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.
RenalTransLSTM is a hybrid deep learning framework that combines LSTM and Transformer architectures to achieve superior multi-horizon prediction of acute kidney injury in ICU patients by effectively capturing both local temporal dynamics and global contextual dependencies in electronic health records.
This study introduces the Embedding-Distance Gait Biomarker (EDGB), a supervised contrastive learning framework that utilizes a compact convolutional neural network to extract robust 32-dimensional latent representations from raw wearable IMU signals, achieving high accuracy in distinguishing between healthy, neurological, and orthopedic gait patterns while demonstrating strong reliability and significant group differentiation.
This target trial emulation study using real-world data demonstrates that combining PD-1/PD-L1 inhibitors with VEGFR tyrosine kinase inhibitors significantly improves restricted mean survival time in renal cell carcinoma patients compared to VEGFR tyrosine kinase inhibitor monotherapy, supporting the generalizability of combination therapy despite non-proportional hazards.
The paper introduces PulseOx-FM, a self-supervised foundation model trained on millions of wearable pulse oximetry segments that outperforms existing methods in predicting diverse cardiometabolic and neuropsychiatric health risks, including future hypertension and next-day glycemic states, thereby establishing a powerful non-invasive tool for global health risk stratification.
This paper introduces the Heterogeneous Gated Graph Transformer (HGGT), a deep learning model that leverages complex, multi-type relationships among biomedical entities to significantly improve the prediction of clinical trial success across multiple phases.
This pilot study demonstrates the technical feasibility of using generative video AI to create standardized psychiatric patient simulations with graded hygiene deterioration, while highlighting that although appearance modulation is achievable, fine-motor artifacts necessitate expert human oversight before clinical deployment.
This study demonstrates that while gradient boosting models using structured electronic health record data can predict depression and anxiety progression in multiple sclerosis patients, their limited predictive power (R² ≤ 0.28) is dominated by baseline scores reflecting regression to the mean, indicating that richer data sources beyond structured clinical variables are necessary for meaningful individual-level forecasting.
This study employs an AI-driven multimodal mediation framework to analyze data from the All of Us Research Program, revealing that psychosocial vulnerability acts as a key latent mediator linking socioeconomic disadvantage to cardiometabolic multimorbidity.
This paper introduces the Demographic Calibration Gap Score (DCGS) to demonstrate that standard global calibration methods fail to address systematic prediction errors across racial and gender subgroups in breast cancer risk models, particularly under distributional shifts, thereby highlighting the need for subgroup-specific calibration metrics to prevent biased clinical decisions.
A global survey of 766 mental health professionals reveals that over half are already using generative AI tools, primarily for treatment planning and administrative tasks, yet this widespread adoption occurs despite a significant lack of institutional guidance, formal training, and regulatory frameworks.