Prescriptions

Attention-based Multimodal Spatiotemporal Enhanced Interaction Network For Major Depressive Disorder Detection.

Although deep learning models have shown promising results in detecting major depressive disorder (MDD), two main limitations remain: insufficient exploitation of interactive information across multimodal brain networks and a lack of adaptive mechanisms for capturing crucial spatiotemporal dependencies among brain regions. To address these challenges, we propose the Attention-based Multimodal Spat...

Beyond the hype: A simulation study evaluating the predictive performance of machine learning models in psychology.

Although machine learning (ML) methods are gaining popularity in psychological research, the debate about their usefulness ranges from hype to disillusionment. The discrepancy between the hopes placed in ML methods and the empirical reality is often attributed to the quality of psychological data sets, which tend to be small and subject to imprecise measurement. In this simulation study, we examin...

Improving Long-Term Adherence to Endocrine Therapy Among Breast Cancer Survivors: Development of a Multiscale Modeling and Intervention System.

BACKGROUND: Breast cancer is a significant public health burden. Despite its critical role in preventing the recurrence of breast cancer, rates of lon...

Capturing induced-fit effects: A geometry-aware and interpretable framework for robust drug-target affinity prediction.

Accurate drug-target affinity (DTA) prediction is pivotal for virtual screening, yet practical reliability is often limited by the static treatment of...

Predicting early and complete drug release from long-acting injectables using explainable machine learning.

Polymer-based long-acting injectables (LAIs) have transformed the treatment of chronic diseases by enabling controlled drug delivery, thus reducing do...

A multi-target drug design method based on target feature fusion.

BACKGROUND: Targeted drugs are medications designed to treat diseases by targeting specific sites on cancerous or diseased cells. Multi-target drugs c...

Evolution and forecasting of antiretroviral therapy utilisation in British Columbia: a population-level analysis of provincial drug treatment programme data, 2014-2024.

OBJECTIVE: To characterise temporal trends in antiretroviral therapy (ART) utilisation and forecast short-term changes in regimen distribution within ...

Deep learning-based breast cancer detection with customized ensemble attention.

Breast cancer remains one of the leading malignancies globally, and accurate diagnostic decisions at the early stages of the disease can significantly...

A Qualitative Exploration of Ethical Aspects of Using AI in Parkinson Disease: Patient Panel Study.

BACKGROUND: As Parkinson disease (PD) rates increase, so does interest in finding new technological solutions for PD management. Despite substantial e...

Patient Concerns Regarding Artificial Intelligence Applications in Health Care: Systematic Review and Meta-Synthesis Based on Social Ecological Theory.

BACKGROUND: The use of artificial intelligence (AI) in health care is growing quickly, but there is not enough research that looks at patient concerns...

Ensemble machine learning framework with SHAP and LIME for accurate early prediction of student success in online learning environments.

The development of digital learning environments has generated rich educational data capable of supporting early prediction of student outcomes. In th...

How host-guest interaction types shape value co-creation in urban tourism: The moderating role of AI technology stimuli.

This study examines how different types of host-guest interaction relate to tourists' value co-creation intention in urban tourism and whether artific...

Taste-active peptides, end-to-end: from generation and sensory validation to AI-driven discovery.

Taste-active peptides (TAPs) are food-protein-derived peptides that elicit or modulate gustatory sensations; presently gaining interest as natural uma...

LEP-AD: language embedding of proteins and attention to drugs predicts drug-target interactions.

INTRODUCTION: Predicting drug-target interactions remains a significant challenge in drug development and lead optimization. Recent advances have leve...

Artificial intelligence and computerized decision support in adult intensive care: A systematic review of randomized controlled trials.

BACKGROUND: Artificial intelligence (AI) and computerized clinical decision support systems (CDSS) are increasingly applied in intensive care, yet the...

DrugGPS: Attention-guided multimodal fusion for intelligent exploration of drug-target and drug-disease interactions.

BACKGROUND AND PURPOSE: Accurate prediction of drug-target interactions (DTIs) and drug-disease interactions (DDIs) are critical for accelerating the ...

Dual-Sabatier Optima: How Reaction Mechanism Determines Activity Volcano Map of Dual-Atom Catalysts for Oxygen Reduction Reaction.

Dual-atom catalysts (DACs) have demonstrated superior potential in the oxygen reduction reaction (ORR). However, the single-peak activity volcano deri...

Algometric assessment of sensitization and habituation as predictive markers for OnabotulinumtoxinA response in chronic migraine.

BACKGROUND: Chronic migraine is a debilitating disorder characterized by central sensitization and impaired habituation. Although OnabotulinumtoxinA (...

The Dark Side of Love: Prediction of Digital Intimate Partner Violence and Associated Factors Among University Students Using Machine Learning.

This study aimed to identify key risk factors and predict digital intimate partner violence (DIPV) exposure and perpetration among university students...

Ai-enhanced clinical decision support reduces medication errors and adverse drug events in a multicenter teaching hospital network: A prospective randomized controlled trial.

BACKGROUND: Medication errors remain a leading source of preventable harm in hospitalized patients, contributing to adverse drug events (ADEs), prolon...