Search AMIA.org

Secondary headaches require urgent recognition due to potentially devastating consequences if untreated. Despite established clinical "red flag" criteria, identifying patients needing immediate evaluation remains challenging in primary care. This study developed and evaluated a large language model (LLM)-based multi-agent clinical decision support system for interpretable secondary headache diagnosis.

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.

Using AI algorithms can exacerbate health disparities if care or resources are allocated away from underserved populations. We evaluated an algorithm for its potential to worsen health disparities across different clinical use cases.

Missed healthcare appointments, or "Did Not Attends" (DNAs), pose challenges for healthcare systems. Technology-facilitated reminder systems, such as SMS and phone calls are often implemented to improve attendance. This scoping review aimed to map the evidence on patient and carer perspectives and experiences of technology-enabled appointment reminders in healthcare and, secondly, examine how equity-relevant characteristics were reported.

This study aimed to characterize patterns of digital health technology (DHT) use and examine the relationships between multimorbidity, DHT adoption, and user-reported frustration, with a focus on identifying socioeconomic and phenotypic determinants of digital health burden.

We aimed to develop a data model and a natural language processing (NLP) pipeline for representing physical activity (PA) in Electronic Health Records (EHRs), and to evaluate transformer- and Large Language Model (LLM)-based classifiers for sentence-level PA attribute classification.

To evaluate the clinical applications and translation readiness of model-based synthetic tabular data in healthcare, and identify gaps in governance reporting that may hinder translation.

To evaluate the impact of incorporating community-level social determinants of health (SDoH) on the performance and algorithmic fairness of artificial intelligence (AI) models trained to predict glaucoma progression to surgery.

Electronic health record (EHR) time series are irregularly sampled and frequently incomplete, with missingness potentially carrying clinically informative signals. We developed Masked-Attention-based timestamp-wise Data Embedding (MADE), a missingness-aware model for continuous prediction of deterioration in general wards, and evaluated it across heterogeneous hospitals.

To systematically synthesize how data quality (DQ) in disease and health outcomes registries has been conceptualized, assessed, and improved, emphasizing artificial intelligence (AI) and machine learning methods within registry-based DQ workflows.