Interpretable multimodal machine learning model for predicting health risks of patients with heart failure.

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Bibliographic Details
Title: Interpretable multimodal machine learning model for predicting health risks of patients with heart failure.
Authors: Chae R; Nuffield Department of Primary Care Health Sciences, Somerville College, University of Oxford, Oxford, United Kingdom., Zhou J; Department of Family Medicine and Primary Care, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong Special Administrative Region of China; School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong Special Administrative Region of China; Department of Pharmacology and Pharmacy, The University of Hong Kong, Pokfulam, Hong Kong Special Administrative Region of China., Chou OHI; Division of Clinical Pharmacology, Department of Medicine, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, University of Hong Kong, Pokfulam, Hong Kong Special Administrative Region of China., Yang B; Department of Family Medicine and Primary Care, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong Special Administrative Region of China., Pu H; Department of Applied Science, School of Science and Technology, Hong Kong Metropolitan University, Hong Kong Special Administrative Region of China., Tse G; Tianjin Key Laboratory of Ionic-Molecular Function of Cardiovascular Disease, Department of Cardiology, Tianjin Institute of Cardiology, Second Hospital of Tianjin Medical University, Tianjin 300211, China; Department of Health Sciences, School of Nursing and Health Studies, Hong Kong Metropolitan University, Hong Kong Special Administrative Region of China., Cheung BMY; Division of Clinical Pharmacology, Department of Medicine, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, University of Hong Kong, Pokfulam, Hong Kong Special Administrative Region of China., Zhu T; Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, United Kingdom., Car J; School of Life Course & Population Sciences, King's College London, London, United Kingdom., Lu L; School of Life Course & Population Sciences, King's College London, London, United Kingdom. Electronic address: lei.lu@kcl.ac.uk.
Source: Methods (San Diego, Calif.) [Methods] 2026 May; Vol. 249, pp. 23-36. Date of Electronic Publication: 2026 Feb 14.
Publication Type: Journal Article
Journal Info: Publisher: Academic Press Country of Publication: United States NLM ID: 9426302 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1095-9130 (Electronic) Linking ISSN: 10462023 NLM ISO Abbreviation: Methods Subsets: MEDLINE; In Process
Database: MEDLINE Ultimate
Description
ISSN:1095-9130
DOI:10.1016/j.ymeth.2026.02.007