Revista da Associacao Medica Brasileira
Artificial intelligence-driven triage in emergency departments: a comparative study of machine learning approaches
By Akan AŞ, Kucuk S, Yakar Ş and 3 others.
Abstract
Objective
In emergency departments, accurate and timely triage plays a critical role in prioritizing patient care. Traditional triage often suffers from subjectivity and inconsistency, especially under time pressure and overcrowded conditions. The aim of this study was to evaluate the predictive potential of multiple supervised machine learning models for three-class emergency department triage classification and compare their performance using routinely available triage variables.
Methods
We conducted a comparative analysis of eight supervised machine learning models using a retrospective dataset comprising 510 patient records from the emergency department triage. The dataset included demographic features, vital signs, and chief complaints. A 10-fold cross-validation strategy was employed to evaluate performance in a three-class triage outcome scenario based on urgency levels using accuracy, precision, recall, and F1-score.