Interpretable Machine Learning Model for Early Prediction of Mortality in ICU Patients with Rhabdomyolysis.

Journal: Medicine and science in sports and exercise
Published Date: Sep 1, 2021

Abstract

PURPOSE: Rhabdomyolysis (RM) is a complex set of clinical syndromes that involves the rapid dissolution of skeletal muscles. Mortality from RM is approximately 10%. This study aimed to develop an interpretable and generalizable model for early mortality prediction in RM patients.

Authors

  • Chao Liu
    Anti-Drug Technology Center of Guangdong Province, National Anti-Drug Laboratory Guangdong Regional Center, Guangzhou 510230, China.
  • Xiaoli Liu
    Neurology Department, Zhejiang Hospital, Zhejiang 310013, China.
  • Zhi Mao
    Xi'an Technological University, Xi'an, China.
  • Pan Hu
    Department of Anesthesiology, The 920 Hospital of Joint Logistic Support Force of Chinese PLA, Kunming Yunnan, CHINA.
  • Xiaoming Li
    Department of Radiology, Southwest Hospital, Third Military Medical University (Army Military Medical University), Chongqing, China.
  • Jie Hu
    Corteva Agriscience, Farming Solutions and Digital, Indianapolis, IN, United States.
  • Quan Hong
    Department of Urology, General Hospital of the Air Force, PLA, No. 30 Fucheng Road Haidian District, Beijing, 100142 China.
  • Xiaodong Geng
  • Kun Chi
  • Feihu Zhou
    Department of Critical Care Medicine, The First Medical Center, Chinese PLA General Hospital, Beijing, China.
  • Guangyan Cai
    Department of Nephrology, Chinese PLA General Hospital, Chinese PLA Institute of Nephrology, State Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing, CHINA.
  • Xiangmei Chen
    Department of Nephrology, Chinese PLA General Hospital, Chinese PLA Institute of Nephrology, State Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Disease Research, Beijing, China.
  • Xuefeng Sun
    Department of Nephrology, Chinese PLA General Hospital, Chinese PLA Institute of Nephrology, State Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Disease Research, Beijing, China.

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