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http://dx.doi.org/10.1111/all.16417 | DOI Listing |
J Med Internet Res
September 2025
School of Advertising, Marketing and Public Relations, Faculty of Business and Law, Queensland University of Technology, Brisbane, Australia.
Background: Labor shortages in health care pose significant challenges to sustaining high-quality care for people with intellectual disabilities. Social robots show promise in supporting both people with intellectual disabilities and their health care professionals; yet, few are fully developed and embedded in productive care environments. Implementation of such technologies is inherently complex, requiring careful examination of facilitators and barriers influencing sustained use.
View Article and Find Full Text PDFJMIR Med Inform
September 2025
College of Medical Informatics, Chongqing Medical University, 1 Yixueyuan Road, Yuzhong District, Chongqing, 400016, China, 86 13500303273.
Background: Cirrhosis is a leading cause of noncancer deaths in gastrointestinal diseases, resulting in high hospitalization and readmission rates. Early identification of high-risk patients is vital for proactive interventions and improving health care outcomes. However, the quality and integrity of real-world electronic health records (EHRs) limit their utility in developing risk assessment tools.
View Article and Find Full Text PDFSci Transl Med
September 2025
Department of Mechanical Engineering, University of California, Santa Barbara, Santa Barbara, CA 93106, USA.
Endotracheal intubation is a critical medical procedure for protecting a patient's airway. Current intubation technology requires extensive anatomical knowledge, training, technical skill, and a clear view of the glottic opening. However, all of these may be limited during emergency care for trauma and cardiac arrest outside the hospital, where first-pass failure is nearly 35%.
View Article and Find Full Text PDFPLoS One
September 2025
Department of Medicine, The Red Rogers Centre for Heart Research, Peter Munk Cardiac Centre, University Health Network, University of Toronto, Toronto, Ontario, Canada.
Background: In order to seriously impact the global burden of heart failure (HF) and coronary artery disease (CAD), identifying at-risk individuals as early as possible is vital. Risk calculator tools in wide clinical use today are informed by traditional statistical methods that have historically yielded only modest prediction accuracy.
Methods: This study uses machine learning algorithms to generate predictions models for the development and progression of severe HF and CAD.
Drugs Aging
September 2025
Dalla Lana School of Public Health, University of Toronto, V1 06, 2075 Bayview Avenue, Toronto, ON, M4N 3M5, Canada.
Background And Objectives: Older adults living with dementia are a heterogeneous group, which can make studying optimal medication management challenging. Unsupervised machine learning is a group of computing methods that rely on unlabeled data-that is, where the algorithm itself is discovering patterns without the need for researchers to label the data with a known outcome. These methods may help us to better understand complex prescribing patterns in this population.
View Article and Find Full Text PDF