Artificial Intelligence Medical Compendium

OBJECTIVE: Sleep disorders are frequent but undercharacterized comorbidities in developmental and epileptic encephalopathies (DEEs). We aimed to estimate the frequency, spectrum, and burden of sleep disturbances across genetic DEE subtypes using real... read more 

Online learning in nonstationary environments is essential for data-stream applications, yet its reliability is often compromised by the coexistence of concept drift and verification latency. Delayed labels may introduce temporal inconsistency in mod... read more 

The integration of Artificial Intelligence (AI) into psychiatry is progressing rapidly, with promising yet underexplored applications in suicide prevention. While most existing studies have focused on suicide risk prediction, AI could be applied more... read more 

BackgroundOccupational accidents in open-pit mining remain a significant workplace safety and public health concern due to hazardous environmental conditions, high-risk tasks, and complex human-organizational interactions. Data-driven risk modeling m... read more 

INTRODUCTION: Electrocardiographic (ECG) criteria for left atrial enlargement (LAE) serve as clinical surrogates for structural and hemodynamic left atrial (LA) overload. Their diagnostic performance compared to echocardiography remains suboptimal, a... read more 

Introduction Blood-based biomarkers can accurately detect Alzheimer's disease (AD) pathology, but symptom-guided testing approaches are understudied. This study evaluated the utility of early symptom and biomarker changes in predicting cognitive decl... read more 

Sepsis-related myocardial injury (SRMI) is associated with high mortality, but the transportability of prediction models across heterogeneous cohorts remains uncertain. We developed and externally validated a machine learning model for in-hospital mo... read more 

Accurate forecasting of fine particulate matter (PM2.5) is critical for mitigating severe health risks in South Asia, yet it remains challenging in data-scarce regions like Bangladesh. While spatiotemporal graph neural networks (GNNs) offer a promisi... read more 

Non-obstructive azoospermia (NOA) is a severe male infertility disorder. This study aims to identify immune-related biomarkers for explore immune-related molecular signatures associated with NOA. Here, we integrated transcriptomic data from four inde... read more 

PURPOSE: To examine the pupillary light reflex (PLR) in Immunoglobulin G4-related ophthalmic disease (IgG4-ROD) compared with healthy controls using virtual reality-based infrared pupillometry. DESIGN: Cross-sectional matched case-control study. PART... read more