PURPOSE: To evaluate machine learning-based survival model roles in predicting rehospitalization after hip fractures to improve reduce the burden on the healthcare system.
OBJECTIVE: This study aims to shed light on the transformative potential of artificial intelligence (AI) in the early detection and risk assessment of non-communicable diseases (NCDs).
OBJECTIVES: To identify the factors associated with post-stroke depression (PSD) and develop a machine learning predictive model using a large dataset, considering sociodemographic, lifestyle, and clinical factors.
IMPORTANCE: Agitation events are increasing in emergency departments (EDs), exacerbating safety risks for patients and clinicians. A wide range of clinical etiologies and behavioral patterns in the emergency setting make agitation prediction difficul...
Non-compaction cardiomyopathy (NCCM) is a rare, congenital form of cardiomyopathy characterized by excessive trabeculations in the left ventricle myocardium. NCCM is often an underdiagnosed heart condition characterized by abnormal myocardial trabecu...
Purpose To assess the prognostic value of an open-source deep learning-based chest radiographs algorithm, CXR-Lung-Risk, for stratifying respiratory disease mortality risk among an Asian health screening population using baseline and follow-up chest ...
Journal of the American Medical Informatics Association : JAMIA
May 1, 2025
OBJECTIVES: Artificial intelligence (AI) models utilizing electronic health record data for disease prediction can enhance risk stratification but may lack specificity, which is crucial for reducing the economic and psychological burdens associated w...
BACKGROUND: Recent advances in artificial intelligence (AI) have enabled development of natural language algorithms capable of generating coherent texts. We evaluated the quality, validity, and safety of this generative AI in preoperative anaesthetic...
Health information management : journal of the Health Information Management Association of Australia
May 1, 2025
BACKGROUND: Hospital-acquired complications (HACs) have an adverse impact on patient recovery by impeding their path to full recovery and increasing healthcare costs.
The Journal of thoracic and cardiovascular surgery
May 1, 2025
OBJECTIVES: With the expanding integration of artificial intelligence (AI) and machine learning (ML) into the structural heart domain, numerous ML models have emerged for the prediction of adverse outcomes after transcatheter aortic valve implantatio...
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