Identification of distinct clinical phenotypes of cardiogenic shock using machine learning consensus clustering approach.

Journal: BMC cardiovascular disorders
PMID: 37644414

Abstract

BACKGROUND: Cardiogenic shock (CS) is a complex state with many underlying causes and associated outcomes. It is still difficult to differentiate between various CS phenotypes. We investigated if the CS phenotypes with distinctive clinical profiles and prognoses might be found using the machine learning (ML) consensus clustering approach.

Authors

  • Li Wang
    College of Marine Electrical Engineering, Dalian Maritime University, Dalian, China.
  • Yufeng Zhang
  • Renqi Yao
    Translational Medicine Research Center, Fourth Medical Center and Medical Innovation Research Division of the Chinese PLA General Hospital, Beijing, China.
  • Kai Chen
    Department of Critical Care Medicine, Fujian Provincial Key Laboratory of Critical Care Medicine, Shengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital, Fujian Provincial Center for Critical Care Medicine, Fuzhou, Fujian, China.
  • Qiumeng Xu
    Department of Orthopaedics, Changzheng Hospital, Naval Medical University, Shanghai, China.
  • Renhong Huang
    Department of General Surgery, Comprehensive Breast Health Center, Ruijin Hospital, Jiaotong University School of Medicine, Shanghai, China.
  • Zhiguo Mao
    Department of Nephrology, Changzheng Hospital, Naval Medical University, Shanghai, China. maozhiguo93@126.com.
  • Yue Yu
    Department of Mathematics, Lehigh University, Bethlehem, PA, USA.

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