A comparative study of supervised machine learning approaches to predict patient triage outcomes in hospital emergency departments

Background: The inconsistency in triage evaluation in emergency departments (EDs) and the limitations in practice within the standard triage tools among triage nurses have led researchers to seek more accurate and robust triage evaluation that provides better patient prioritization based on their medical conditions. This study aspires to establish the best methodological practices for applying machine learning (ML) techniques to build an automated triage model for more accurate evaluation. Methods: A comparative study of selected supervised ML models was conducted to determine the best-performing approach to evaluate patient triage outcomes in hospital emergency departments. A retrospective dataset of 2688 patients who visited the ED between April 1, 2020 and June 9, 2020 was collected. Data included patient demographics (age and gender), Vital signs (body temperature, respiratory rate, heart rate, blood pressure and oxygen saturation), chief complaints, and chronic illness. Nine supervised ML techniques were investigated in this study. Models were trained based on patient disposition outcomes and then validated to evaluate their performance. Findings: ML models show high capabilities in predicting patient disposition outcomes in ED settings. Four models (KNN, GBDT, XGBoost, and RF) performed better than the rest. RF was selected as the optimal model as it demonstrated a slight advantage over the other models with 89.1% micro accuracy, 89.0% precision, 89.1% recall, and 89.0% F1-score, exhibiting outstanding performance in differentiation between patients with critical outcomes (e.g., Mortality and ICU admission) from those patients with less critical outcomes (e.g., discharged and hospitalized) in ED settings. Conclusion: Machine learning techniques demonstrate high promise in improving predictive abilities in emergency medicine and providing robust decision-making tools that can enhance the patient triage process, assist triage personnel in their decision and thus reduce the effects of ED overcrowding and enhance patient outcomes.