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 paper presents a reproducible Python-based workflow that simulates and integrates early-phase oncology clinical, biomarker, and pharmacokinetic data to generate analysis-ready datasets, visualizations, and exploratory predictive models for translational decision support.
This study demonstrates that multimodal machine learning models integrating structured electronic health record data with clinical note embeddings can accurately predict high inpatient opioid exposure, thereby supporting targeted opioid stewardship efforts.
This study demonstrates that a hybrid machine learning framework combining gradient boosting and Hidden Semi-Markov Models can accurately classify four menstrual cycle phases using only self-reported symptoms, achieving 67.6% accuracy and establishing symptom dynamics as a scalable, device-free digital biomarker for reproductive health.
This prospective pilot study demonstrates that DR. INFO, an agentic AI clinical assistant, significantly improves time efficiency and clinical decision support for physicians in Portuguese healthcare settings, achieving high satisfaction scores and a Net Promoter Score of 81.2 without evidence of attrition bias.
This paper introduces MedScope, a lightweight benchmarking framework that systematically evaluates six open-source large language models on medical multiple-choice questions using multi-dimensional metrics and visual analyses, revealing significant performance heterogeneity and highlighting their current unsuitability for unsupervised high-risk clinical deployment despite their value as transparent baselines.
This study presents and evaluates a multi-stage pipeline that combines a fine-tuned Bio-ClinicalBERT token classifier with locally deployed open-source large language models for revision, demonstrating that this hybrid approach significantly improves the accuracy and efficiency of extracting structured Age-Friendly 4M (What Matters, Medication, Mentation, and Mobility) information from informal nursing home text messages compared to single-stage models.
MedResearchBench introduces the first multi-domain benchmark specifically designed to evaluate AI research agents on clinical medical tasks by leveraging public datasets and high-quality ground truth to assess performance across seven clinical domains using six medical-specific dimensions, thereby addressing the critical gap in evaluating AI's ability to conduct publication-quality, clinically sound research.
This paper introduces and validates VaaS, a multi-layer, cost-effective pipeline that reduces AI hallucinations in scientific citation generation to near-zero levels through iterative refinement and rigorous benchmarking, thereby enabling reliable AI-assisted biomedical research at production scale.
The SwissPedGrowth project demonstrates the feasibility of extracting high-quality anthropometric data from heterogeneous Swiss children's hospital electronic health records for growth research, despite challenges regarding data completeness and the need for weighting to ensure population representativeness.
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.