E-ISSN 1658-8487
 
2023, Vol: 4, Issue: 2
4 / 2Current Issue Online First Archive Aims and Scope Abstracting & Indexing Most Accessed Articles Most Downloaded Articles Most Cited Articles

Original Article 


The utilizing of machine learning algorithms to improve triage in emergency departments: a retrospective observational study

Maitham Jawad Aljubran, Hussain Jawad Aljubran, Mohammad Aljubran, Mohammed Alkhalifah, Moayd Alkhalifah, Tawfik Alabdullah.

Abstract
Background: Machine learning in the healthcare sector represents a group of technologies in all aspects of medicine, and it appears promising, especially in emergency medicine. Hence, this study aims to utilize emergency department (ED) records to train machine learning algorithms and assess medical performance and outcomes.
Methods: This is a retrospective observational cohort study utilizing emergency patient records acquired from the Emergency Department of King Faisal Specialist Hospital & Research Centre in Riyadh City. Also, different machine learning models were evaluated, including regression, instance-based, regularization, tree-based, Bayesian, dimensionality reduction, and ensemble algorithms.
Results: A total of 149,513 emergency patient records were acquired. Due to many outliers and mislabeled data, clinical knowledge and a confident learning algorithm were used to preprocess the dataset. This resulted in only 84,970 patient records being kept. We observed that ensemble algorithms outperformed the others in all evaluation metrics, achieving an F-1 score and quadratic weighted kappa of 93.1% and 0.8623, respectively, in the case of CatBoost. In addition, the model never classified an emergent patient as nonurgent, nor did it classify a nonurgent ED patient as emergent. Optimizing the healthcare center workforce while ensuring that all critical patients are treated immediately is vital.
Conclusion: Machine learning-based triage models are feasible, highly accurate, and provide an in-depth assessment of the patient’s risk profile, which may not be found in routinely used emergency triage systems. A prospective study to evaluate the potential efficacy of machine learning-based triage models in predicting emergency visit outcomes needs to be conducted.

Key words: Machine learning, artificial intelligence, emergency medicine, triage.


 
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How to Cite this Article
Pubmed Style

Aljubran MJ, Aljubran HJ, Aljubran M, Alkhalifah M, Alkhalifah M, Alabdullah T. The utilizing of machine learning algorithms to improve triage in emergency departments: a retrospective observational study. SJEMed. 2023; 4(2): 354-361. doi:10.24911/SJEMed/72-1673885230

Web Style

Aljubran MJ, Aljubran HJ, Aljubran M, Alkhalifah M, Alkhalifah M, Alabdullah T. The utilizing of machine learning algorithms to improve triage in emergency departments: a retrospective observational study. https://sjemed.com/?mno=139947 [Access: October 02, 2023]. doi:10.24911/SJEMed/72-1673885230

AMA (American Medical Association) Style

Aljubran MJ, Aljubran HJ, Aljubran M, Alkhalifah M, Alkhalifah M, Alabdullah T. The utilizing of machine learning algorithms to improve triage in emergency departments: a retrospective observational study. SJEMed. 2023; 4(2): 354-361. doi:10.24911/SJEMed/72-1673885230


Vancouver/ICMJE Style

Aljubran MJ, Aljubran HJ, Aljubran M, Alkhalifah M, Alkhalifah M, Alabdullah T. The utilizing of machine learning algorithms to improve triage in emergency departments: a retrospective observational study. SJEMed. (2023), [cited October 02, 2023]; 4(2): 354-361. doi:10.24911/SJEMed/72-1673885230


Harvard Style

Aljubran, M. J., Aljubran, . H. J., Aljubran, . M., Alkhalifah, . M., Alkhalifah, . M. & Alabdullah, . T. (2023) The utilizing of machine learning algorithms to improve triage in emergency departments: a retrospective observational study. SJEMed, 4 (2), 354-361. doi:10.24911/SJEMed/72-1673885230


Turabian Style

Aljubran, Maitham Jawad, Hussain Jawad Aljubran, Mohammad Aljubran, Mohammed Alkhalifah, Moayd Alkhalifah, and Tawfik Alabdullah. 2023. The utilizing of machine learning algorithms to improve triage in emergency departments: a retrospective observational study. Saudi Journal of Emergency Medicine, 4 (2), 354-361. doi:10.24911/SJEMed/72-1673885230


Chicago Style

Aljubran, Maitham Jawad, Hussain Jawad Aljubran, Mohammad Aljubran, Mohammed Alkhalifah, Moayd Alkhalifah, and Tawfik Alabdullah. "The utilizing of machine learning algorithms to improve triage in emergency departments: a retrospective observational study." Saudi Journal of Emergency Medicine 4 (2023), 354-361. doi:10.24911/SJEMed/72-1673885230


MLA (The Modern Language Association) Style

Aljubran, Maitham Jawad, Hussain Jawad Aljubran, Mohammad Aljubran, Mohammed Alkhalifah, Moayd Alkhalifah, and Tawfik Alabdullah. "The utilizing of machine learning algorithms to improve triage in emergency departments: a retrospective observational study." Saudi Journal of Emergency Medicine 4.2 (2023), 354-361. Print. doi:10.24911/SJEMed/72-1673885230


APA (American Psychological Association) Style

Aljubran, M. J., Aljubran, . H. J., Aljubran, . M., Alkhalifah, . M., Alkhalifah, . M. & Alabdullah, . T. (2023) The utilizing of machine learning algorithms to improve triage in emergency departments: a retrospective observational study. Saudi Journal of Emergency Medicine, 4 (2), 354-361. doi:10.24911/SJEMed/72-1673885230




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