Development of an explainable machine learning model for predicting device-related pressure injuries in clinical settings.

Journal: BMC medical informatics and decision making
Published Date: Jul 9, 2025

Abstract

BACKGROUND: Device-related pressure injury (DRPI) is a prevalent and severe problem for patients using medical devices. Timely identification of patients at high risk of DRPI is crucial for healthcare providers to make informed decisions and prevent DRPI quickly. Given the rapid advancements in computer technology, we aimed to develop an interpretable artificial intelligence (AI) model for predicting DRPI, utilizing SHAP (SHapley Additive exPlanations) to enhance the model's transparency and provide insights into feature importance.

Authors

  • Yijie Qian
    Nursing Department, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China.
  • Hongying Pan
    Nursing Department, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China. 3191016@zju.edu.cn.
  • Jun Chen
    Department of Bioengineering, University of California, Los Angeles, Los Angeles, CA 90095, USA.
  • Hongyang Hu
    Department of Microelectronics, Nankai University, Tianjin, 300350, PR China; Key Laboratory of Microelectronic Devices & Integrated Technology, Institute of Microelectronics, Chinese Academy of Sciences, Beijing, 10010, PR China.
  • Mei Fang
    College of Biosystems Engineering and Food Science, State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou 310058, China.
  • Chen Huang
    Department of Pharmacy, The First Affiliated Hospital, Fujian Medical University, Fuzhou, China.
  • Yihong Xu
    Nursing Department, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China.
  • Yang Gao
    State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100050, China.

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