Machine learning derivation of two cardiac arrest subphenotypes with distinct responses to treatment.

Journal: Journal of translational medicine
Published Date: Jan 6, 2025

Abstract

INTRODUCTION: Cardiac arrest (CA), characterized by its heterogeneity, poses challenges in patient management. This study aimed to identify clinical subphenotypes in CA patients to aid in patient classification, prognosis assessment, and treatment decision-making.

Authors

  • Weidong Zhang
    Department of Automation, Shanghai Jiaotong University, Shanghai 200240, China. Electronic address: wdzhang@sjtu.edu.cn.
  • Chenxi Wu
    Center for Advanced Medical Computing and Analysis, Massachusetts General Hospital, 55 Fruit St, White 427, Boston, MA, 02114, USA.
  • Peifeng Ni
    Zhejiang University School of Medicine, Zhejiang, 310006, Hangzhou, China.
  • Sheng Zhang
    Department of Critical Care Medicine, Taizhou Hospital of Zhejiang Province, Wenzhou Medical University, Taizhou, China.
  • Hongwei Zhang
    Jiangsu Provincial Key Laboratory for TCM Evaluation and Translational Development, China Pharmaceutical University, Nanjing, Jiangsu 211198, China.
  • Ying Zhu
    China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China.
  • Wei Hu
    State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China.
  • Mengyuan Diao
    Fourth Clinical Medical College of Zhejiang Chinese Medical University, Zhejiang, 310006, Hangzhou, China. diaomengyuan@hospital.westlake.edu.cn.

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