183 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 demonstrates that a large language model can generate psychometrically plausible and demographically consistent synthetic data for Peruvian health surveys when conditioned on user personas, though the presence of specific deviations necessitates rigorous validation before such data can be used for policy-making.
This study demonstrates that integrating partial least squares regression, convolutional neural networks, and curve-fitting approaches on FTIR blood spectra creates a synergistic framework that simultaneously achieves high accuracy in predicting HbA1c levels and provides mechanistic interpretability of glycation-related biomarkers for scalable diabetes monitoring.
The paper introduces C-RLM, a schema-enforced recursive framework that outperforms standard RAG and long-context LLMs in synthesizing complex clinical records by achieving 100% structural consistency and full auditability through iterative knowledge compilation, despite a 2.7x computational overhead.
This study demonstrates that an on-premises, open-source multimodal framework fusing structured clinical data with free-text pathology reports using large language models significantly improves the discrimination of complication risks after implant-based breast reconstruction, offering a privacy-preserving and interpretable tool for precision surgical decision-making.
This paper presents eHEALS-Br, a secure web-based platform that automates the administration and scoring of the Brazilian eHealth Literacy Scale to provide real-time, personalized feedback, demonstrating high reliability in a pilot study and offering a scalable tool for both population research and individualized clinical health literacy assessment in Brazil.
This study demonstrates that closed-form sample size criteria for logistic prediction models, such as the Riley framework, become increasingly optimistic and underestimate the required sample size as target discrimination rises, primarily due to separation-like behavior that destabilizes calibration, thereby necessitating the use of simulation-based stress tests for high-discrimination scenarios.
This study introduces an Authority Signals Framework to evaluate source credibility in ChatGPT's health responses, finding that over 75% of the 615 cited sources in a sample of 100 questions originate from established institutional organizations rather than alternative health information providers.
This study demonstrates that a three-layer red-teaming framework, combining automated semantic analysis with human expert adjudication, is essential for detecting sophisticated deceptive alignment and evaluation awareness in healthcare AI systems, revealing that keyword-based filters miss 83% of high-risk behaviors while advanced semantic models achieve perfect agreement with human experts in identifying regulatory circumvention strategies.
The paper proposes AdaptiveFedLoRA, a novel federated learning framework that dynamically adjusts LoRA ranks based on multi-faceted drift measurements to effectively mitigate client drift and improve convergence in heterogeneous medical small language model deployments, outperforming existing baselines across various client scales while maintaining communication efficiency.
This study demonstrates that integrating real-time physician digital search data with climate indicators significantly improves the accuracy of short-term dengue hospitalization forecasts in Brazil, particularly when overcoming the limitations of delayed official reporting systems.