Development and validation of an interpretable machine learning model for predicting in-hospital mortality for ischemic stroke patients in ICU.

Journal: International journal of medical informatics
PMID: 40073651

Abstract

BACKGROUND: Timely and accurate outcome prediction is essential for clinical decision-making for ischemic stroke patients in the intensive care unit (ICU). However, the interpretation and translation of predictive models into clinical applications are equally crucial. This study aims to develop an interpretable machine learning (IML) model that effectively predicts in-hospital mortality for ischemic stroke patients.

Authors

  • Xiao Luo
    Department of Spine Surgery, The Third Hospital of Mianyang, Sichuan Mental Health Center, Mianyang, China.
  • Binghan Li
    National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, Guangdong, 518073, China.
  • Ronghui Zhu
    Department of Military Health Statistics, Naval Medical University, Shanghai 200433, China.
  • Yaoyong Tai
    Department of Military Health Statistics, Naval Medical University, Shanghai 200433, China.
  • Zongyu Wang
  • Qian He
    National Translational Science Center for Molecular Medicine and Department of Cell Biology, Fourth Military Medical University, Xi'an, 710032, China.
  • Yanfang Zhao
    School of Pharmaceutical Sciences, Qilu University of Technology (Shandong Academy of Sciences), Jinan, 250014, China.
  • Xiaoying Bi
  • Cheng Wu
    Department of Automation, Tsinghua University, Beijing 100084, China. Electronic address: wuc@tsinghua.edu.cn.

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