nscale biomedical literature. We evaluate BioGPT on six biomedical NLP tasks and\ndemonstrate that our model outperforms previous models on most tasks.\nEspecially, we get 44.98%, 38.42% and 40.76% F1 score on BC5CDR, KD-DTI and DDI\nend-to-end relation extraction tasks respectively, and 78.2% accuracy on\nPubMedQA, creating a new record. Our case study on text generation further\ndemonstrates the advantage of BioGPT on biomedical literature to generate\nfluent descriptions for biomedical terms. Code is available at\nhttps://github.com/microsoft/BioGPT.\n", "date": "2022-10-19", "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": "2303.16886", "title": "End-to-End $n$-ary Relation Extraction for Combination Drug Therapies", "abstract": "  Combination drug therapies are treatment regimens that involve two or more\ndrugs, administered more commonly for patients with cancer, HIV, malaria, or\ntuberculosis. Currently there are over 350K articles in PubMed that use the\n\"combination drug therapy\" MeSH heading with at least 10K articles published\nper year over the past two decades. Extracting combination therapies from\nscientific literature inherently constitutes an $n$-ary relation extraction\nproblem. Unlike in the general $n$-ary setting where $n$ is fixed (e.g.,\ndrug-gene-mutation relations where $n=3$), extracting combination therapies is\na special setting where $n \\geq 2$ is dynamic, depending on each instance.\nRecently, Tiktinsky et al. (NAACL 2022) introduced a first of its kind dataset,\nCombDrugExt, for extracting such therapies from literature. Here, we use a\nsequence-to-sequence style end-to-end extraction method to achieve an F1-Score\nof $66.7\\%$ on the CombDrugExt test set for positive (or effective)\ncombinations. This is an absolute $\\approx 5\\%$ F1-score improvement even over\nthe prior best relation classification score with spotted drug entities (hence,\nnot end-to-end). Thus our effort introduces a state-of-the-art first model for\nend-to-end extraction that is already superior to the best prior non end-to-end\nmodel for this task. Our model seamlessly extracts all drug entities and\nrelations in a single pass and is highly suitable for dynamic $n$-ary\nextraction scenarios.\n", "date": "2023-03-29", "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": "2303.13314", "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": "2303.10328", "title": "Revisiting Automatic Question Summarization Evaluation in the Biomedical\n  Domain", "abstract": "  Automatic evaluation metrics have been facilitating the rapid development of\nautomatic summarization methods by providing instant and fair assessments of\nthe quality of summaries. Most metrics have been developed for the general\ndomain, especially news and meeting notes, or other language-generation tasks.\nHowever, these metrics are applied to evaluate summarization systems in\ndifferent domains, such as biomedical question summarization. To better\nunderstand whether commonly used evaluation metrics are capable of evaluating\nautomatic summarization in the biomedical domain, we conduct human evaluations\nof summarization quality from four different aspects of a biomedical question\nsummarization task. Based on human judgments, we identify different noteworthy\nfeatures for current automatic metrics and summarization systems as well. We\nalso release a dataset of our human annotations to aid the research of\nsummarization evaluation metrics in the biomedical domain.\n", "date": "2023-03-18", "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": "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": "2211.15544", "title": "Automatically Extracting Information in Medical Dialogue: Expert System\n  And Attention for Labelling", "abstract": "  Medical dialogue information extraction is becoming an increasingly\nsignificant problem in modern medical care. It is difficult to extract key\ninformation from electronic medical records (EMRs) due to their large numbers.\nPreviously, researchers proposed attention-based models for retrieving features\nfrom EMRs, but their limitations were reflected in their inability to recognize\ndifferent categories in medical dialogues. In this paper, we propose a novel\nmodel, Expert System and Attention for Labelling (ESAL). We use mixture of\nexperts and pre-trained BERT to retrieve the semantics of different categories,\nenabling the model to fuse the differences between them. In our experiment,\nESAL was applied to a public dataset and the experimental results indicated\nthat ESAL significantly improved the performance of Medical Information\nClassification.\n", "date": "2022-11-28", "categories": []}
{"id": "2303.07069", "title": "Generating multiple-choice questions for medical question answering with\n  distractors and cue-masking", "abstract": "  Medical multiple-choice question answering (MCQA) is particularly difficult.\nQuestions may describe patient symptoms and ask for the correct diagnosis,\nwhich requires domain knowledge and complex reasoning. Standard language\nmodeling pretraining alone is not sufficient to achieve the best results.\n\\citet{jin2020disease} showed that focusing masked language modeling on disease\nname prediction when using medical encyclopedic paragraphs as input leads to\nconsiderable MCQA accuracy improvement. In this work, we show that (1)\nfine-tuning on generated MCQA dataset outperforms the masked language modeling\nbased objective and (2) correctly masking the cues to the answers is critical\nfor good performance. We release new pretraining datasets and achieve\nstate-of-the-art results on 4 MCQA datasets, notably +5.7\\% with base-size\nmodel on MedQA-USMLE.\n", "date": "2023-03-13", "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": "2302.09155", "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": "2302.06854", "title": "Large-Scale Knowledge Synthesis and Complex Information Retrieval from\n  Biomedical Documents", "abstract": "  Recent advances in the healthcare industry have led to an abundance of\nunstructured data, making it challenging to perform tasks such as efficient and\naccurate information retrieval at scale. Our work offers an all-in-one scalable\nsolution for extracting and exploring complex information from large-scale\nresearch documents, which would otherwise be tedious. First, we briefly explain\nour knowledge synthesis process to extract helpful information from\nunstructured text data of research documents. Then, on top of the knowledge\nextracted from the documents, we perform complex information retrieval using\nthree major components- Paragraph Retrieval, Triplet Retrieval from Knowledge\nGraphs, and Complex Question Answering (QA). These components combine lexical\nand semantic-based methods to retrieve paragraphs and triplets and perform\nfaceted refinement for filtering these search results. The complexity of\nbiomedical queries and documents necessitates using a QA system capable of\nhandling queries more complex than factoid queries, which we evaluate\nqualitatively on the COVID-19 Open Research Dataset (CORD-19) to demonstrate\nthe effectiveness and value-add.\n", "date": "2023-02-14", "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": "2302.01588", "title": "Bioformer: an efficient transformer language model for biomedical text\n  mining", "abstract": "  Pretrained language models such as Bidirectional Encoder Representations from\nTransformers (BERT) have achieved state-of-the-art performance in natural\nlanguage processing (NLP) tasks. Recently, BERT has been adapted to the\nbiomedical domain. Despite the effectiveness, these models have hundreds of\nmillions of parameters and are computationally expensive when applied to\nlarge-scale NLP applications. We hypothesized that the number of parameters of\nthe original BERT can be dramatically reduced with minor impact on performance.\nIn this study, we present Bioformer, a compact BERT model for biomedical text\nmining. We pretrained two Bioformer models (named Bioformer8L and Bioformer16L)\nwhich reduced the model size by 60% compared to BERTBase. Bioformer uses a\nbiomedical vocabulary and was pre-trained from scratch on PubMed abstracts and\nPubMed Central full-text articles. We thoroughly evaluated the performance of\nBioformer as well as existing biomedical BERT models including BioBERT and\nPubMedBERT on 15 benchmark datasets of four different biomedical NLP tasks:\nnamed entity recognition, relation extraction, question answering and document\nclassification. The results show that with 60% fewer parameters, Bioformer16L\nis only 0.1% less accurate than PubMedBERT while Bioformer8L is 0.9% less\naccurate than PubMedBERT. Both Bioformer16L and Bioformer8L outperformed\nBioBERTBase-v1.1. In addition, Bioformer16L and Bioformer8L are 2-3 fold as\nfast as PubMedBERT/BioBERTBase-v1.1. Bioformer has been successfully deployed\nto PubTator Central providing gene annotations over 35 million PubMed abstracts\nand 5 million PubMed Central full-text articles. We make Bioformer publicly\navailable via https://github.com/WGLab/bioformer, including pre-trained models,\ndatasets, and instructions for downstream use.\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": "2301.13318", "title": "Proxy-based Zero-Shot Entity Linking by Effective Candidate Retrieval", "abstract": "  A recent advancement in the domain of biomedical Entity Linking is the\ndevelopment of powerful two-stage algorithms, an initial candidate retrieval\nstage that generates a shortlist of entities for each mention, followed by a\ncandidate ranking stage. However, the effectiveness of both stages are\ninextricably dependent on computationally expensive components. Specifically,\nin candidate retrieval via dense representation retrieval it is important to\nhave hard negative samples, which require repeated forward passes and nearest\nneighbour searches across the entire entity label set throughout training. In\nthis work, we show that pairing a proxy-based metric learning loss with an\nadversarial regularizer provides an efficient alternative to hard negative\nsampling in the candidate retrieval stage. In particular, we show competitive\nperformance on the recall@1 metric, thereby providing the option to leave out\nthe expensive candidate ranking step. Finally, we demonstrate how the model can\nbe used in a zero-shot setting to discover out of knowledge base biomedical\nentities.\n", "date": "2023-01-30", "categories": []}
{"id": "2210.05598", "title": "Enriching Biomedical Knowledge for Low-resource Language Through\n  Large-Scale Translation", "abstract": "  Biomedical data and benchmarks are highly valuable yet very limited in\nlow-resource languages other than English such as Vietnamese. In this paper, we\nmake use of a state-of-the-art translation model in English-Vietnamese to\ntranslate and produce both pretrained as well as supervised data in the\nbiomedical domains. Thanks to such large-scale translation, we introduce\nViPubmedT5, a pretrained Encoder-Decoder Transformer model trained on 20\nmillion translated abstracts from the high-quality public PubMed corpus.\nViPubMedT5 demonstrates state-of-the-art results on two different biomedical\nbenchmarks in summarization and acronym disambiguation. Further, we release\nViMedNLI - a new NLP task in Vietnamese translated from MedNLI using the\nrecently public En-vi translation model and carefully refined by human experts,\nwith evaluations of existing methods against ViPubmedT5.\n", "date": "2022-10-11", "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": "2301.11847", "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": "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": "2301.03980", "title": "Language Models sounds the Death Knell of Knowledge Graphs", "abstract": "  Healthcare domain generates a lot of unstructured and semi-structured text.\nNatural Language processing (NLP) has been used extensively to process this\ndata. Deep Learning based NLP especially Large Language Models (LLMs) such as\nBERT have found broad acceptance and are used extensively for many\napplications. A Language Model is a probability distribution over a word\nsequence. Self-supervised Learning on a large corpus of data automatically\ngenerates deep learning-based language models. BioBERT and Med-BERT are\nlanguage models pre-trained for the healthcare domain. Healthcare uses typical\nNLP tasks such as question answering, information extraction, named entity\nrecognition, and search to simplify and improve processes. However, to ensure\nrobust application of the results, NLP practitioners need to normalize and\nstandardize them. One of the main ways of achieving normalization and\nstandardization is the use of Knowledge Graphs. A Knowledge Graph captures\nconcepts and their relationships for a specific domain, but their creation is\ntime-consuming and requires manual intervention from domain experts, which can\nprove expensive. SNOMED CT (Systematized Nomenclature of Medicine -- Clinical\nTerms), Unified Medical Language System (UMLS), and Gene Ontology (GO) are\npopular ontologies from the healthcare domain. SNOMED CT and UMLS capture\nconcepts such as disease, symptoms and diagnosis and GO is the world's largest\nsource of information on the functions of genes. Healthcare has been dealing\nwith an explosion in information about different types of drugs, diseases, and\nprocedures. This paper argues that using Knowledge Graphs is not the best\nsolution for solving problems in this domain. We present experiments using LLMs\nfor the healthcare domain to demonstrate that language models provide the same\nfunctionality as knowledge graphs, thereby making knowledge graphs redundant.\n", "date": "2023-01-10", "categories": []}
{"id": "2212.14206", "title": "Maximizing Use-Case Specificity through Precision Model Tuning", "abstract": "  Language models have become increasingly popular in recent years for tasks\nlike information retrieval. As use-cases become oriented toward specific\ndomains, fine-tuning becomes default for standard performance. To fine-tune\nthese models for specific tasks and datasets, it is necessary to carefully tune\nthe model's hyperparameters and training techniques. In this paper, we present\nan in-depth analysis of the performance of four transformer-based language\nmodels on the task of biomedical information retrieval. The models we consider\nare DeepMind's RETRO (7B parameters), GPT-J (6B parameters), GPT-3 (175B\nparameters), and BLOOM (176B parameters). We compare their performance on the\nbasis of relevance, accuracy, and interpretability, using a large corpus of\n480000 research papers on protein structure/function prediction as our dataset.\nOur findings suggest that smaller models, with <10B parameters and fine-tuned\non domain-specific datasets, tend to outperform larger language models on\nhighly specific questions in terms of accuracy, relevancy, and interpretability\nby a significant margin (+50% on average). However, larger models do provide\ngenerally better results on broader prompts.\n", "date": "2022-12-29", "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": "2212.11933", "title": "Word Embedding Neural Networks to Advance Knee Osteoarthritis Research", "abstract": "  Osteoarthritis (OA) is the most prevalent chronic joint disease worldwide,\nwhere knee OA takes more than 80% of commonly affected joints. Knee OA is not a\ncurable disease yet, and it affects large columns of patients, making it costly\nto patients and healthcare systems. Etiology, diagnosis, and treatment of knee\nOA might be argued by variability in its clinical and physical manifestations.\nAlthough knee OA carries a list of well-known terminology aiming to standardize\nthe nomenclature of the diagnosis, prognosis, treatment, and clinical outcomes\nof the chronic joint disease, in practice there is a wide range of terminology\nassociated with knee OA across different data sources, including but not\nlimited to biomedical literature, clinical notes, healthcare literacy, and\nhealth-related social media. Among these data sources, the scientific articles\npublished in the biomedical literature usually make a principled pipeline to\nstudy disease. Rapid yet, accurate text mining on large-scale scientific\nliterature may discover novel knowledge and terminology to better understand\nknee OA and to improve the quality of knee OA diagnosis, prevention, and\ntreatment. The present works aim to utilize artificial neural network\nstrategies to automatically extract vocabularies associated with knee OA\ndiseases. Our finding indicates the feasibility of developing word embedding\nneural networks for autonomous keyword extraction and abstraction of knee OA.\n", "date": "2022-12-22", "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": "2212.09867", "title": "Detecting Contradictory COVID-19 Drug Efficacy Claims from Biomedical\n  Literature", "abstract": "  The COVID-19 pandemic created a deluge of questionable and contradictory\nscientific claims about drug efficacy -- an \"infodemic\" with lasting\nconsequences for science and society. In this work, we argue that NLP models\ncan help domain experts distill and understand the literature in this complex,\nhigh-stakes area. Our task is to automatically identify contradictory claims\nabout COVID-19 drug efficacy. We frame this as a natural language inference\nproblem and offer a new NLI dataset created by domain experts. The NLI framing\nallows us to create curricula combining existing datasets and our own. The\nresulting models are useful investigative tools. We provide a case study of how\nthese models help a domain expert summarize and assess evidence concerning\nremdisivir and hydroxychloroquine.\n", "date": "2022-12-19", "categories": []}
{"id": "2212.07914", "title": "The Effects of In-domain Corpus Size on pre-training BERT", "abstract": "  Many prior language modeling efforts have shown that pre-training on an\nin-domain corpus can significantly improve performance on downstream\ndomain-specific NLP tasks. However, the difficulties associated with collecting\nenough in-domain data might discourage researchers from approaching this\npre-training task. In this paper, we conducted a series of experiments by\npre-training Bidirectional Encoder Representations from Transformers (BERT)\nwith different sizes of biomedical corpora. The results demonstrate that\npre-training on a relatively small amount of in-domain data (4GB) with limited\ntraining steps, can lead to better performance on downstream domain-specific\nNLP tasks compared with fine-tuning models pre-trained on general corpora.\n", "date": "2022-12-15", "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": "2212.04734", "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": "2212.01669", "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": "2212.00223", "title": "Biomedical NER for the Enterprise with Distillated BERN2 and the Kazu\n  Framework", "abstract": "  In order to assist the drug discovery/development process, pharmaceutical\ncompanies often apply biomedical NER and linking techniques over internal and\npublic corpora. Decades of study of the field of BioNLP has produced a plethora\nof algorithms, systems and datasets. However, our experience has been that no\nsingle open source system meets all the requirements of a modern pharmaceutical\ncompany. In this work, we describe these requirements according to our\nexperience of the industry, and present Kazu, a highly extensible, scalable\nopen source framework designed to support BioNLP for the pharmaceutical sector.\nKazu is a built around a computationally efficient version of the BERN2 NER\nmodel (TinyBERN2), and subsequently wraps several other BioNLP technologies\ninto one coherent system. KAZU framework is open-sourced:\nhttps://github.com/AstraZeneca/KAZU\n", "date": "2022-12-01", "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": "2211.15022", "title": "Summer: WeChat Neural Machine Translation Systems for the WMT22\n  Biomedical Translation Task", "abstract": "  This paper introduces WeChat's participation in WMT 2022 shared biomedical\ntranslation task on Chinese to English. Our systems are based on the\nTransformer, and use several different Transformer structures to improve the\nquality of translation. In our experiments, we employ data filtering, data\ngeneration, several variants of Transformer, fine-tuning and model ensemble.\nOur Chinese$\\to$English system, named Summer, achieves the highest BLEU score\namong all submissions.\n", "date": "2022-11-28", "categories": []}
{"id": "2211.12124", "title": "A Large-Scale Dataset for Biomedical Keyphrase Generation", "abstract": "  Keyphrase generation is the task consisting in generating a set of words or\nphrases that highlight the main topics of a document. There are few datasets\nfor keyphrase generation in the biomedical domain and they do not meet the\nexpectations in terms of size for training generative models. In this paper, we\nintroduce kp-biomed, the first large-scale biomedical keyphrase generation\ndataset with more than 5M documents collected from PubMed abstracts. We train\nand release several generative models and conduct a series of experiments\nshowing that using large scale datasets improves significantly the performances\nfor present and absent keyphrase generation. The dataset is available under\nCC-BY-NC v4.0 license at https://huggingface.co/ datasets/taln-ls2n/kpbiomed.\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": "2211.08429", "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": "2211.06778", "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": "2211.04569", "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": "2211.05035", "title": "Combining Contrastive Learning and Knowledge Graph Embeddings to develop\n  medical word embeddings for the Italian language", "abstract": "  Word embeddings play a significant role in today's Natural Language\nProcessing tasks and applications. While pre-trained models may be directly\nemployed and integrated into existing pipelines, they are often fine-tuned to\nbetter fit with specific languages or domains. In this paper, we attempt to\nimprove available embeddings in the uncovered niche of the Italian medical\ndomain through the combination of Contrastive Learning (CL) and Knowledge Graph\nEmbedding (KGE). The main objective is to improve the accuracy of semantic\nsimilarity between medical terms, which is also used as an evaluation task.\nSince the Italian language lacks medical texts and controlled vocabularies, we\nhave developed a specific solution by combining preexisting CL methods\n(multi-similarity loss, contextualization, dynamic sampling) and the\nintegration of KGEs, creating a new variant of the loss. Although without\nhaving outperformed the state-of-the-art, represented by multilingual models,\nthe obtained results are encouraging, providing a significant leap in\nperformance compared to the starting model, while using a significantly lower\namount of data.\n", "date": "2022-11-09", "categories": []}
{"id": "2211.03943", "title": "Final Report on MITRE Evaluations for the DARPA Big Mechanism Program", "abstract": "  This report presents the evaluation approach developed for the DARPA Big\nMechanism program, which aimed at developing computer systems that will read\nresearch papers, integrate the information into a computer model of cancer\nmechanisms, and frame new hypotheses. We employed an iterative, incremental\napproach to the evaluation of the three phases of the program. In Phase I, we\nevaluated the ability of system and human teams ability to read-with-a-model to\ncapture mechanistic information from the biomedical literature, integrated with\ninformation from expert curated biological databases. In Phase II we evaluated\nthe ability of systems to assemble fragments of information into a mechanistic\nmodel. The Phase III evaluation focused on the ability of systems to provide\nexplanations of experimental observations based on models assembled (largely\nautomatically) by the Big Mechanism process. The evaluation for each phase\nbuilt on earlier evaluations and guided developers towards creating\ncapabilities for the new phase. The report describes our approach, including\ninnovations such as a reference set (a curated data set limited to major\nfindings of each paper) to assess the accuracy of systems in extracting\nmechanistic findings in the absence of a gold standard, and a method to\nevaluate model-based explanations of experimental data. Results of the\nevaluation and supporting materials are included in the appendices.\n", "date": "2022-11-08", "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": "2209.08124", "title": "Comprehensively identifying Long Covid articles with human-in-the-loop\n  machine learning", "abstract": "  A significant percentage of COVID-19 survivors experience ongoing\nmultisystemic symptoms that often affect daily living, a condition known as\nLong Covid or post-acute-sequelae of SARS-CoV-2 infection. However, identifying\nscientific articles relevant to Long Covid is challenging since there is no\nstandardized or consensus terminology. We developed an iterative\nhuman-in-the-loop machine learning framework combining data programming with\nactive learning into a robust ensemble model, demonstrating higher specificity\nand considerably higher sensitivity than other methods. Analysis of the Long\nCovid collection shows that (1) most Long Covid articles do not refer to Long\nCovid by any name (2) when the condition is named, the name used most\nfrequently in the literature is Long Covid, and (3) Long Covid is associated\nwith disorders in a wide variety of body systems. The Long Covid collection is\nupdated weekly and is searchable online at the LitCovid portal:\nhttps://www.ncbi.nlm.nih.gov/research/coronavirus/docsum?filters=e_condition.LongCovid\n", "date": "2022-09-16", "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": "2210.14814", "title": "BioNLI: Generating a Biomedical NLI Dataset Using Lexico-semantic\n  Constraints for Adversarial Examples", "abstract": "  Natural language inference (NLI) is critical for complex decision-making in\nbiomedical domain. One key question, for example, is whether a given biomedical\nmechanism is supported by experimental evidence. This can be seen as an NLI\nproblem but there are no directly usable datasets to address this. The main\nchallenge is that manually creating informative negative examples for this task\nis difficult and expensive. We introduce a novel semi-supervised procedure that\nbootstraps an NLI dataset from existing biomedical dataset that pairs\nmechanisms with experimental evidence in abstracts. We generate a range of\nnegative examples using nine strategies that manipulate the structure of the\nunderlying mechanisms both with rules, e.g., flip the roles of the entities in\nthe interaction, and, more importantly, as perturbations via logical\nconstraints in a neuro-logical decoding system. We use this procedure to create\na novel dataset for NLI in the biomedical domain, called BioNLI and benchmark\ntwo state-of-the-art biomedical classifiers. The best result we obtain is\naround mid 70s in F1, suggesting the difficulty of the task. Critically, the\nperformance on the different classes of negative examples varies widely, from\n97% F1 on the simple role change negative examples, to barely better than\nchance on the negative examples generated using neuro-logic decoding.\n", "date": "2022-10-26", "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": "2210.12949", "title": "Enhancing Label Consistency on Document-level Named Entity Recognition", "abstract": "  Named entity recognition (NER) is a fundamental part of extracting\ninformation from documents in biomedical applications. A notable advantage of\nNER is its consistency in extracting biomedical entities in a document context.\nAlthough existing document NER models show consistent predictions, they still\ndo not meet our expectations. We investigated whether the adjectives and\nprepositions within an entity cause a low label consistency, which results in\ninconsistent predictions. In this paper, we present our method, ConNER, which\nenhances the label dependency of modifiers (e.g., adjectives and prepositions)\nto achieve higher label agreement. ConNER refines the draft labels of the\nmodifiers to improve the output representations of biomedical entities. The\neffectiveness of our method is demonstrated on four popular biomedical NER\ndatasets; in particular, its efficacy is proved on two datasets with 7.5-8.6%\nabsolute improvements in the F1 score. We interpret that our ConNER method is\neffective on datasets that have intrinsically low label consistency. In the\nqualitative analysis, we demonstrate how our approach makes the NER model\ngenerate consistent predictions. Our code and resources are available at\nhttps://github.com/dmis-lab/ConNER/.\n", "date": "2022-10-24", "categories": []}
{"id": "2209.07859", "title": "Extracting Biomedical Factual Knowledge Using Pretrained Language Model\n  and Electronic Health Record Context", "abstract": "  Language Models (LMs) have performed well on biomedical natural language\nprocessing applications. In this study, we conducted some experiments to use\nprompt methods to extract knowledge from LMs as new knowledge Bases (LMs as\nKBs). However, prompting can only be used as a low bound for knowledge\nextraction, and perform particularly poorly on biomedical domain KBs. In order\nto make LMs as KBs more in line with the actual application scenarios of the\nbiomedical domain, we specifically add EHR notes as context to the prompt to\nimprove the low bound in the biomedical domain. We design and validate a series\nof experiments for our Dynamic-Context-BioLAMA task. Our experiments show that\nthe knowledge possessed by those language models can distinguish the correct\nknowledge from the noise knowledge in the EHR notes, and such distinguishing\nability can also be used as a new metric to evaluate the amount of knowledge\npossessed by the model.\n", "date": "2022-08-26", "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": "2210.03304", "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": "2210.06566", "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": "2210.05875", "title": "MedJEx: A Medical Jargon Extraction Model with Wiki's Hyperlink Span and\n  Contextualized Masked Language Model Score", "abstract": "  This paper proposes a new natural language processing (NLP) application for\nidentifying medical jargon terms potentially difficult for patients to\ncomprehend from electronic health record (EHR) notes. We first present a novel\nand publicly available dataset with expert-annotated medical jargon terms from\n18K+ EHR note sentences ($MedJ$). Then, we introduce a novel medical jargon\nextraction ($MedJEx$) model which has been shown to outperform existing\nstate-of-the-art NLP models. First, MedJEx improved the overall performance\nwhen it was trained on an auxiliary Wikipedia hyperlink span dataset, where\nhyperlink spans provide additional Wikipedia articles to explain the spans (or\nterms), and then fine-tuned on the annotated MedJ data. Secondly, we found that\na contextualized masked language model score was beneficial for detecting\ndomain-specific unfamiliar jargon terms. Moreover, our results show that\ntraining on the auxiliary Wikipedia hyperlink span datasets improved six out of\neight biomedical named entity recognition benchmark datasets. Both MedJ and\nMedJEx are publicly available.\n", "date": "2022-10-12", "categories": []}
{"id": "2206.14504", "title": "GERNERMED++: Transfer Learning in German Medical NLP", "abstract": "  We present a statistical model for German medical natural language processing\ntrained for named entity recognition (NER) as an open, publicly available\nmodel. The work serves as a refined successor to our first GERNERMED model\nwhich is substantially outperformed by our work. We demonstrate the\neffectiveness of combining multiple techniques in order to achieve strong\nresults in entity recognition performance by the means of transfer-learning on\npretrained deep language models (LM), word-alignment and neural machine\ntranslation. Due to the sparse situation on open, public medical entity\nrecognition models for German texts, this work offers benefits to the German\nresearch community on medical NLP as a baseline model. Since our model is based\non public English data, its weights are provided without legal restrictions on\nusage and distribution. The sample code and the statistical model is available\nat: https://github.com/frankkramer-lab/GERNERMED-pp\n", "date": "2022-06-29", "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": "2210.03246", "title": "HealthE: Classifying Entities in Online Textual Health Advice", "abstract": "  The processing of entities in natural language is essential to many medical\nNLP systems. Unfortunately, existing datasets vastly under-represent the\nentities required to model public health relevant texts such as health advice\noften found on sites like WebMD. People rely on such information for personal\nhealth management and clinically relevant decision making. In this work, we\nrelease a new annotated dataset, HealthE, consisting of 6,756 health advice.\nHealthE has a more granular label space compared to existing medical NER\ncorpora and contains annotation for diverse health phrases. Additionally, we\nintroduce a new health entity classification model, EP S-BERT, which leverages\ntextual context patterns in the classification of entity classes. EP S-BERT\nprovides a 4-point increase in F1 score over the nearest baseline and a\n34-point increase in F1 when compared to off-the-shelf medical NER tools\ntrained to extract disease and medication mentions from clinical texts. All\ncode and data are publicly available on Github.\n", "date": "2022-10-06", "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": "2209.14377", "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": "2209.13773", "title": "METS-CoV: A Dataset of Medical Entity and Targeted Sentiment on COVID-19\n  Related Tweets", "abstract": "  The COVID-19 pandemic continues to bring up various topics discussed or\ndebated on social media. In order to explore the impact of pandemics on\npeople's lives, it is crucial to understand the public's concerns and attitudes\ntowards pandemic-related entities (e.g., drugs, vaccines) on social media.\nHowever, models trained on existing named entity recognition (NER) or targeted\nsentiment analysis (TSA) datasets have limited ability to understand\nCOVID-19-related social media texts because these datasets are not designed or\nannotated from a medical perspective. This paper releases METS-CoV, a dataset\ncontaining medical entities and targeted sentiments from COVID-19-related\ntweets. METS-CoV contains 10,000 tweets with 7 types of entities, including 4\nmedical entity types (Disease, Drug, Symptom, and Vaccine) and 3 general entity\ntypes (Person, Location, and Organization). To further investigate tweet users'\nattitudes toward specific entities, 4 types of entities (Person, Organization,\nDrug, and Vaccine) are selected and annotated with user sentiments, resulting\nin a targeted sentiment dataset with 9,101 entities (in 5,278 tweets). To the\nbest of our knowledge, METS-CoV is the first dataset to collect medical\nentities and corresponding sentiments of COVID-19-related tweets. We benchmark\nthe performance of classical machine learning models and state-of-the-art deep\nlearning models on NER and TSA tasks with extensive experiments. Results show\nthat the dataset has vast room for improvement for both NER and TSA tasks.\nMETS-CoV is an important resource for developing better medical social media\ntools and facilitating computational social science research, especially in\nepidemiology. Our data, annotation guidelines, benchmark models, and source\ncode are publicly available (https://github.com/YLab-Open/METS-CoV) to ensure\nreproducibility.\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": "2209.09814", "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": "2209.08698", "title": "LED down the rabbit hole: exploring the potential of global attention\n  for biomedical multi-document summarisation", "abstract": "  In this paper we report on our submission to the Multidocument Summarisation\nfor Literature Review (MSLR) shared task. Specifically, we adapt PRIMERA (Xiao\net al., 2022) to the biomedical domain by placing global attention on important\nbiomedical entities in several ways. We analyse the outputs of the 23 resulting\nmodels, and report patterns in the results related to the presence of\nadditional global attention, number of training steps, and the input\nconfiguration.\n", "date": "2022-09-19", "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": "2209.03528", "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": "2209.02812", "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": "2209.03182", "title": "On the Effectiveness of Compact Biomedical Transformers", "abstract": "  Language models pre-trained on biomedical corpora, such as BioBERT, have\nrecently shown promising results on downstream biomedical tasks. Many existing\npre-trained models, on the other hand, are resource-intensive and\ncomputationally heavy owing to factors such as embedding size, hidden\ndimension, and number of layers. The natural language processing (NLP)\ncommunity has developed numerous strategies to compress these models utilising\ntechniques such as pruning, quantisation, and knowledge distillation, resulting\nin models that are considerably faster, smaller, and subsequently easier to use\nin practice. By the same token, in this paper we introduce six lightweight\nmodels, namely, BioDistilBERT, BioTinyBERT, BioMobileBERT, DistilBioBERT,\nTinyBioBERT, and CompactBioBERT which are obtained either by knowledge\ndistillation from a biomedical teacher or continual learning on the Pubmed\ndataset via the Masked Language Modelling (MLM) objective. We evaluate all of\nour models on three biomedical tasks and compare them with BioBERT-v1.1 to\ncreate efficient lightweight models that perform on par with their larger\ncounterparts. All the models will be publicly available on our Huggingface\nprofile at https://huggingface.co/nlpie and the codes used to run the\nexperiments will be available at\nhttps://github.com/nlpie-research/Compact-Biomedical-Transformers.\n", "date": "2022-09-07", "categories": []}
{"id": "2209.01815", "title": "Query-focused Extractive Summarisation for Biomedical and COVID-19\n  Complex Question Answering", "abstract": "  This paper presents Macquarie University's participation to the two most\nrecent BioASQ Synergy Tasks (as per June 2022), and to the BioASQ10 Task~B\n(BioASQ10b), Phase~B. In these tasks, participating systems are expected to\ngenerate complex answers to biomedical questions, where the answers may contain\nmore than one sentence. We apply query-focused extractive summarisation\ntechniques. In particular, we follow a sentence classification-based approach\nthat scores each candidate sentence associated to a question, and the $n$\nhighest-scoring sentences are returned as the answer. The Synergy Task\ncorresponds to an end-to-end system that requires document selection, snippet\nselection, and finding the final answer, but it has very limited training data.\nFor the Synergy task, we selected the candidate sentences following two phases:\ndocument retrieval and snippet retrieval, and the final answer was found by\nusing a DistilBERT/ALBERT classifier that had been trained on the training data\nof BioASQ9b. Document retrieval was achieved as a standard search over the\nCORD-19 data using the search API provided by the BioASQ organisers, and\nsnippet retrieval was achieved by re-ranking the sentences of the top retrieved\ndocuments, using the cosine similarity of the question and candidate sentence.\nWe observed that vectors represented via sBERT have an edge over tf.idf.\nBioASQ10b Phase B focuses on finding the specific answers to biomedical\nquestions. For this task, we followed a data-centric approach. We hypothesised\nthat the training data of the first BioASQ years might be biased and we\nexperimented with different subsets of the training data. We observed an\nimprovement of results when the system was trained on the second half of the\nBioASQ10b training data.\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": "2208.09437", "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": "2208.02301", "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": "2208.01316", "title": "Joint Learning-based Causal Relation Extraction from Biomedical\n  Literature", "abstract": "  Causal relation extraction of biomedical entities is one of the most complex\ntasks in biomedical text mining, which involves two kinds of information:\nentity relations and entity functions. One feasible approach is to take\nrelation extraction and function detection as two independent sub-tasks.\nHowever, this separate learning method ignores the intrinsic correlation\nbetween them and leads to unsatisfactory performance. In this paper, we propose\na joint learning model, which combines entity relation extraction and entity\nfunction detection to exploit their commonality and capture their\ninter-relationship, so as to improve the performance of biomedical causal\nrelation extraction. Meanwhile, during the model training stage, different\nfunction types in the loss function are assigned different weights.\nSpecifically, the penalty coefficient for negative function instances increases\nto effectively improve the precision of function detection. Experimental\nresults on the BioCreative-V Track 4 corpus show that our joint learning model\noutperforms the separate models in BEL statement extraction, achieving the F1\nscores of 58.4% and 37.3% on the test set in Stage 2 and Stage 1 evaluations,\nrespectively. This demonstrates that our joint learning system reaches the\nstate-of-the-art performance in Stage 2 compared with other systems.\n", "date": "2022-08-02", "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": "2207.06226", "title": "Building a Relation Extraction Baseline for Gene-Disease Associations: A\n  Reproducibility Study", "abstract": "  Reproducibility is an important task in scientific research. It is crucial\nfor researchers to compare newly developed systems with the state-of-the-art to\nassess whether they made a breakthrough. However previous works may not be\nimmediately reproducible, for example due to the lack of source code. In this\nwork we reproduce DEXTER, a system to automatically extract Gene-Disease\nAssociations (GDAs) from biomedical abstracts. The goal is to provide a\nbenchmark for future works regarding Relation Extraction (RE), enabling\nresearchers to test and compare their results.\n", "date": "2022-07-04", "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": "2207.02419", "title": "BioTABQA: Instruction Learning for Biomedical Table Question Answering", "abstract": "  Table Question Answering (TQA) is an important but under-explored task. Most\nof the existing QA datasets are in unstructured text format and only few of\nthem use tables as the context. To the best of our knowledge, none of TQA\ndatasets exist in the biomedical domain where tables are frequently used to\npresent information. In this paper, we first curate a table question answering\ndataset, BioTABQA, using 22 templates and the context from a biomedical\ntextbook on differential diagnosis. BioTABQA can not only be used to teach a\nmodel how to answer questions from tables but also evaluate how a model\ngeneralizes to unseen questions, an important scenario for biomedical\napplications. To achieve the generalization evaluation, we divide the templates\ninto 17 training and 5 cross-task evaluations. Then, we develop two baselines\nusing single and multi-tasks learning on BioTABQA. Furthermore, we explore\ninstructional learning, a recent technique showing impressive generalizing\nperformance. Experimental results show that our instruction-tuned model\noutperforms single and multi-task baselines on an average by ~23% and ~6%\nacross various evaluation settings, and more importantly, instruction-tuned\nmodel outperforms baselines by ~5% on cross-tasks.\n", "date": "2022-07-06", "categories": []}
{"id": "2207.00876", "title": "A Biomedical Pipeline to Detect Clinical and Non-Clinical Named Entities", "abstract": "  There are a few challenges related to the task of biomedical named entity\nrecognition, which are: the existing methods consider a fewer number of\nbiomedical entities (e.g., disease, symptom, proteins, genes); and these\nmethods do not consider the social determinants of health (age, gender,\nemployment, race), which are the non-medical factors related to patients'\nhealth. We propose a machine learning pipeline that improves on previous\nefforts in the following ways: first, it recognizes many biomedical entity\ntypes other than the standard ones; second, it considers non-clinical factors\nrelated to patient's health. This pipeline also consists of stages, such as\npreprocessing, tokenization, mapping embedding lookup and named entity\nrecognition task to extract biomedical named entities from the free texts. We\npresent a new dataset that we prepare by curating the COVID-19 case reports.\nThe proposed approach outperforms the baseline methods on five benchmark\ndatasets with macro-and micro-average F1 scores around 90, as well as our\ndataset with a macro-and micro-average F1 score of 95.25 and 93.18\nrespectively.\n", "date": "2022-07-02", "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": "2206.12866", "title": "Contextual embedding and model weighting by fusing domain knowledge on\n  Biomedical Question Answering", "abstract": "  Biomedical Question Answering aims to obtain an answer to the given question\nfrom the biomedical domain. Due to its high requirement of biomedical domain\nknowledge, it is difficult for the model to learn domain knowledge from limited\ntraining data. We propose a contextual embedding method that combines\nopen-domain QA model \\aoa and \\biobert model pre-trained on biomedical domain\ndata. We adopt unsupervised pre-training on large biomedical corpus and\nsupervised fine-tuning on biomedical question answering dataset. Additionally,\nwe adopt an MLP-based model weighting layer to automatically exploit the\nadvantages of two models to provide the correct answer. The public dataset\n\\biomrc constructed from PubMed corpus is used to evaluate our method.\nExperimental results show that our model outperforms state-of-the-art system by\na large margin.\n", "date": "2022-06-26", "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": "2206.05311", "title": "Graph-in-Graph Network for Automatic Gene Ontology Description\n  Generation", "abstract": "  Gene Ontology (GO) is the primary gene function knowledge base that enables\ncomputational tasks in biomedicine. The basic element of GO is a term, which\nincludes a set of genes with the same function. Existing research efforts of GO\nmainly focus on predicting gene term associations. Other tasks, such as\ngenerating descriptions of new terms, are rarely pursued. In this paper, we\npropose a novel task: GO term description generation. This task aims to\nautomatically generate a sentence that describes the function of a GO term\nbelonging to one of the three categories, i.e., molecular function, biological\nprocess, and cellular component. To address this task, we propose a\nGraph-in-Graph network that can efficiently leverage the structural information\nof GO. The proposed network introduces a two-layer graph: the first layer is a\ngraph of GO terms where each node is also a graph (gene graph). Such a\nGraph-in-Graph network can derive the biological functions of GO terms and\ngenerate proper descriptions. To validate the effectiveness of the proposed\nnetwork, we build three large-scale benchmark datasets. By incorporating the\nproposed Graph-in-Graph network, the performances of seven different\nsequence-to-sequence models can be substantially boosted across all evaluation\nmetrics, with up to 34.7%, 14.5%, and 39.1% relative improvements in BLEU,\nROUGE-L, and METEOR, respectively.\n", "date": "2022-06-10", "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": "2206.09600", "title": "SPBERTQA: A Two-Stage Question Answering System Based on Sentence\n  Transformers for Medical Texts", "abstract": "  Question answering (QA) systems have gained explosive attention in recent\nyears. However, QA tasks in Vietnamese do not have many datasets.\nSignificantly, there is mostly no dataset in the medical domain. Therefore, we\nbuilt a Vietnamese Healthcare Question Answering dataset (ViHealthQA),\nincluding 10,015 question-answer passage pairs for this task, in which\nquestions from health-interested users were asked on prestigious health\nwebsites and answers from highly qualified experts. This paper proposes a\ntwo-stage QA system based on Sentence-BERT (SBERT) using multiple negatives\nranking (MNR) loss combined with BM25. Then, we conduct diverse experiments\nwith many bag-of-words models to assess our system's performance. With the\nobtained results, this system achieves better performance than traditional\nmethods.\n", "date": "2022-06-20", "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": "2206.06581", "title": "CHQ-Summ: A Dataset for Consumer Healthcare Question Summarization", "abstract": "  The quest for seeking health information has swamped the web with consumers'\nhealth-related questions. Generally, consumers use overly descriptive and\nperipheral information to express their medical condition or other healthcare\nneeds, contributing to the challenges of natural language understanding. One\nway to address this challenge is to summarize the questions and distill the key\ninformation of the original question. To address this issue, we introduce a new\ndataset, CHQ-Summ that contains 1507 domain-expert annotated consumer health\nquestions and corresponding summaries. The dataset is derived from the\ncommunity question-answering forum and therefore provides a valuable resource\nfor understanding consumer health-related posts on social media. We benchmark\nthe dataset on multiple state-of-the-art summarization models to show the\neffectiveness of the dataset.\n", "date": "2022-06-14", "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": "2606.20997", "title": "BioInsight: Multi-Agent Orchestration for Interactive Biomedical Knowledge Discovery", "abstract": "Biomedical researchers increasingly use AI-generated analyses and reports to interpret protein-level signals, but static outputs are often insufficient for research decision-making, where users need to inspect evidence, assess uncertainty, compare mechanisms, and refine hypotheses. We present \\textsc{BioInsight}, a multi-agent system that moves from static biomedical report generation to interactive evidence-centered interactive interface generation. Given a disease name, a protein association table, and optional cohort metadata, BioInsight organizes disease-specific evidence through typed intermediate artifacts, including ranked pathways, literature evidence packets, protein-level reasoning notes, citation-grounded reports, dashboard schemas, and rendered interactive interfaces. The system decomposes evidence retrieval from mechanistic reasoning, normalizes citations through deterministic components, and converts the same structured evidence used in the report into an interactive interface. We evaluate BioInsight on standardized biomedical QA, challenging protein-function reasoning, and end-to-end biomedical evidence synthesis. Results show that BioInsight achieves best, and suggest that biomedical AI systems should move beyond text-only and static reports toward provenance-preserving, interactive evidence artifacts.", "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": "2606.21959", "title": "OpenBioRQ: Unsolved Biomedical Research Questions for Agents", "abstract": "A working citation looks like proof -- but the fact that a link resolves does not mean the cited paper supports the claim. I find that current agentic models rarely fabricate citations (over $99\\%$ resolve), yet roughly $15.9\\%$ link to the wrong paper. Existing benchmarks miss this failure mode: when a question has a fixed answer key, a model can reproduce the expected source from that key rather than independently verifying that the source supports the claim. I introduce \\textbf{\\openbiorq{}}, a retrieval-grounded agentic benchmark of $12{,}553$ unsolved biomedical research questions across $12$ domains that treats open questions as a faithfulness-and-abstention probe. To my knowledge, this is the first biomedical benchmark to combine an agentic setting -- where the model must issue multiple tool calls -- with unsolved questions that have no answer key. Openness is verified against real follow-up evidence rather than a model's parametric knowledge. Difficulty is empirical: I anchor it on questions that three open-weight reference models fail to answer, rather than on subjective hardness labels. On this hardest subset, held-out models from the same lineage as the difficulty anchors solve only ~17%, while three independent frontier agents (Gemini-3-Pro, Opus-4.7, GPT-5.5) span a wide 29-60% range. The benchmark is thus hard, non-saturating (the best agent still leaves ~33-40\\% unsolved), and discriminating across capability tiers. Beyond difficulty, I observe agentic collapse on the hardest questions, where agents stop using their tools. For the most collapse-prone model, blocking tool access entirely barely changes its score -- so tools stop paying off exactly where they are needed most. A frozen per-question checklist raises inter-judge agreement from Spearman 0.35 to 0.82.", "date": "2026-06-20", "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": "2606.22886", "title": "Explanation-Guided Medical Named Entity Recognition with Stability and Boundary Awareness for Atopic Dermatitis", "abstract": "Objective: This study aims to improve the reliability and robustness of medical named entity recognition (NER) in Chinese atopic dermatitis (AD) clinical texts through explanation-guided learning. Methods: We propose a stability and boundary-aware explanation-guided NER framework. Perturbation-based analysis is used to evaluate explanation stability and entity boundary sensitivity. An adaptive fusion strategy dynamically combines local and global explanation to generate more reliable token-level explanations. The fused explanation signals are further incorporated into model training through stability, boundary-aware, and consistency constraints. Results: Experiments on Chinese AD NER datasets show that the proposed framework improves explanation robustness and achieves consistent performance gains across multiple NER models. The adaptive fusion strategy also provides more stable explanations and stronger boundary perception than individual explanation methods. Conclusion: The proposed method effectively integrates reliable explanation signals into medical NER training, improving both recognition performance and explanation reliability. The framework provides a practical and generalizable solution for explainable medical NER and offers reliable support for downstream clinical decision-making and medical knowledge applications.", "date": "2026-06-22", "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": "2606.24145", "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": "2606.27174", "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": "2606.28798", "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": "2606.30236", "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": []}