An interpretable machine learning scoring tool for estimating time to recurrence readmissions in stroke patients.

Journal: International journal of medical informatics
PMID: 39561668

Abstract

BACKGROUND: Stroke recurrence readmission poses an additional burden on both patients and healthcare systems. Risk stratification aims to accurately divide patients into groups to provide targeted interventions at reducing readmission. To accurately predict short and intermediate-term risks of readmission and provide information for further temporal risk stratification, we developed and validated an interpretable machine learning risk scoring tool.

Authors

  • Xiao Luo
    Department of Spine Surgery, The Third Hospital of Mianyang, Sichuan Mental Health Center, Mianyang, China.
  • Xin Cui
    Beijing Key Laboratory of Environmental & Viral Oncology, College of Life Science & Bioengineering, Beijing University of Technology, Beijing 100124, China. cuixin1201@emails.bjut.edu.cn.
  • Rui Wang
    Department of Clinical Laboratory Medicine Center, Inner Mongolia Autonomous Region People's Hospital, Hohhot, Inner Mongolia, China.
  • Yi Cheng
    College of Engineering, Lishui University, Lishui, 323000, 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.
  • Cheng Wu
    Department of Automation, Tsinghua University, Beijing 100084, China. Electronic address: wuc@tsinghua.edu.cn.
  • Jia He
    Shandong College of Electronic Technology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Key Laboratory of Rheumatic Disease and Translational Medicine, Shandong Lung Cancer Institute, China.

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