• 1. Key Laboratory of Intelligent Computing and Signal Processing, Anhui University, Ministry of Education, Hefei 230601, P.R. China;
  • 2. The 2nd Department of Intensive Care Unit, Laboratory of Cardiopulmonary Resuscitation and Critical Care Medicine, the Second Affiliated Hospital of Anhui Medical University, Hefei 230601, P.R. China;
YANG Min, Email: 512130761@qq.com
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Objective To systematically review the early clinical prediction value of machine learning (ML) for cardiac arrest (CA).Methods PubMed, EMbase, WanFang Data and CNKI databases were electronically searched to retrieve all ML studies on predicting CA from January 2015 to February 2021. Two reviewers independently screened literature, extracted data and assessed the risk of bias of included studies. The value of each model was evaluated based on the area under receiver operating characteristic curve (AUC) and accuracy.Results A total of 38 studies were included. In terms of data sources, 13 studies were based on public database, and other studies retrospectively collected clinical data, in which 21 directly predicted CA, 3 predicted CA-related arrhythmias, and 9 predicted sudden cardiac death. A total of 51 models had been adopted, among which the most popular ML methods included artificial neural network (n=11), followed by random forest (n=9) and support vector machine (n=5). The most frequently used input feature was electrocardiogram parameters (n=20), followed by age (n=12) and heart rate variability (n=10). Six studies compared the ML models with other traditional statistical models and the results showed that the AUC value of ML was generally higher than that in traditional statistical models.Conclusions The available evidence suggests that ML can accurately predict the occurrence of CA, and the performance is significantly superior to traditional statistical model in certain cases.

Citation: WU Qiushuo, LU Zongqing, LIU Yu, XU Yaohua, ZHANG Jin, XIAO Wenyan, YANG Min. Machine learning for early warning of cardiac arrest: a systematic review. Chinese Journal of Evidence-Based Medicine, 2021, 21(8): 942-952. doi: 10.7507/1672-2531.202103082 Copy

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