Developing and validating an artificial intelligence-based electronic triage model for predicting clinical outcomes among cardiac-suspected patients in the emergency department

Emergency department overcrowding, especially in cardiac units, delays care and raises mortality. Conventional triage is error-prone. We developed an AI-based model integrating routine data and automated ECGs to improve early risk classification.

This retrospective cross-sectional study involved 600 medical records of patients presenting with suspected cardiac symptoms. Model development was conducted in three phases: Designing a triage model using routine triage data, designing a triage model based on ECG images, and combining the ECG-based model and triage data. Model performance was evaluated regarding standard clinical outcomes within the first 24 h and compared against the Emergency Severity Index. The best-performing model based on triage data alone (i.e. multilayer perceptron neural network) yielded an accuracy of 89.42%, F-score of 84.51, and area under the curve between 0.815 and 0.858. The best-performing model based on ECG interpretation alone (i.e. convolutional neural network) yielded an accuracy of 93.83%, F-score of 91.08, and area under the curve ranging from 0.852 to 0.914. The fusion model demonstrated superior performance, with an accuracy of 97.22%, F-score of 94.60, and area under the curve between 0.881 and 0.938—significantly outperforming the conventional Emergency Severity Index. In the fusion model, the key predictive variables included ECG interpretation, heart rate, and mode of entry to emergency department.

Given its advantages over models using only routine data, ECG, or conventional triage, the fused AI-based triage model may effectively prioritize and predict cardiac emergency outcomes, providing a foundation for developing reliable, intelligent support systems in acute care.