%0 Journal Article
%@ 2291-9694
%I JMIR Publications
%V 8
%N 8
%P e15932
%T Prediction of Cardiac Arrest in the Emergency Department Based on Machine Learning and Sequential Characteristics: Model Development and Retrospective Clinical Validation Study
%A Hong,Sungjun
%A Lee,Sungjoo
%A Lee,Jeonghoon
%A Cha,Won Chul
%A Kim,Kyunga
%+ Department of Emergency Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea, 82 10 5386 6597, wc.cha@samsung.com
%K machine learning
%K cardiac arrest prediction
%K emergency department
%K sequential characteristics
%K clinical validity
%D 2020
%7 4.8.2020
%9 Original Paper
%J JMIR Med Inform
%G English
%X Background: The development and application of clinical prediction models using machine learning in clinical decision support systems is attracting increasing attention. Objective: The aims of this study were to develop a prediction model for cardiac arrest in the emergency department (ED) using machine learning and sequential characteristics and to validate its clinical usefulness. Methods: This retrospective study was conducted with ED patients at a tertiary academic hospital who suffered cardiac arrest. To resolve the class imbalance problem, sampling was performed using propensity score matching. The data set was chronologically allocated to a development cohort (years 2013 to 2016) and a validation cohort (year 2017). We trained three machine learning algorithms with repeated 10-fold cross-validation. Results: The main performance parameters were the area under the receiver operating characteristic curve (AUROC) and the area under the precision-recall curve (AUPRC). The random forest algorithm (AUROC 0.97; AUPRC 0.86) outperformed the recurrent neural network (AUROC 0.95; AUPRC 0.82) and the logistic regression algorithm (AUROC 0.92; AUPRC=0.72). The performance of the model was maintained over time, with the AUROC remaining at least 80% across the monitored time points during the 24 hours before event occurrence. Conclusions: We developed a prediction model of cardiac arrest in the ED using machine learning and sequential characteristics. The model was validated for clinical usefulness by chronological visualization focused on clinical usability. 
%M 32749227
%R 10.2196/15932
%U http://medinform.jmir.org/2020/8/e15932/
%U https://doi.org/10.2196/15932
%U http://www.ncbi.nlm.nih.gov/pubmed/32749227