Revista da Associacao Medica Brasileira (1992) · 2026
Artificial intelligence-driven triage in emergency departments: a comparative study of machine learning approaches.
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Akan Ayşe Şule, Kucuk Sefa, Yakar Şule, Tutam Mahmut, Esen Hatice, Toker İbrahim
Аннотация
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.
RESULTS: Among the evaluated models, logistic regression, random forest, support vector machine, and majority voting achieved the highest predictive performance, with overall accuracies exceeding 89%. The confusion matrix analysis revealed that low-urgency cases were consistently classified with the highest accuracy across all models, while misclassifications occurred primarily between high-urgency and medium-urgency cases.
CONCLUSION: The results show that machine learning models appear well-suited to support the emergency departments triage decision-making process. Integrating such models into emergency departments workflows can improve patient prioritization, streamline clinical operations, and ultimately contribute to better quality of care.
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.
RESULTS: Among the evaluated models, logistic regression, random forest, support vector machine, and majority voting achieved the highest predictive performance, with overall accuracies exceeding 89%. The confusion matrix analysis revealed that low-urgency cases were consistently classified with the highest accuracy across all models, while misclassifications occurred primarily between high-urgency and medium-urgency cases.
CONCLUSION: The results show that machine learning models appear well-suited to support the emergency departments triage decision-making process. Integrating such models into emergency departments workflows can improve patient prioritization, streamline clinical operations, and ultimately contribute to better quality of care.