Interpretable machine learning model for early morbidity risk prediction in patients with sepsis-induced coagulopathy: a multi-center study.

Journal: Frontiers in immunology
PMID: 40098952

Abstract

BACKGROUND: Sepsis-induced coagulopathy (SIC) is a complex condition characterized by systemic inflammation and coagulopathy. This study aimed to develop and validate a machine learning (ML) model to predict SIC risk in patients with sepsis.

Authors

  • Ruimin Tan
    School of Clinical Medical, North China University of Science and Technology, Tangshan, Hebei, China.
  • Chen Ge
    Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, 100190, Beijing, China. gechen@iphy.ac.cn.
  • Jingmei Wang
    Critical Care Department, Handan Central Hospital, Handan, Hebei, China.
  • Zinan Yang
    Critical Care Department, Hebei General Hospital, Shijiazhuang, Hebei, China.
  • He Guo
    School of Software Technology, Dalian University of Technology, Dalian, 116620, China.
  • Yating Yan
    School of Clinical Medical, North China University of Science and Technology, Tangshan, Hebei, China.
  • Quansheng Du

Keywords

External Resources

Popular Topics
Recent Journals