Prescriptions

Explainable AI for mortality prediction: a comparative study using the MIMIC-III dataset.

OBJECTIVES: Predicting mortality is vital for tailoring treatments, improving care and reducing costs. Machine learning (ML) has shown strong potential, often outperforming traditional severity-of-illness scoring systems in intensive care units (ICUs). However, the black-box nature of ML limits adoption. This study evaluates the accuracy of several ML algorithms on the Medical Information Mart for...

Hypnosis reshapes multilevel stress response and enhances executive performance in stressed medical students.

Stress and anxiety impair executive function and degrade performance, yet rapid and scalable interventions remain limited. This controlled study tested whether a single personalized hypnosis session could enhance stress regulation and cognitive performance during negative memory recall in medical students. Forty-nine final-year students were assigned to hypnosis or a breath-focused attention condi...

Explainable deep learning framework incorporating medical knowledge for insulin titration in diabetes.

BACKGROUND: Deep learning has shown promise in diabetes management but faces challenges in real-world application due to its "black-box" nature, chara...

AI-Enabled Smart Glasses for Active Aging: Scoping Review.

BACKGROUND: The daily use of digital technologies is transforming the day-to-day lives of older adults. Among these technologies, artificial intellige...

Ultrasound-Responsive Serotonylation and Hallmark Pathway Gene Signatures Reveal Tumor Microenvironment Vulnerabilities and Prognostic Subtypes in Colorectal Cancer.

BACKGROUND: Focused ultrasound, low-intensity focused ultrasound, and microbubble-enhanced sonoporation are examples of ultrasound-based cancer therap...

Antiviral drug discovery and development: challenges and future directions.

The coronavirus disease 2019 (COVID-19) pandemic has stimulated extensive endeavors toward the development of therapeutic interventions targeting seve...

Real-World Impact and Educational Effectiveness of an AI-Powered Medical History-Taking System: Retrospective Propensity Score-Matched Cohort Study.

BACKGROUND: Medical history-taking is a core clinical skill; yet, traditional teaching methods face challenges. We developed an artificial intelligenc...

Longitudinal multisource clinical model for early lung cancer risk stratification and screening.

OBJECTIVES: Lung cancer is the leading cause of cancer-related mortality worldwide, with poor prognosis largely due to late-stage diagnosis. Current s...

A switching-based deep learning framework for personalized and adaptive E-commerce recommendations.

In recommendation systems, it is bit challenging to address diverse user profiles, particularly when users demonstrate varied interaction histories an...

Protocol for the development and validation of a questionnaire to evaluate user-robot interaction in the compounding of hazardous drugs in a hospital setting.

INTRODUCTION: The automation of hazardous drug preparation in hospitals using robotic systems is an effective strategy to enhance safety, quality, and...

DVMMHGNN: A Dual View Multi-Modal Heterogeneous Graph Neural Network with Contrastive Learning for Microbe-Informed Drug Repurposing.

Drug repurposing (DR) offers an efficient and cost-effective strategy for pharmaceutical development by identifying new therapeutic applications for e...

Cardiorespiratory Markers of Type 2 Diabetes: Machine Learning-Based Analysis.

BACKGROUND: The global prevalence of type 2 diabetes mellitus (T2DM) poses significant challenges due to its association with increased cardiovascular...

CMCL-DDI: Pharmacophore-aware cross-view contrastive learning for drug-drug interaction prediction.

Accurate prediction of potential drug-drug interactions (DDIs) is vital for ensuring medication safety and efficacy. Existing graph-based methods typi...

Multi-relational knowledge graph for drug-drug interaction prediction via dual aggregation and collaborative optimization.

Identifying potential drug-drug interactions is crucial in clinical care and new drug development, as mutual interference between drugs can lead to ad...

Machine learning-assisted HS-GC-IMS for discrimination and traceability of baby bottles based on volatile fingerprints.

Ensuring the safety of food contact materials, particularly baby bottles, is crucial for infant health. In this study, a comprehensive analytical stra...

Beyond Partitioning: Using Force Field Science to Evaluate Electrostatics Models.

Accurate models for electrostatic and induction interactions are fundamental for computational molecular science, including drug discovery, studies of...

Reproducibility of cardiac volumetric parameters derived from fully automatically prescribed image planes: a direct comparison to manual planning at 1.5-T and 3-T MRI.

OBJECTIVES: The prescription of cardiac MRI (CMR) image planes is essential for comparable volumetric assessment, but manual planning is time-consumin...

Risk Modeling and Association Pathways Integrating Psychosocial Factors for Drug Misuse in Adolescents and Young Adults: A Machine Learning Approach.

INTRODUCTION: Early identification for preventing drug misuse among adolescents and young adults (AYAs) is more cost-effective than drug treatment. Ho...

The Trial Bank initiative to promote trustworthy living evidence for medication safety (PROMISE) of children: design and pilot validation.

BACKGROUND: While medications are essential for preventing and treating disease, they can also cause harm. Evidence synthesis has been widely adopted ...

CL-MHAD: Contrastive Learning-based Multi-Hypergraph Aggregation and Diffusion model for prescription recommendation.

Multiple syndrome-based prescription recommendations are significant for personalized diagnosis and treatment in Traditional Chinese Medicine (TCM). H...