256 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 paper presents MOE-ECG, a multi-objective ensemble fusion framework that utilizes particle swarm optimization and Dempster-Shafer theory to simultaneously optimize predictive performance and model diversity, achieving robust and accurate atrial fibrillation detection across multiple ECG datasets.
The paper proposes HealthFormer, a dual-level, time-aware Transformer framework pretrained on large-scale longitudinal EHRs using multi-task self-supervision to learn hierarchical, event-centric patient representations that achieve state-of-the-art performance in incident cancer prediction through straightforward fine-tuning.
This study demonstrates that Federated Learning effectively enables privacy-preserving, multi-site machine learning for HIV care across diverse international cohorts, achieving performance comparable to centralized models while significantly outperforming local site-specific approaches.
This study demonstrates that passive monitoring of wearable-derived data, specifically reduced step counts and elevated resting heart rates, can effectively identify heart failure patients at increased risk of hospitalization up to three days in advance.
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.