NDLI: Predicting triage of pediatric patients in the emergency department using machine learning approach

Content Provider	Springer Nature : BioMed Central
Author	Halwani, Manal Ahmed Merdad, Ghada Almasre, Miada Doman, Ghadeer AlSharif, Shafiqa Alshiakh, Safinaz M. Mahboob, Duaa Yousof Halwani, Marwah A. Faqerah, Nojoud Adnan Mosuily, Mahmoud Talal
Abstract	Background The efficient performance of an Emergency Department (ED) relies heavily on an effective triage system that prioritizes patients based on the severity of their medical conditions. Traditional triage systems, including those using the Canadian Triage and Acuity Scale (CTAS), may involve subjective assessments by healthcare providers, leading to potential inconsistencies and delays in patient care. Objective This study aimed to evaluate six Machine Learning (ML) models K-Nearest Neighbors (KNN), Support Vector Machine (SCM), Decision Tree (DT), Random Forest (RF), Gaussian Naïve Bayes (GNB), and Light GBM (Light Gradient Boosting Machine) for triage prediction in the King Abdulaziz University Hospital using the CTAS framework. Methodology We followed three essential phases: data collection (7125 records of ED patients), data exploration and processing, and the development of machine learning predictive models for ED triage at King Abdulaziz University Hospital. Results and conclusion The overall predictive performance of CTAS was the highest using GNB = 0.984 accuracy. The CTAS-level model performance indicated that SVM, RF, and LGBM achieved the highest performance regarding the consistency of precision and recall values across all CTAS levels.
Related Links	https://intjem.biomedcentral.com/counter/pdf/10.1186/s12245-025-00861-z.pdf
Ending Page	12
Page Count	12
Starting Page	1
File Format	HTM / HTML
ISSN	18651380
DOI	10.1186/s12245-025-00861-z
Journal	International Journal of Emergency Medicine
Issue Number	1
Volume Number	18
Language	English
Publisher	BioMed Central
Publisher Date	2025-03-10
Access Restriction	Open
Subject Keyword	Emergency Medicine Internal Medicine Cardiology Angiology Pediatrics Canadian triage and acuity scale K-Nearest neighbours Support vector machine Gaussian Naive Bayes Decision tree Random forest Light GBM
Content Type	Text
Resource Type	Article
Subject	Emergency Medicine

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