📄 Category

health informatics

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.

📄 health informatics

Falsification Testing of Sepsis Prediction Models: Evaluating Independent Biological Signal After Controlling for Care-Process Intensity

This pre-registered falsification study across four clinical datasets demonstrates that while sepsis prediction models at elite academic centers primarily detect genuine biological signals rather than care-process intensity, they reveal a systematic and consequential divergence between clinical sepsis definitions and administrative coding that undermines the validity of regulatory metrics and AI benchmarks relying on the latter.

Dickens, A. R.2026-03-18
📄 health informatics

Persistent Proxy Discrimination in HIV Testing Prediction Models: A National Fairness Audit of 386,775 US Adults

This national fairness audit of 386,775 US adults demonstrates that enforcing demographic parity in HIV testing prediction models is inappropriate for differential-burden clinical contexts, as it significantly reduces screening access for high-risk populations and underscores the need for fairness metrics like equalized odds and calibration that align with clinical needs.

Farquhar, H.2026-03-16
📄 health informatics

WITHDRAWN: Causal Effects of Natural Language Processing-Enhanced Clinical Decision Support on Early Cognitive Impairment Detection: A Propensity Score Analysis Using Inverse Probability of Treatment Weighting

This paper is a withdrawn study from medRxiv that claimed to analyze the causal effects of natural language processing-enhanced clinical decision support on early cognitive impairment detection, but was retracted because it was submitted with false information.

Dimitriou, A., Foster, M.2026-03-16
📄 health informatics

WITHDRAWN: Blockchain-Enabled Health Information Exchange Efficiency Across South Korean Hospital Networks: A Stochastic Frontier Analysis with Bayesian Model Averaging

This withdrawn study utilized Stochastic Frontier Analysis with Bayesian Model Averaging on a panel of 247 South Korean hospital networks to demonstrate that blockchain-enabled health information exchange systems significantly improve technical efficiency compared to conventional platforms, even after controlling for endogeneity and model uncertainty.

Park, J.-H., Kim, S.-Y.2026-03-16
📄 health informatics

Reward-Guided Generation Improves the Scientific Utility of Synthetic Biomedical Data

The paper introduces RLSYN+REG, a reinforcement learning-driven generative model that significantly enhances the scientific utility of synthetic biomedical data by ensuring regression models trained on it accurately reproduce the coefficients and predictions of models trained on real data, while maintaining high fidelity and privacy.

Jackson, N. J., Espinosa-Dice, N., Yan, C., Malin, B. A.2026-03-16
📄 health informatics

Early Parkinson's Revealed by Unlocking Longitudinal Omics at Population Scale

The study introduces Chronos, a privacy-preserving framework that links archived plasma samples with longitudinal clinical records to identify early molecular signatures of Parkinson's disease years before symptom onset, achieving a predictive accuracy of 0.76 across multiple independent cohorts.

Feng, C., Kosti, I., Guo, Y., Wang, Y., Watson-Haigh, N. S., File, B., Hin, N., Nanasi, T., Guo, J., Suchecki, R., Tearl (…)2026-03-14
📄 health informatics

Comparative Evaluation of Logistic Regression and Gradient Boosting Models for Influenza Outbreak Early-Warning Using U.S. CDC ILINet Surveillance Data (2010-2025)

This study demonstrates that both logistic regression and gradient boosting models achieve near-perfect accuracy in detecting national influenza outbreaks using U.S. CDC ILINet surveillance data from 2010 to 2025, validating the operational utility of framing early-warning as a threshold-based binary classification problem.

Onwuameze, C. N., Madu, V.2026-03-13