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2. Predictive Models for Emergency Department Triage using Machine Learning: A Review

Author

Fei Gao, Baptiste Boukebous, Pozzar Mario, Alaoui Enora, Sano Batourou, Sahar Bayat-Makoei, École des Hautes Études en Santé Publique [EHESP] (EHESP), Département Méthodes quantitatives en santé publique (METIS), Centre de Recherches sur l'Action Politique en Europe (ARENES), Université de Rennes (UR)-Institut d'Études Politiques [IEP] - Rennes-École des Hautes Études en Santé Publique [EHESP] (EHESP)-Centre National de la Recherche Scientifique (CNRS), Recherche sur les services et le management en santé (RSMS), Université de Rennes (UR)-École des Hautes Études en Santé Publique [EHESP] (EHESP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Equipe 4 : ECaMO - Épidémiologie clinique appliquée aux maladies rhumatismales et musculo-squelettiques (CRESS - U1153), Centre de Recherche Épidémiologie et Statistique Sorbonne Paris Cité (CRESS (U1153 / UMR_A_1125 / UMR_S_1153)), Conservatoire National des Arts et Métiers [CNAM] (CNAM), HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-Université Sorbonne Paris Cité (USPC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris Cité (UPCité)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Conservatoire National des Arts et Métiers [CNAM] (CNAM), and HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-Université Sorbonne Paris Cité (USPC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris Cité (UPCité)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)

Subjects
Machine Learning, Artificially Intelligence, Patient Prioritization, Modeling, Decision Support Systems, Emergency Department/Emergency Room, Triage, Classification, Predictive, Model, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
Abstract

International audience; Background: Recently, many research groups have tried to develop emergency department triage decision support systems based on big volumes of historical clinical data to differentiate and prioritize patients. Machine learning models might improve the predictive capacity of emergency department triage systems. The aim of this review was to assess the performance of recently described machine learning models for patient triage in emergency departments, and to identify future challenges.Methods: Four databases (ScienceDirect, PubMed, Google Scholar and Springer) were searched using key words identified in the research questions. To focus on the latest studies on the subject, the most cited papers between 2018 and October 2021 were selected. Only works with hospital admission and critical illness as outcomes were included in the analysis.Results: Eleven articles concerned the two outcomes (hospital admission and critical illness) and developed 55 predictive models. Random Forest and Logistic Regression were the most commonly used prediction algorithms, and the receiver operating characteristic-area under the curve (ROC-AUC) the most frequently used metric to assess the algorithm prediction performance. Random Forest and Logistic Regression were the most discriminant models according to the selected studies.Conclusions: Machine learning-based triage systems could improve decision-making in emergency departments, thus leading to better patients’ outcomes. However, there is still scope for improvement concerning the prediction performance and explicability of ML models.

Published
2022
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