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Development of an interpretable machine learning model for frailty risk prediction in older adult care institutions: a mixed-methods, cross-sectional study in China

tech

Objective To develop and validate an interpretable machine learning (ML)-based frailty risk prediction model that combines real-time health data with validated scale assessments for enhanced decision-making and targeted health management in integrated medical and older adult care institutions (IMOACIs) in central China. Design Mixed-methods, cross-sectional study. Setting 13 IMOACIs across seven cities in Hunan province, central China, from 8 to 16 July 2022. Participants Five healthcare experts and two data scientists participated in the requirements analysis stage. A total of 586 older adults were included in the assessment data collection stage, and 15 participants (10 healthcare professionals and five data scientists) were involved in the model evaluation stage.

BMJ     Jul 05, 2025