127 papers
Health informatics sits at the vibrant intersection of medicine, data science, and technology, transforming how we store, analyze, and utilize health information. This rapidly evolving field empowers clinicians and researchers to uncover patterns in patient data, improve diagnostic accuracy, and personalize treatment plans without getting lost in complex databases. By turning raw medical records into actionable insights, these innovations are reshaping the future of healthcare delivery and population health management.
At Gist.Science, we bridge the gap between cutting-edge research and public understanding by curating the latest preprints from medRxiv specifically within this domain. Our team processes every new submission in this category, providing both accessible plain-language explanations and detailed technical summaries to ensure the science is clear for everyone, from policymakers to curious readers. Below are the latest papers in health informatics, freshly distilled and ready for you to explore.
This study developed and validated a multi-label BERT-based natural language processing application that successfully extracts and categorizes various forms of violence, patient roles, and contextual details from unstructured mental health records, achieving high performance on most features except temporal aspects.
The authors have withdrawn their manuscript regarding a high-precision model for predicting ICU admission from ED triage due to identified analytical issues requiring revision, and they request that the work not be cited.
This paper presents a regulatory-informed statistical framework that quantifies the semantic and internal repeatability and reproducibility of large language models, demonstrating that these metrics vary significantly based on prompting strategies and model configurations, are often independent of diagnostic accuracy, and are essential for systematically evaluating LLM reliability in biomedical applications.
This study of 442 adults demonstrates that wearable-derived cardiovascular fitness age is a valid metric that significantly correlates with independent lifestyle factors like sleep quality and physical activity, while also tracking physiological improvements over time.
This study presents a pragmatic evaluation of AIFYA, a human-supervised large language model-based clinical decision support system aligned with Kenya's national newborn protocols, demonstrating successful implementation, high expert-rated accuracy, and strong user adoption in low-resource public health facilities.
This study reveals that despite achieving high diagnostic accuracy on full histories, large language models exhibit critical safety failures—including discouraging essential investigations and inappropriately downgrading triage for life-threatening conditions, with significant gender disparities—when evaluated against 1,000 synthetic clinical transcripts that simulate real-world medical complexity.
This study evaluates four major open health data repositories using a novel two-degree citation methodology to reveal that while open data consistently generates a ~10x indirect citation amplification across vastly different funding levels, significant disparities in global representation and persistent gender gaps in senior authorship highlight that data access alone cannot address structural inequities in research leadership.
Analyzing data from nearly 260,000 participants in the All of Us Research Program, this study demonstrates that integrating social determinants of health with demographics significantly improves chronic disease risk prediction, revealing that mental health outcomes are primarily driven by experiential factors like stress and discrimination, whereas cardiometabolic conditions are more strongly influenced by structural neighborhood characteristics, thereby supporting the adoption of condition-specific social screening and targeted interventions to reduce health disparities.
This study demonstrates that a social media-informed digital self-management platform, "Jang Geongang," significantly reduced population-level irritable bowel syndrome emergency department visits and unplanned hospitalizations in South Korea, particularly among younger adults and those with the diarrhea-predominant subtype, as evidenced by a controlled interrupted time series analysis of national health insurance data.
This study demonstrates that an LLM-based platform (Brim Analytics) achieves high accuracy and efficient processing for extracting cancer registry data from pathology reports, outperforming an ontology-driven system (DeepPhe) particularly in T stage classification across pancreatic and breast cancer cases.