Development and Assessment of an Interpretable Machine Learning Triage Tool for Estimating Mortality After Emergency Admissions

This cohort study examines the performance of an interpretable machine learning triage tool in estimating mortality in individuals admitted to the hospital from the emergency department. Question How does an interpretable machine learning triage tool for estimating mortality perform in a cohort of individuals admitted to the hospital from the emergency department compared with other clinical scores? Findings In this cohort study, the parsimonious and point-based Score for Emergency Risk Prediction was more accurate in identifying patients who died within 2, 7, or 30 days of admissions than other point-based clinical scores. Meaning These results suggest that the Score for Emergency Risk Prediction tool shows promise for triaging patients admitted from the emergency department according to mortality risk. IMPORTANCE Triage in the emergency department (ED) is a complex clinical judgment based on the tacit understanding of the patient's likelihood of survival, availability of medical resources, and local practices. Although a scoring tool could be valuable in risk stratification, currently available scores have demonstrated limitations. OBJECTIVES To develop an interpretable machine learning tool based on a parsimonious list of variables available at ED triage; provide a simple, early, and accurate estimate of patients' risk of death; and evaluate the tool's predictive accuracy compared with several established clinical scores. DESIGN, SETTING, AND PARTICIPANTS This single-site, retrospective cohort study assessed all ED patients between January 1, 2009, and December 31, 2016, who were subsequently admitted to a tertiary hospital in Singapore. The Score for Emergency Risk Prediction (SERP) tool was derived using a machine learning framework. To estimate mortality outcomes after emergency admissions, SERP was compared with several triage systems, including Patient Acuity Category Scale, Modified Early Warning Score, National Early Warning Score, Cardiac Arrest Risk Triage, Rapid Acute Physiology Score, and Rapid Emergency Medicine Score. The initial analyses were completed in October 2020, and additional analyses were conducted in May 2021. MAIN OUTCOMES AND MEASURES Three SERP scores, namely SERP-2d, SERP-7d, and SERP-30d, were developed using the primary outcomes of interest of 2-, 7-, and 30-day mortality, respectively. Secondary outcomes included 3-day mortality and inpatient mortality. The SERP's predictive power was measured using the area under the curv