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 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 blo

SUBMITTER: Agius S 

PROVIDER: S-EPMC12306103 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature