Background: Triage is a critical component of Emergency department care. Erroneous patient classification and mis-triaging are common in present triage systems worldwide. Therefore, several institutes worldwide have developed artificial intelligence-based algorithms that use machine learning approaches to sort and triage patients effectively. Objective: This study aims were to propose a machine learning model to predict the triage level for emergency medicine department patients and compare its performance to the standard nursing triage system. Methods: This retrospective pilot study collected the dataset of emergency department records from King Fahad Hospital of the University in khobar, between January 1, 2020, and December 31, 2022. A sample of 998 randomly selected patients was included in this cohort. The machine learning model was trained using 10-fold cross-validation. Two experiments were conducted, including five triage levels, and the second combing triage levels 2, 3, 4, and 5. Results: The machine learning model achieved an accuracy of 84% in experiment 1 and 64% in experiment 2. The mis-triage rates of the machine learning model were significantly lower than those of the standard nursing triage system. Conclusion: The machine learning model achieved higher accuracy and lower mis-triage rates than the standard nursing triage system. Thus, the proposed machine learning model can be a helpful tool for emergency department triage, enabling more efficient and accurate patient management.
Key words: Canadian Triage and Acuity Scale Machine Learning, Emergency Department Mis-triage, Random Forest.
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