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This study explores the use of advanced natural language processing (NLP) techniques to enhance food classification and dietary analysis using raw text input from a diet tracking app.

Mapping clinical classification systems, such as the International Classification of Diseases (ICD), is essential yet challenging. While the manual mapping method remains labor-intensive and lacks scalability, existing embedding-based automatic mapping methods, particularly those leveraging transformer-based pretrained encoders, encounter 2 persistent challenges: (1) linguistic variation and (2) varying granular details in clinical conditions.

Federated Ecosystems for Analytics and Standardized Technologies (FEAST) is a modular, cloud-based platform developed through the ARPA-H Biomedical Data Fabric initiative to enable secure, federated analysis of real-world biomedical data. To guide and iteratively refine its modular design, the FEAST team conducted a cross-institutional survey to systematically identify and prioritize research needs related to authorized-access data across diverse biomedical domains. This study presents a structured synthesis of submitted use cases [...]

Advancing health through informatics requires attending to justice. Recent policy changes in the United States have introduced significant barriers to promoting justice within informatics due to targeted funding cuts and hostility to science, especially science that prioritizes justice.

To highlight the importance of reporting negative results in large language model (LLM) research, particularly as these systems are increasingly integrated into healthcare.

Trafficked persons experience adverse health consequences and seek help, but many go unrecognized by health-care professionals. This study explored professionals' perspectives on current approaches toward identifying and supporting trafficked persons in health-care settings, highlighting current technology roles, gaps, and future directions.

To develop and evaluate a knowledge graph-augmented large language model (LLM) framework that synthesizes epidemiological evidence to infer life-course exposure-outcome pathways, using gestational diabetes mellitus (GDM) and dementia as a case study.

Documentation of gender identity (GI) and anatomy data in the electronic health record (EHR) is a proposed standard of care for transgender populations. However, there is limited research on implementation of proposed best practices, particularly anatomy data collection. This study aims to characterize factors that influence patient preferences and comfort around the collection and documentation of GI and anatomy in EHRs.

Real-world evidence (RWE) increasingly informs clinical decisions, yet manual adjustment for confounding limits scalability. Data-adaptive (DA) algorithms for high-dimensional proxy adjustment show promise but have not been systematically compared to investigator-specified (IS) approaches across diverse treatment scenarios. We evaluated whether DA strategies perform comparably to manually curated IS models using claims-based emulations of 15 randomized trials from the RCT-DUPLICATE initiative.