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

Agius, S, Cassar, V, Khan, W, Al-Jumeily OBE, D, Caruana, G and Topham, L ORCID: 0000-0002-6689-7944 (2025) Predicting Emergency Severity Index (ESI) level, hospital admission, and admitting ward in an emergency department using data-driven machine learning. BMC Medical Informatics and Decision Making, 25 (1).

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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 visits spanning six years from Mater Dei Hospital, Malta. This dataset enables detailed trend analysis, demographic variation exploration, and predictive modelling of patient prioritisation, admission likelihood, and admitting ward. Methods Two predictive models (Stage 1 and Stage 2) were developed using the Extreme Gradient Boosting (XGBoost) algorithm. In Stage 1, predictions were made at the triage level using basic demographic and presenting symptom data. Stage 2 incorporated critical blood test results (e.g., Haemoglobin, C-Reactive Protein, Troponin T, and White Blood Cell Count) alongside the demographic and symptom data from Stage 1 to refine and enhance predictions. Key steps in data preprocessing, such as handling missing values, balancing class distributions with SMOTE, and feature encoding, are discussed. Model evaluation employed comprehensive metrics, including AUC-ROC and calibration curves, to assess both performance and reliability. This enhanced description provides a clear roadmap of the model development process, reinforcing the study’s rigor and contribution to advancing machine learning applications in emergency care. Results The models demonstrated significant predictive capabilities. Key metrics showed improvement between Stage 1 and Stage 2. For example, patient prioritisation accuracy improved from 0.75 to 0.76, admission prediction accuracy rose from 0.80 to 0.82, and admitting ward prediction accuracy increased from 0.80 to 0.86. These enhancements underscore the value of incorporating clinical data to optimise predictions. Discussion The integration of early predictions into ED workflows has the potential to improve patient flow, reduce wait times, and enhance resource allocation. By leveraging XGBoost’s capabilities and integrating both demographic and clinical data, this study provides a robust framework for advancing decision-making processes in triage environments. Conclusions This research demonstrates the efficacy of machine learning models in predicting key ED outcomes, highlighting their potential to transform emergency care through data-driven insights.

Item Type:	Article
Uncontrolled Keywords:	0806 Information Systems; 1103 Clinical Sciences; Medical Informatics; 4203 Health services and systems
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:	Computer Science and Mathematics
Publisher:	BioMed Central
Date of acceptance:	18 February 2025
Date of first compliant Open Access:	30 July 2025
Date Deposited:	30 Jul 2025 14:56
Last Modified:	30 Jul 2025 15:00
DOI or ID number:	10.1186/s12911-025-02941-9
URI:	https://researchonline.ljmu.ac.uk/id/eprint/26867

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