Predicting Emergency Severity Index (ESI) level, hospital admission, and admitting ward in an emergency department using data-driven machine learning

Abstract Introduction Emergency departments (EDs) are critical for ensuring timely patient care, especially in triage, where accurate prioritisation is essential for patient safety and resource utilisation. Building on previous research, this study leverages a comprehensive dataset of 653,546 ED vis...

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Bibliographic Details
Main Authors:	Steve Agius, Vincent Cassar, Caroline Magri, Wasiq Khan, Dhiya Al-Jumeily Obe, Godwin Caruana, Luke Topham
Format:	Article
Language:	English
Published:	BMC 2025-07-01
Series:	BMC Medical Informatics and Decision Making
Subjects:	Emergency department Triage Big-data Machine learning Decision-making XGBoost
Online Access:	https://doi.org/10.1186/s12911-025-02941-9
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Predicting Emergency Severity Index (ESI) level, hospital admission, and admitting ward in an emergency department using data-driven machine learning

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