Machine Learning-Based Model to Classify Emergency Severity Index Levels 1-3 in Febrile Patients With Tachycardia:...

Febrile patients with tachycardia present diverse profiles that complicate triage. Although the Emergency Severity Index (ESI) is widely used in Thailand, inter-rater variability limits consistency. Machine learning (ML) may enhance reliability using routinely collected triage data. The objectives were to develop and evaluate ML models predicting ESI levels 1-3 in febrile tachycardic adults and identify the best model for clinical use.

This diagnostic prediction study analyzed adults with fever (≥ 37.6 °C) and pulse rate > 100 beats per minute in the triage area of Lampang Hospital, Thailand, during June - August 2024. Patients with complete data were included, whereas referrals and expert-disagreement cases were excluded. Expert-assigned ESI levels were the outcome. Thirteen routinely collected triage variables were evaluated as candidate predictors. The dataset (n = 500) was randomly split 80:20 into development and testing sets. Random forest, extreme gradient boosting (XGBoost), and gradient boosting machine models were developed using five-fold cross-validation with class-weighting for imbalance correction. Performance was assessed using area under the receiver operating characteristic curve (AuROC), calibration, and confusion matrices, with attention to clinically relevant misclassification.

XGBoost demonstrated the best discrimination with AuROC values of 1.00 (confidence interval (CI): 0.99 - 1.00), 0.94 (CI: 0.89 - 0.98), and 0.97 (CI: 0.93 - 1.00) for ESI levels 1-3 in the test set. Calibration showed the lowest Brier scores, and misclassification was minimal, supporting strong predictive consistency across categories.

XGBoost was selected for integration into the Smart ER system as the Thailand Triage Prediction System (TTPS), providing real-time prediction to enhance triage accuracy, support decision-making, and improve workflow.