Application of Machine Learning for Patients With Cardiac Arrest: Systematic Review and Meta-Analysis.

Journal: Journal of medical Internet research
PMID: 40063076

Abstract

BACKGROUND: Currently, there is a lack of effective early assessment tools for predicting the onset and development of cardiac arrest (CA). With the increasing attention of clinical researchers on machine learning (ML), some researchers have developed ML models for predicting the occurrence and prognosis of CA, with certain models appearing to outperform traditional scoring tools. However, these models still lack systematic evidence to substantiate their efficacy.

Authors

  • Shengfeng Wei
    Department of Emergency Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
  • Xiangjian Guo
    Department of Emergency Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
  • Shilin He
    Department of Emergency Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
  • Chunhua Zhang
    Department of Biochemistry and Molecular Biology of Kunming Medical University, Kunming, Yunnan 650500, P.R. China.
  • Zhizhuan Chen
    Department of Emergency Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
  • Jianmei Chen
    Department of Emergency Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
  • Yanmei Huang
    Department of Emergency Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
  • Fan Zhang
    Department of Anesthesiology, Bishan Hospital of Chongqing Medical University, Chongqing, China.
  • Qiangqiang Liu
    Department of Neurosurgery, Clinical Neuroscience Center Comprehensive Epilepsy Unit, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.

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