AIMC Topic: Cross-Sectional Studies
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Insights Into the Current and Future State of AI Adoption Within Health Systems in Southeast Asia: Cross-Sectional Qualitative Study.
Journal of medical Internet research
Jun 16, 2025
BACKGROUND: Artificial intelligence (AI) holds potential to enhance health systems worldwide. However, its implementation in health systems in Southeast Asia (SEA)-a region of diverse geopolitical and socioeconomic development-has been understudied.
Development of an explainable machine learning model for predicting depression in adolescent girls with non-suicidal self-injury: A cross-sectional multicenter study.
Journal of affective disorders
Jun 15, 2025
Non-suicidal self-injury (NSSI) in adolescent girls is a critical predictor of subsequent depression and suicide risk, yet current tools lack both accuracy and clinical interpretability. We developed the first explainable machine learning model integ...
Mental Health Issues and 24-Hour Movement Guidelines-Based Intervention Strategies for University Students With High-Risk Social Network Addiction: Cross-Sectional Study Using a Machine Learning Approach.
Journal of medical Internet research
Jun 13, 2025
BACKGROUND: The exponential growth of digital technologies and the ubiquity of social media platforms have led to unprecedented mental health challenges among college students, highlighting the critical need for effective intervention approaches.
Prediction of Insulin Resistance in Nondiabetic Population Using LightGBM and Cohort Validation of Its Clinical Value: Cross-Sectional and Retrospective Cohort Study.
JMIR medical informatics
Jun 13, 2025
BACKGROUND: Insulin resistance (IR), a precursor to type 2 diabetes and a major risk factor for various chronic diseases, is becoming increasingly prevalent in China due to population aging and unhealthy lifestyles. Current methods like the gold-stan...
Exploring supportive care needs of lung cancer patients in China and predicting with machine learning models.
Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer
Jun 13, 2025
PURPOSE: This study aims to explore the level of supportive care needs among hospitalized lung cancer patients in China, explore the key influencing factors and use machine learning (ML) to develop predictive models for the level of supportive care n...
Assessing training needs and influencing factors among personnel at centers for disease control and prevention in northeast China: a cross-sectional study framed by SDT and TPB using machine learning techniques.
BMC public health
Jun 10, 2025
OBJECTIVES: Training public health personnel is crucial for enhancing the capacity of public health systems. However, existing research often falls short in providing a comprehensive theoretical framework and fails to account for the intricate interp...
Predicting Stress, Anxiety, and Depression in Adult Men Based on Nutritional and Lifestyle Variables: A Comparative Analysis of Machine Learning Methods.
Journal of food science
Jun 1, 2025
Mental health disorders like depression, anxiety, and stress (DAS) are rising globally. Understanding how diet and lifestyle influence these conditions is vital for targeted interventions. This study explores the potential of machine learning (ML) to...
Development of Machine-Learning-Based Models for Detection of Cognitive Impairment in Patients Receiving Maintenance Hemodialysis.
European journal of neurology
Jun 1, 2025
BACKGROUND: Cognitive impairment is common but frequently undiagnosed in the dialysis population. We aimed to develop and validate a quick and accurate screening tool using machine-learning-based approaches in them.
Acceptance and Readiness of Critical Care Nurses to Use Artificial Intelligence: A Structural Equation Modeling Approach.
International nursing review
Jun 1, 2025
AIM: The aims of this study was to evaluate the acceptance and readiness of critical care nurses to use artificial intelligence (AI).
Knowledge and Awareness of Generative Artificial Intelligence Use in Medicine Among International Stakeholders: A Cross-Sectional Study.
Journal of evidence-based medicine
Jun 1, 2025
OBJECTIVE: To assess the knowledge, attitudes, and practices (KAP) of medical stakeholders regarding the use of generative artificial intelligence (GAI) tools.
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