180 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, which originally aimed to analyze the causal impact of NLP-driven clinical decision support on sepsis mortality in England, has been withdrawn from medRxiv due to the submission of false information.
This paper has been withdrawn from medRxiv because it was submitted with false information, rendering its original findings on NLP-augmented clinical decision support invalid.
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.
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.
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.
This study analyzed over 410,000 Reddit posts to reveal that while gastrointestinal symptoms are the most common self-reported side effects of semaglutide and tirzepatide, online communities also highlight emerging concerns like reproductive and temperature-related issues that are often underrepresented in clinical trials and official labeling.
This paper introduces a modular, adapter-based framework that decouples reasoning supervision from reward tuning to stabilize the alignment of small clinical language models, achieving robust, auditable, and accurate performance without sacrificing privacy or efficiency.
This paper presents the Orphanet Nomenclature and Classification system as a comprehensive, multilingual, and interoperable standard for rare diseases, detailing its July 2025 update which includes nearly 10,000 clinical entities with extensive mappings to major medical terminologies to facilitate global data sharing, accurate patient identification, and improved healthcare outcomes.
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.
This study demonstrates that a cross-attention-based multimodal deep learning framework, which effectively integrates dermoscopic images with patient metadata, outperforms both image-only and conventional fusion models in diagnosing skin lesions with superior accuracy and calibration.