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 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.
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.
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.
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.
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.
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.
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.
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.
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.
This study demonstrates that a locally deployed 20-billion parameter LLM, utilizing a sensitivity-enhanced prompting strategy, can significantly accelerate systematic review abstract screening with high accuracy and zero data privacy risks, though its performance varies by domain and is best used as a second screener alongside human experts.