📄 Category

health informatics

256 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

Development and Cross-Database External Validation of 24-Hour Landmark Models for ICU Mortality After Intracerebral Hemorrhage: Incremental Value of Routine Coagulation Markers

This study developed and externally validated 24-hour ICU mortality prediction models for intracerebral hemorrhage, finding that while multivariable models outperformed simple clinical benchmarks, routine coagulation markers offered no clearly supported incremental predictive value beyond standard clinical and physiologic data.

Pathak, S., Si, Y., Sanjaya, J., Haghi, M., Kudrot, N. T., Placencia, G., Alaei, K., Pishgar, M.2026-10-13
📄 health informatics

WHITE-Net : White matter HyperIntensities Tissue Extraction using deep learning Network

The paper introduces WHITE-Net, a robust and scalable 3D ResUNet-based deep learning framework trained on diverse multisite MRI data that achieves high-accuracy, generalizable, and fast automated segmentation of white matter hyperintensities without requiring parameter tuning.

Cathala, C., Roduit, V., Carneiro, F., Heffernan, M., Vanderbecq, Q., Colliot, O., Kherif, F., Thiran, J.-P., Bussy, A. (…)2026-10-12
📄 health informatics

Machine Learning-Supported Efficient VTE Risk Assessment using Routinely Collected Electronic Health Record Data

This study demonstrates that machine learning models utilizing routinely collected electronic health record data can effectively support real-time venous thromboembolism risk assessment with clinical validity comparable to clinician-recorded factors, offering a viable solution to reduce documentation burden while maintaining patient safety.

Li, Z., Yagis, E., Riad, A., Windrath-Carr, O., Arribas, M., Sodiq, T., Goldsmith, K., Glampson, B., Flott, K., Haji, G. (…)2026-10-09
📄 health informatics

Evidence-processing errors and their correction in an LLM-assisted systematic review: a retrospective methodological case study

This retrospective methodological case study investigates how documented evidence-processing errors affected an LLM-assisted systematic review, demonstrating that an auditable workflow successfully linked error detection and correction to released outputs while providing operational rules for future review teams.

Yin, C., Jing, Z., Zhang, Z.2026-10-08
📄 health informatics

Sense and Sensibility: Sensible Quality Control and Data Decision-Making in Passive Sensing Data for Adolescent Substance Use Risk Prediction

This paper provides principled recommendations for preprocessing passive sensing data from smartphones and wearables to improve adolescent substance use risk prediction, using the ABCD Study to illustrate how rigorous data quality control and transparent feature engineering can address challenges like outlier handling, platform differences, and protocol deviations.

Hong, A., Diaz, J. L., Kennedy, T. M., Wang, F. L.2026-10-08
📄 health informatics

A world model simulates the latent dynamics of human health

The paper introduces HealthFlux, a pan-modal world model that learns the latent dynamics of human health by integrating diverse multimodal data to continuously update a unified health state, enabling highly accurate long-term predictions of diseases and mortality while supporting virtual clinical trials.

Wang, Z., Zhang, O., Wang, J., Jiang, Y., Ma, Z. C., Guo, M., Al Dajani, S., Eames, A., Moldakozhayev, A., Poganik, J. R (…)2026-10-07
📄 health informatics

Validation of a two-stage automated screening pipeline for medical systematic reviews: a stratified concordance study with three independent human reviewers, using PABAK and Gwet's AC1

This study validates a two-stage automated screening pipeline's ability to achieve perfect concordance with human reviewers for automatically excluding records in systematic reviews, while demonstrating that the system fails to reliably identify include decisions due to low inter-rater agreement among humans and poor machine performance on the remaining uncertain cases.

Pirani, E., Costazza, D., Berti, P. P.2026-10-07
📄 health informatics

Interoperability of standardised electronic healthcare records facilitates transfer learning of clinical language models

This study demonstrates that standardizing electronic healthcare records to the OMOP common data model effectively enables the transfer learning of clinical language models across diverse UK datasets, achieving high predictive performance for emergency hospital readmission despite demographic differences and vocabulary limitations.

Remfry, E., Henkin, R.2026-09-30
📄 health informatics

What Embedding-Based Guideline-Literature Drift Measures (and What It Doesn't): A Proof-of-Concept and Mechanistic Dissection of TIDE in Orthopedic Surgery

This proof-of-concept study introduces and validates TIDE, an embedding-based metric that successfully tracks the temporal alignment between orthopedic guidelines and evolving literature by distinguishing divergent, convergent, and stable trajectories, while clarifying that the measure reflects topical shifts rather than evidentiary direction.

Grames, C., Zakarian, P., Franquemont, C., Cabrera, A., Elsissy, J.2026-09-29