180 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 proposes an explainable multimodal AI framework that integrates supervised deep learning with conservative offline reinforcement learning to support post-surgical clinical decisions, while simultaneously critiquing synthetic data practices and validating the system's architecture through a rigorous real-data methodology.
This registered report presents an artifact index for capacitive ECG signals acquired from an instrumented armchair, which utilizes a voting approach of three signal quality indices to effectively distinguish clean from artifact segments during reading and TV-watching activities, thereby enabling reliable continuous health monitoring in unsupervised real-world settings.
The paper introduces PolyCLIP-T, a topology-guided multimodal framework that leverages whole-genome sequencing and persistent homology to identify stable clusters of polygenic variants across familial multimorbid disorders, effectively overcoming the limitations of traditional rule-based pipelines in detecting non-coding, structural, and low-penetrance genetic drivers.
This study evaluates three photoplethysmography-derived microvascular reactivity signals in the MIMIC-IV-WDB v0.1.0 dataset using human and AI-assisted visual inspection, finding that two signals failed to capture their intended physiology in most cases and the third was limited by sensor placement, thereby highlighting the necessity of preliminary raw-data validation before downstream modeling.
This paper introduces BodyMAE, a surface-area aware masked autoencoder that leverages metric-scale 3D body scans to accurately estimate body composition metrics like fat and lean mass, achieving high correlation with clinical DXA measurements while overcoming challenges such as nonuniform point density and device variability.
Genosolver is an integrated workflow that leverages Large Language and Reasoning Models to extract detailed clinical insights from unstructured narratives, significantly improving rare disease diagnosis rates and outperforming existing tools like Exomiser by effectively prioritizing causative genetic variants.
This paper presents a methodological framework for localizing the open-source medical imaging software 3D Slicer into Brazilian Portuguese, addressing specific linguistic and terminological challenges to enhance accessibility for non-English-speaking users in Latin America.
This paper proposes K-NeSyNet, a novel knowledge-driven neuro-symbolic framework that integrates a multi-modal oncology knowledge graph with a differentiable three-channel symbolic reasoning mechanism and adaptive neural fusion to deliver accurate, safe, and interpretable personalized cancer treatment recommendations.
This paper empirically demonstrates that relying solely on identifier matching for biomedical knowledge graph integration is insufficient, revealing that while cross-ontology and embedding-based methods increase coverage, they systematically introduce clinically significant failure modes like over-merging and semantic collapse that obscure critical distinctions in downstream applications.
This paper introduces an Exploratory AI Recommender that leverages explainable AI to generate data-driven recommendations for feature selection, non-linear terms, and interactions, thereby significantly enhancing the predictive performance and interpretability of high-dimensional clinical models like the Cox Proportional Hazards model.