225 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 demonstrates that when carefully prompted, ChatGPT-4.0 outperforms human coders in generating high-quality, detailed, and complete descriptions of innovations within NIH grant abstracts focused on opioid addiction treatment.
This paper introduces Sino-US-DrugQA, a bilingual benchmark dataset of 11,871 questions derived from US and Chinese pharmaceutical regulations, which reveals that while current large language models perform well on monolingual regulatory queries, they face significant challenges in cross-jurisdictional comparative reasoning, necessitating expert oversight for high-stakes compliance applications.
In a study using ASPREE trial data, Random Survival Forests (RSF) demonstrated comparable discrimination and calibration to standard Random Forests (RF) for predicting disability-free longevity, suggesting that RSF does not always offer superior predictive accuracy over RF for time-to-event outcomes.
This study demonstrates that a standardized prompt-based framework achieves clinical-grade accuracy across five large language models from four independent vendors when analyzing biomarkers from 4,018 NHANES patients, confirming the feasibility of vendor-independent AI systems for clinical decision support.
This paper presents a computational pipeline that combines asymmetric semantic aggregation of patient phenotypes with network propagation to improve the accuracy and clinical consistency of rare disease diagnosis, outperforming existing methods in identifying correct diagnoses and generating coherent differential hypotheses.
This study presents a deep learning framework based on a vision foundation model that not only accurately predicts retinal biological age from fundus images but also disentangles physiological heterogeneity by decomposing aging signals into normative and pathological components linked to systemic health factors like inflammation and hemodynamics.
Researchers developed and validated a machine learning-based Neurological Health Score (NHS) using genetic, lifestyle, and biochemical data, demonstrating that higher scores significantly correlate with a reduced risk of various neurological disorders and improved social well-being.
This study evaluates and compares various statistical and machine learning imputation methods for handling missing data in esophageal cancer patient-reported outcomes to provide evidence-based recommendations for improving research validity.
This study reveals a critical dissociation between abstract ethical reasoning and clinical memory in large language models, finding that while most models can resist harmful commands, very few can successfully recall patient-specific contraindications like allergies during ICU simulations.
This study presents a patient-centric Markov-chain framework that utilizes eight years of de-identified specialty pharmacy data to predict medication adherence risks, identifying systemic barriers like cost and administrative delays as primary drivers of non-adherence to support more equitable patient-assistance strategies.