Journal of Biomedical Informatics

ObjectiveAdverse events (AEs) resulting from medical interventions are significant contributors to patient morbidity, mortality, and healthcare costs. Prediction of these events using electronic health records (EHRs) can facilitate timely clinical interventions. However, effective prediction remains challenging due to severe class imbalance, missing labels, and the complexity of EHR records. Classical machine learning approaches frequently underperform due to insufficient representation of minor...

Cross-jurisdictional pharmaceutical compliance requires comparative analysis of regulatory requirements across jurisdictions such as the US FDA and Chinas NMPA. Although large language models (LLMs) are increasingly explored for healthcare-related applications, their performance in cross-jurisdictional regulatory comparison has not been systematically characterized using dedicated benchmarks. This study introduces Sino-US-DrugQA, a bilingual benchmark dataset designed to evaluate LLM performance...

ObjectiveSystematic clinical phenotyping using Human Phenotype Ontology (HPO) is central to rare disease diagnosis. However, current disease prioritization (ranking candidate diseases from HPO for a patient) methods face key challenges: they often fail to account for the hierarchical structure of HPO terms, ignore dependencies among correlated terms, and do not adjust for batch effects arising from systematic differences in phenotype documentation across cohorts, institutions, or clinicians. We ...

IMPORTANCEAlthough angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin receptor blockers (ARBs) are recommended for people with chronic kidney disease (CKD), they remain underused. Barriers to adherence, such as adverse effects or patient refusal, are frequently embedded within unstructured clinical narratives and are therefore inaccessible to structured data analytics. Scalable natural language processing (NLP) approaches are needed to identify these barriers and support guideline-...

Clinical decision making often relies on expert judgment guided by established guidelines, which can be challenging to standardize and abstract to implement. For example, selecting between gene panels and whole exome/genome sequencing (WES/WGS) for rare disease diagnosis frequently requires interpretation of evidence-based recommendations from the American College of Medical Genetics and Genomics (ACMG) guideline. Traditional machine learning (ML) models predicting suitable genetic tests often f...

Ambient AI documentation tools generate draft notes that clinicians can review and edit before signing off in electronic health records. Scalable computational approaches to characterize how clinicians modify drafts remain limited, yet are essential for evaluating and improving AI effectiveness. We examined the feasibility of a few-shot prompted large language model (LLM) for categorizing sentence-level edits between AI drafts and final documentation. We developed five label-specific binary mode...

Achieving timely diagnosis for rare diseases remains challenging due to, among others, phenotypic heterogeneity and incomplete clinical data. While the Solve-RD project developed a phenotype-based gene prioritisation method, this approach did not account for the clinical consistency among related diseases in Orphanets hierarchical classifications. We present a phenotype-based computational pipeline that ranks candidate ORPHAcodes based on patient phenotypes. The pipeline computes patient-diseas...

Data scarcity and stylistic heterogeneity pose major challenges for emotion intensity classification. This paper presents a cross-dataset augmentation framework that leverages prompt-conditioned generative models alongside deterministic and heuristic transformations to synthesize target-style examples for improved transfer learning. We introduce a unified taxonomy of augmentation strategies--Heuristic Lexical Perturbation (HLA), Prompt-Conditioned Generative Augmentation (CGA), Sequential Hybrid...

Cox proportional hazard regressions are frequently employed to develop prognostic models for time-to-event data, considering both patient-specific and disease-specific characteristics. In high-dimensional clinical modeling, these biological features can exhibit high collinearity due to inter-feature relationships, potentially causing instability and numerical issues during estimation without regularization. For rare diseases such as acute myeloid leukemia (AML), the sparsity and scarcity of data...

Large language models (LLMs) perform strongly across a wide range of medical applications, yet it remains unclear whether such success reflects genuine understanding of medical concepts. We present an ontology-grounded, concept-centered evaluation of medical concept understanding in LLMs. Using 6,252 phenotype concepts from Human Phenotype Ontology, we decompose concept understanding into three core dimensions--concept identity, concept hierarchy, and concept meaning--and design corresponding be...

As of early 2026, over 115 million US adults (more than 1 in 3) have prediabetes, a condition with an annual conversion rate of 5%-10% to type 2 diabetes. Total diabetes (diagnosed and undiagnosed) affects approximately 40.1 million Americans, or 12% of the population, with roughly 1.5 million new cases diagnosed annually. Continuous Glucose Monitoring (CGM) provides real-time, 24/7 insights into glycemic variability, detecting dangerous highs, lows, and trends that HbA1c (a 3-month average) mis...

BackgroundLarge language models show promise for clinical decision support, yet their propensity for hallucination--generating plausible but unsupported claims--poses sub-stantial patient safety risks. Retrieval-augmented generation (RAG) is widely assumed to mitigate this problem by grounding outputs in retrieved documents, but this assumption remains inadequately tested in clinical contexts where information density, temporal complexity, and safety stakes are uniquely high. MethodsWe develope...

Cross-device medical federated learning--where individual patients participate directly rather than institutions--poses a unique challenge: each client holds only a few samples, often just one (e.g., a single diagnostic record), leaving insufficient local data for gradient computation. Existing approaches, such as Secure Aggregation, require client-to-client coordination impractical for intermittently available mobile devices, while homomorphic encryption introduces substantial computational ove...

Rare diseases affect over 300 million people worldwide, yet patients often endure years-long diagnostic delays that limit timely intervention and trial opportunities. Computational rare disease recognition (RDR) remains constrained by knowledge resources that are often incomplete, heterogeneous, and dependent on extensive multi-disciplinary expert curation that cannot scale. Large language models (LLMs) applied directly for end-to-end diagnosis or disease discrimination face similar knowledge bo...

Large language models (LLMs) are increasingly deployed in structured clinical decision support, yet the architectural effects of internal role decomposition within multi-agent systems remain poorly isolated. Prior comparisons of single-agent and multi-agent prompting frequently confound workflow structure with changes in model configuration, training, or decoding. We present a controlled architectural study of role-structured inference under fixed model parameters, isolating internal role decomp...

Acquiring insights from electronic health records (EHRs) is slowed by manual analytical workflows that limit scalability and reproducibility. We present LATCH (LLM-Assisted Testing of Clinical Hypotheses), an agentic framework that converts natural language clinical hypotheses into fully auditable analyses on structured EHR data. LATCH integrates LLM-assisted semantic layers with deterministic execution pipelines to automate cohort construction, statistical analysis, and result reporting, while ...

Structured AbstractO_ST_ABSBackgroundC_ST_ABSHosting large language models (LLMs) on-premises can secure patient data but requires compact architectures to function on standard hardware. The impact of such constraints on the robustness of their representations for medical terminology is important for clinical AI safety but poorly understood. The statistical nature of LLM training inherently limits the representation of terms with low societal prominence or lexical frequency, and high ambiguity. ...

BackgroundLarge language models (LLMs) are increasingly deployed in medical contexts as patient-facing assistants, providing medication information, symptom triage, and health guidance. Understanding their robustness to adversarial inputs is critical for patient safety, as even a single safety failure can lead to adverse outcomes including severe harm or death. ObjectiveTo systematically evaluate the safety guardrails of state-of-the-art LLMs through adversarial red-teaming specifically designe...

PurposeLarge language models (LLMs) are used for biomedical text processing, but individual decisions are often hard to audit. We evaluated whether enforcing a mechanically checkable "show your work" quote affects accuracy, stability, and verifiability for trial eligibility-scope classification from abstracts. MethodsWe used 200 oncology randomized controlled trials (2005 - 2023) and provided models with only the title and abstract. Trials were labeled with whether they allowed for the inclusio...

AO_SCPLOWBSTRACTC_SCPLOWLongitudinal healthcare surveys frequently contain inconsistencies in self-reported onset ages, where participants report different ages for the same condition between enrollment and follow-up surveys. We propose two methods to handle this challenge. First, we introduce a procedure that aggregates inconsistency patterns to construct participant-level reliability scores, enabling researchers to stratify participants and prioritize analysis on high-reliability cohorts. Seco...