Abstract
BACKGROUND: Timely identification of deteriorating patients is crucial to prevent the progression to cardiac arrest. However, current methods predicting emergency department cardiac arrest are primarily static, rule-based with limited precision and cannot accommodate time-series data. Deep learning has the potential to continuously update data and provide more precise predictions throughout the emergency department stay. METHODS: We developed and internally validated a deep learning-based scoring system, the Deep EDICAS for early prediction of cardiac arrest and a subset of arrest, cardiopulmonary resuscitation (CPR), in the emergency department. Our proposed model effectively integrates tabular and time series data to enhance predictive accuracy. To address data imbalance and bolster early prediction capabilities, we implemented data augmentation techniques. RESULTS: Our system achieved an AUPRC of 0.5178 and an AUROC of 0.9388 on on data from the National Taiwan University Hospital. For early prediction, our system achieved an AUPRC of 0.2798 and an AUROC of 0.9046, demonstrating superiority over other early warning scores. Moerover, Deep EDICAS offers interpretability through feature importance analysis. CONCLUSION: Our study demonstrates the effectiveness of deep learning in predicting cardiac arrest in emergency department. Despite the higher clinical value associated with detecting patients requiring CPR, there is a scarcity of literature utilizing deep learning in CPR detection tasks. Therefore, this study embarks on an initial exploration into the task of CPR detection.