Explainable Machine Learning for Predicting Hospital Admission at Emergency Department Triage: Retrospective Cohort Study
Year: 2026
Type: article
Abstract: Background Emergency departments (EDs) are vital access points for urgent care, yet persistent overcrowding in health care systems worldwide compromises service quality and efficiency. Urban tertiary care centers are increasingly strained by surging patient numbers and scarce inpatient beds. Prior admission prediction studies mostly rely on limited triage features and discrimination metrics, neglecting operational factors, calibration, and explainability. Identifying likely admissions early could enable proactive resource allocation and shorter wait times. Objective This study aimed to develop and evaluate machine learning models for predicting hospital admission at the end of triage using routinely collected structured data while addressing prior limitations regarding feature selection, model calibration, and explainability. Methods We conducted a retrospective cohort study of adult ED visits between January 2015 and December 2022 at 2 academic hospitals in the McGill University Health Centre. We excluded visits with dispositions other than admission or discharge to ensure a binary outcome. Data were split into training (70%), validation (10%), and testing (20%) sets using stratified random sampling. We trained classifiers, including logistic regression (LR), decision trees, random forests, artificial neural networks, and gradient boosting algorithms (extreme gradient boosting [XGBoost], light gradient boosting machine, and categorical boosting), using demographics, vital signs, chief complaints, comorbidities, and historical hospitalization frequency available at triage. Sensitivity analyses tested exclusion criteria and temporal data shifts, while subgroup analyses examined differences by age, sex, and site. Performance was evaluated using area under the receiver operating characteristic curve (AUC-ROC), sensitivity, specificity, and calibration. We also performed a decision threshold analysis to assess operational flexibility and used Shapley additive explanations (SHAP) values to interpret feature contributions. Results The final cohort included 526,145 visits (mean age 50.9, SD 21.1 years; 272,767/526,145, 51.8% female), with 91,363 patients (17.4%) admitted. The XGBoost model achieved the highest performance, with an AUC-ROC of 0.86 (95% CI 0.858‐0.864), a sensitivity of 0.67, and a specificity of 0.86, outperforming the LR baseline (AUC 0.84). Key predictors identified by SHAP values included age, Canadian Triage and Acuity Scale scores, arrival mode, and historical hospitalization frequency. Subgroup analyses confirmed strong performance across age, sex, and sites. Sensitivity analyses evaluating alternative outcome dispositions and chronological data splits confirmed overall model stability. Threshold analysis showed that adjusting the decision threshold to the 0.70 to 0.75 range improved the positive predictive value and did not disproportionately reclassify older or higher-acuity patients. Conclusions In urban tertiary care settings, structured triage data available at triage can effectively anticipate hospital admissions. By incorporating broad feature sets and operational variables, models such as XGBoost offer a feasible, explainable tool for early decision-making. Such tools could improve coordination between emergency and inpatient services and ease overcrowding. Future work should prioritize prospective validation and real-time clinical workflow integration.
Source: JMIR Medical Informatics
DOI: 10.2196/98273
Language: English
Subfield: Emergency Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $2,300
OpenAlex ID: W7225744742