Automated surveillance for cardiac arrests would be useful in overcrowded emergency departments. The purpose of this study is to develop and test artificial neural network (ANN) classifiers for early detection of patients at risk of cardiac arrest in emergency departments.

This is a single-center electronic health record (EHR)-based study. The primary outcome was the development of cardiac arrest within 24 h after prediction. Three ANN models were trained: multilayer perceptron (MLP), long-short-term memory (LSTM), and hybrid. These were compared to other classifiers including the modified early warning score (MEWS), logistic regression, and random forest. We used AUROC, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) for the comparison.

During the study period, there were a total of 374,605 ED visits and 2,910,321 patient status updates. The ANN models (MLP, LSTM, and hybrid) achieved higher AUROC (AUROC: 0.929, 0.933, and 0.936; 95% confidential interval: 0.926–0.932, 0.930–0.936, and 0.933–0.939, respectively) compared to the non-ANN models, and the hybrid model exhibited the best performance. The ANN classifiers displayed higher performance in most of the test characteristics when the threshold levels of the classifiers were fixed to display the same positive result as those at the three MEWS thresholds (score ≥ 3, ≥4, and ≥5), and when compared with each other.

The ANN improves upon MEWS and conventional machine learning algorithms for the prediction of cardiac arrests in emergency departments. The hybrid ANN model utilizing both baseline and sequence information achieved the best performance.