To search, Click below search items. |
| All Published Papers Search Service |
|
|
|
Title |
A Prediction Triage System for Emergency Department During Hajj Period using Machine Learning Models |
|
Author |
Huda N. Alhazmi hnhazmi@uqu.edu.s |
| Citation |
Vol. 24 No. 7 pp. 11-23 |
|
Abstract |
Triage is a practice of accurately prioritizing patients in emergency department (ED) based on their medical condition to provide them with proper treatment service. The variation in triage assessment among medical staff can cause mis-triage which affect the patients negatively. Developing ED triage system based on machine learning (ML) techniques can lead to accurate and efficient triage outcomes. This study aspires to develop a triage system using machine learning techniques to predict ED triage levels using patients information. We conducted a retrospective study using Security Forces Hospital ED data, from 2021 through 2023 during Hajj period in Saudia Arabi. Using demographics, vital signs, and chief complaints as predictors, two machine learning models were investigated, naming gradient boosted decision tree (XGB) and deep neural network (DNN). The models were trained to predict ED triage levels and their predictive performance was evaluated using area under the receiver operating characteristic curve (AUC) and confusion matrix. A total of 11,584 ED visits were collected and used in this study. XGB and DNN models exhibit high abilities in the predicting performance with AUC-ROC scores 0.85 and 0.82, respectively. Compared to the traditional approach, our proposed system demonstrated better performance and can be implemented in real-world clinical settings. Utilizing ML applications can power the triage decision-making, clinical care, and resource utilization. |
|
Keywords |
Emergency department (ED); Triage, Critical care; Prediction; Machine learning (ML); XGB model; DNN model. |
|
URL |
