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

Journal: BMJ open
Published Date: Jul 5, 2025

Abstract

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.

Authors

  • Li Jing
    Massachusetts Institute of Technology, Cambridge, MA 02139, U.S.A. ljing@mit.edu.
  • Peng Hua
    Institute of Forming Technology & Equipment, Shanghai Jiao Tong University, Shanghai, China.
  • Zeng Shumei
    Department of Nursing Sichuan Provincial Maternity and Child Health Care Hospital, Chengdu, Sichuan, China.
  • Qing Peng
    Department of Orthopedics, Clinical Medical College of Yangzhou University, Yangzhou, China.
  • Weizi Wu
    Yale University School of Nursing, West Haven, Connecticut, USA.
  • Luofang Lv
    The People's Hospital of Wuhai Inner Mongolia, Wuhai, Inner Mongolia, China.
  • Liqing Yue
    Teaching and Research Section of Clinical Nursing, Xiangya Hospital of Central South University, Changsha, Hunan, China.
  • Hu Jian Zhong
    Mobile Health Ministry of Education - China Mobile Joint Laboratory, Changsha, Hunan, China.
  • Huang Weihong
    Mobile Health Ministry of Education - China Mobile Joint Laboratory, Changsha, Hunan, China w.huang@csu.edu.cn.

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