TY  - JOUR
AU  - Hong, Sungjun
AU  - Lee, Sungjoo
AU  - Lee, Jeonghoon
AU  - Cha, Won Chul
AU  - Kim, Kyunga
PY  - 2020
DA  - 2020/8/4
TI  - Prediction of Cardiac Arrest in the Emergency Department Based on Machine Learning and Sequential Characteristics: Model Development and Retrospective Clinical Validation Study
JO  - JMIR Med Inform
SP  - e15932
VL  - 8
IS  - 8
KW  - machine learning
KW  - cardiac arrest prediction
KW  - emergency department
KW  - sequential characteristics
KW  - clinical validity
AB  - 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. 
SN  - 2291-9694
UR  - http://medinform.jmir.org/2020/8/e15932/
UR  - https://doi.org/10.2196/15932
UR  - http://www.ncbi.nlm.nih.gov/pubmed/32749227
DO  - 10.2196/15932
ID  - info:doi/10.2196/15932
ER  -