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

Author(s): Fei Gao, Baptiste Boukebous, Mario Pozzar, Enora Alaoui, Batourou Sano, Sahar Bayat-Makoei

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: Twenty-one articles concerned the two outcomes (hospital admission and critical illness) and developed 75 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. Boosting, 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.

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