Prediction of Cardiac Arrest in the Emergency Department Based on Machine Learning and Sequential Characteristics: Model Development and Retrospective Clinical Validation Study

• Published in: JMIR medical informatics Vol. 8; no. 8; p. e15932
• Main Authors: Hong, Sungjun, Lee, Sungjoo, Lee, Jeonghoon, Cha, Won Chul, Kim, Kyunga
• Format: Journal Article
• Language: English
• Published: Toronto JMIR Publications 01.08.2020
• Subjects: Algorithms; Artificial intelligence; Blood pressure; Body temperature; Cardiac arrest; Data processing; Heart rate; Machine learning; Multimedia; Neural networks; Original Paper; Patients; Signs; Validation studies; Vital signs
• ISSN: 2291-9694; 2291-9694
• DOI: 10.2196/15932

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.