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.
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.
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.
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.
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.
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.
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.