📄 Category

health informatics

127 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.

The Risk Factors, Detection and Classification of Esophageal Cancer Using Ensemble Machine Learning Models

This study presents a robust ensemble machine learning framework utilizing a multi-seed strategy and Random Forest-based feature ranking to achieve near-perfect accuracy (98.3%) and zero false negatives in detecting esophageal cancer in Ethiopia, demonstrating that reduced feature sets focusing on dietary and environmental risk factors can effectively support early diagnosis in resource-limited settings.

Gaso, M. S., Mekuria, R. R., Cankurt, S., Deybasso, H. A., Abdo, A. A., Abbas, G. H.2026-03-11📄 health informatics

Co-designing a virtual reality based mindfulness application to address diabetes distress using Artificial Intelligence-informed Experience-Based Co-Design (AI-EBCD): a feasibility study

This feasibility study utilized Artificial Intelligence-informed Experience-Based Co-Design (AI-EBCD) to gather insights from adults with type 2 diabetes and mindfulness practitioners, resulting in a tailored prototype for a virtual reality mindfulness application aimed at alleviating diabetes distress.

Ghosal, S., Zhang, M., Stanmore, E., Sturt, J., Bogosian, A., Woodcock, D., Milne, N., Mubita, W., Robert, G., O'Connor, S.2026-03-11📄 health informatics

Regression vs. Medical LLMs: A Comprehensive Study for CVD and Mortality Risk Prediction

This study evaluates the performance of traditional regression models against medical large language models (MedLLMs) for predicting cardiovascular disease and mortality risks using the LURIC dataset, finding that while optimized MedLLMs and boosting techniques achieve competitive AUROCs up to 85%, MedLLMs require calibration adjustments to correct systematic over-prediction.

KOM SANDE, S. D., Skorski, M., Theobald, M., Schneider, J., Marz, W.2026-03-11📄 health informatics

Variability in Automated Sepsis Case Detection: A Systematic Analysis of Implementation Methods in Clinical Data Repositories

This systematic review reveals that significant methodological heterogeneity in automated sepsis case detection across MIMIC-III and eICU-CRD databases leads to widely varying detection rates, underscoring the urgent need for standardized reporting and open-source code to ensure reproducibility in sepsis research.

Meyer-Eschenbach, F., Schmiedler, R., Stoephasius, J. v., Zhang, C., Kronfli, L., Frey, N., Naeher, A.-F., Ehret, J., Nothacker, J., Kalle, C. v., Kohler, S., Gruenewald, E., Edel, A., Kumpf, O., Barr (…)2026-03-10📄 health informatics

Time-to-event modeling with multimodal clinical and genetic features improves risk stratification of liver complications in chronic hepatitis C

This study demonstrates that an interpretable, multimodal time-to-event framework integrating clinical, genetic, and socioeconomic data significantly improves the risk stratification of cirrhosis, hepatocellular carcinoma, and mortality in chronic hepatitis C patients compared to traditional fibrosis-based assessment.

Islam, H., Arian, A., Franses, J. W., Ahsan, H.2026-03-09📄 health informatics

Predictors of COVID-19 hospital outcomes: a machine learning analysis of the National COVID Cohort Collaborative

This study utilized machine learning on a large, harmonized cohort of hospitalized COVID-19 patients to demonstrate that while common structured electronic health record features offer moderate utility for predicting mortality, they are insufficient for forecasting length of stay, and that applying SMOTE to address class imbalance creates a critical trade-off between recall and discrimination that necessitates reporting threshold-dependent metrics for clinical utility.

Vazquez, J., Taylor, L., Chen, Y.-Y. K., Araya, K., Farnsworth, M. G., Xue, X., Hasan, M., N3C Consortium,2026-03-09📄 health informatics

A Novel Blended Hybrid Care Model for Maternal Mental Health: Cohort Study of Pregnant and Postpartum Patients

This pilot cohort study demonstrates that a novel blended hybrid care model combining synchronous virtual CBT with the asynchronous mindLAMP app significantly reduced anxiety and depression symptoms in pregnant and postpartum women, suggesting its potential to bridge critical treatment gaps in maternal mental health.

Calvert, E. I., Chen, K., Moon, K., Emerson, M. R., Feldman, N., Lager, C., Torous, J.2026-03-09📄 health informatics

Population differences in wearable device wear time: Rescuing data to address biases and advance health equity

This study analyzes Fitbit data from over 11,000 participants to reveal how demographic and health factors influence wearable device wear time, demonstrating that standard compliance thresholds disproportionately exclude data from disease populations and proposing a flexible methodological framework to mitigate these biases and advance health equity.

Hurwitz, E., Connelly, E., Sklerov, M., Master, H., Hochheiser, H., Butzin-Dozier, Z., Dunn, J., Haendel, M. A.2026-03-06📄 health informatics