Abstract
BACKGROUND AND OBJECTIVE: The Emergency Department (ED) is a critical, high-stakes environment where timely and accurate assessments of patient outcomes are essential for ensuring optimal care and effective resource management. This narrative review aimed to synthesise current evidence on machine learning (ML)-based predictive models used in the ED to forecast patient outcomes such as mortality, intensive care unit (ICU) admission, and discharge probability, whilst identifying key limitations and future research directions. METHODS: This narrative review synthesises recent advancements in ML-based predictive models for ED outcomes published between January 2015 and December 2024. It explores the integration of real-time and historical clinical data, focusing on key ML techniques such as regression models, decision trees, neural networks, and ensemble methods. The review also evaluates data sources, model evaluation metrics, and addresses challenges including data quality, interpretability, and ethical considerations. A comprehensive search of four major databases yielded 156 initial results, with 45 studies ultimately included after systematic screening. KEY CONTENT AND FINDINGS: ML models demonstrate significant promise in processing complex, non-linear data for ED outcome prediction with area under the receiver operating characteristic curve (AUC-ROC) values typically ranging from 0.75-0.95 across different outcomes. Techniques like ensemble methods and neural networks offer strong performance, while personalized prediction models and explainable artificial intelligence (XAI) enhance precision and interpretability. However, current approaches face substantial limitations including data heterogeneity, poor model generalisability across institutions, and lack of real-world implementation studies. Emerging integration of telemedicine further broadens the applicability of predictive modeling in the ED. CONCLUSIONS: ML is reshaping predictive modeling in the ED, offering timely, data-driven support for clinical decision-making. Despite challenges, advancements in personalized and explainable models hold the potential to increase trust and usability in clinical workflows. Critical gaps remain in addressing data quality issues, standardising evaluation metrics, and conducting multi-centre validation studies.