health informatics 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

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

Thyroid Cancer Risk Prediction from Multimodal Datasets Using Large Language Model

This paper proposes a deep learning framework that integrates Vision Transformer-extracted MRI features with a domain-adapted language model's analysis of clinical text via cross-modal attention to achieve superior accuracy in predicting thyroid cancer compared to unimodal approaches.

Ray, P.2026-03-06

📄 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

Predicting cardiovascular risk under intervention: Development and internal validation of the CHARIOT Model in 19 million adults

The CHARIOT model, developed and internally validated using electronic health records from over 19 million UK adults, uniquely predicts 10-year cardiovascular risk reductions under specific interventions like statins or lifestyle changes to enable actionable, patient-centred primary prevention.

Pate, A., Jiang, B., Huang, Y.-T., Griffiths, S., Stables, D., Peek, N., McMillan, B., Sperrin, M.2026-03-05

📄 health informatics

TDA Engine v2.1: A Computational Framework for Detecting Structural Voids in Spatially Censored Epidemiological Data with Temporal Classification and Causal Inference

The TDA Engine v2.1 is a topological framework that mathematically distinguishes structural data voids from stochastic gaps in censored epidemiological data by integrating temporal classification and causal inference to guide public health investigations.

Mboya, G. O.2026-03-05

📄 health informatics

Agent Role Structure and Operating Characteristics in Large Language Model Clinical Classification: A Comparative Study of Specialist and Deliberative Multi-Agent Protocols

This study demonstrates that, even with fixed model parameters, altering the internal role decomposition of multi-agent LLM systems from a generic deliberative to a feature-specialist structure acts as a structured inductive bias that systematically reshapes sensitivity-specificity trade-offs and error distributions across different clinical classification tasks.

Anderson, C. G.2026-03-05

📄 health informatics

Red-Teaming Medical AI: Systematic Adversarial Evaluation of LLM Safety Guardrails in Clinical Contexts

This paper presents a systematic red-teaming framework for evaluating medical AI safety, revealing that while standard guardrails effectively block most adversarial attacks, they remain significantly vulnerable to authority impersonation strategies—particularly those framing requests as educational inquiries—which trigger behavioral mode-switching rather than factual errors.

Ekram, T. T.2026-03-05

📄 health informatics

Enhancing Prediabetes Diagnosis from Continuous Glucose Monitoring Data via Iterative Label Cleaning and Deep Learning

This paper presents a hybrid deep learning framework that combines iterative XGBoost-based label refinement with a Convolutional-Bidirectional LSTM model to significantly improve prediabetes diagnosis from Continuous Glucose Monitoring data by correcting misclassifications in the AI-READI dataset and achieving high diagnostic accuracy with reduced clinical burden.

Arethiya, N. J., Krammer, L., David, J., Bakshi, V., BasuChoudhary, A., Bhuiyan, U., Sen, S., Mazumder, R., McNeely, P.2026-03-05

📄 health informatics

Trustworthy personalized treatment selection: causal effect-trees and calibration in perioperative medicine

This paper proposes a deployment-ready framework that integrates causal inference, interpretable effect-trees, and calibration assessment to distinguish actionable treatment heterogeneity from unreliable noise, thereby enabling trustworthy personalized treatment selection in perioperative medicine.

Mittelberg, Y., Stiglitz, D. K., Kowadlo, G.2026-03-04

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