Machine Learning for Predicting Risk and Prognosis of Acute Kidney Disease in Critically Ill Elderly Patients During Hospitalization: Internet-Based and Interpretable Model Study.

Journal: Journal of medical Internet research
PMID:

Abstract

BACKGROUND: Acute kidney disease (AKD) affects more than half of critically ill elderly patients with acute kidney injury (AKI), which leads to worse short-term outcomes.

Authors

  • Mingxia Li
    Department of Critical Care Medicine, Xiangya Hospital Central South University, Changsha, China.
  • Shuzhe Han
    Department of Obstetrics and Gynecology, 967th Hospital of the Joint Logistics Support Force of the Chinese People's Liberation Army, Dalian, China.
  • Fang Liang
    College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China.
  • Chenghuan Hu
    Department of Critical Care Medicine, Xiangya Hospital Central South University, Changsha, China.
  • Buyao Zhang
    Department of Critical Care Medicine, Xiangya Hospital Central South University, Changsha, China.
  • Qinlan Hou
    Department of Critical Care Medicine, Xiangya Hospital Central South University, Changsha, China.
  • Shuangping Zhao
    Department of Critical Care Medicine, Xiangya Hospital Central South University, Changsha, China.