A Novel Interpretable Deep-Learning-Based System for Triage Prediction in the Emergency Department: A Prospective Study

Overcrowding in the Emergency Department (ED) has become one of the most severe healthcare issues worldwide. A number of researchers have reported that many countries, including Taiwan, have a significant and noticeable increase in ED visits. This phenomenon, overcrowding in the ED, has caused several adverse effects not only on patients but also on the healthcare delivery process, quality of care, and care efficiency. However, the currently adopted five-level triage system, Taiwan Triage and Acuity Scale (TTAS), is insufficient to distinguish patients’ conditions into different priorities. Therefore, a system that could help to triage a patients’ conditions accurately is demanded. While most of the existing studies have only utilized retrospective data and used traditional machine learning as the approaches, which might not satisfy for clinical decisions and real-world situations, this paper proposed an interpretable novel triage prediction system for predicting hospitalization based on prospectively collected data in the emergency department, including vital signs and chief complaints. The performance of our proposed model is evaluated on our own collected data in National Taiwan University Hospital (NTUH), with 73 samples needing hospital admission and 73 samples discharged. Ten times of 10-fold cross-validation were done to evaluate the performance of our proposed model, a mean area under the receiver operating characteristic curve (AUROC) and mean accuracy are achieved at 0.836 and 0.805, respectively.