Document Type : Original Article
Authors
1 Msc, Industrial Engineering, Department of Industrial and Systems Engineering Isfahan University of Technology, Isfahan. Iran
2 Associate Professor, Industrial Engineering, Department of Industrial and Systems Engineering, Isfahan University of Technology, Isfahan, Iran
3 MSc, Student, Industrial Engineering, Department of Industrial and Systems Engineering, Isfahan University of Technology, Isfahan, Iran
Abstract
Introduction: Today, hospitals face challenges such as overcrowding in the emergency ward and increased chaos and disruption in staff work, which leads to increased patient dissatisfaction. With the advancement of artificial intelligence and the expansion of data mining, predicting patient admission has become important. This study endeavors to predict patient admission in the emergency department of Imam Ali Hospital in Shahrekord.
Methods: In this study, 2180 patient records from the emergency ward of the hospital were examined. Initial patient information, including personal details, vital signs, and triage level which were recorded by nurses, were extracted. Using pairwise comparison matrix, the effective features were selected by experts. Then, using naive Bayes, decision tree, random forest, and support vector machine algorithms, the data were classified.
Results: Out of the 15 collected features, 9 features were selected by experts, and the results revealed that the random forest algorithm had the good performance in predicting patient admission in this case study, with an accuracy of 92/2%.
Conclusion: The results indicated that the accuracy of machine learning models increases with the use of expert opinions, and the random forest algorithm can predict patients' admission with high accuracy in this case study.v
Keywords
Main Subjects
- .Reinhardt UE. Does the aging of the population really drive the demand for health care? Health Affairs. 2003;22(6):27-39.
- Grossman M. The demand for health: a theoretical and empirical investigation: Columbia university press; 2017.
- Wilber ST, Gerson LW, Terrell KM, Carpenter CR, Shah MN, Heard K, et al. Geriatric emergency medicine and the 2006 Institute of Medicine reports from the Committee on the Future of Emergency Care in the US health system. Academic Emergency Medicine. 2006;13(12):1345-51.
- Cameron P, Schull M, Cooke M. A framework for measuring quality in the emergency department. British Association for Accident and Emergency Medicine; 2011. p. 735-40.
- Galarraga JE, Pines JM. Costs of ED episodes of care in the United States. The American journal of emergency medicine. 2016;34(3):357-65.
- Hand DJ. Principles of data mining. Drug safety. 2007;30(7):621-2.
- Lucini FR, Fogliatto FS, da Silveira GJ, Neyeloff JL, Anzanello MJ, Kuchenbecker RS, et al. Text mining approach to predict hospital admissions using early medical records from the emergency department. International journal of medical informatics. 2017;100:1-8.
- Graham B, Bond R, Quinn M, Mulvenna M. Using data mining to predict hospital admissions from the emergency department. IEEE Access. 2018 Feb 22;6:10458-69.
- Parker CA, Liu N, Wu SX, Shen Y, Lam SSW, Ong MEH. Predicting hospital admission at the emergency department triage: A novel prediction model. The American journal of emergency medicine. 2019;37(8):1498-504.
- Mowbray F, Zargoush M, Jones A, de Wit K, Costa A. Predicting hospital admission for older emergency department patients: Insights from machine learning. International Journal of Medical Informatics. 2020;140:104163.
- De Hond A, Raven W, Schinkelshoek L, Gaakeer M, Ter Avest E, Sir O, et al. Machine learning for developing a prediction model of hospital admission of emergency department patients: Hype or hope? International journal of medical informatics. 2021;152:104496.
- Menditto VG, Fulgenzi F, Bonifazi M, Gnudi U, Gennarini S, Mei F, et al. Predictors of readmission requiring hospitalization after discharge from emergency departments in patients with COVID-19. The American journal of emergency medicine. 2021;46:146-9.
- James G, Witten D, Hastie T, Tibshirani R. An introduction to statistical learning: Springer; 2013.
- Larose DT. Data Mining Meyhods and Models. John Willey & Sons. Inc New Jersey. 2006.
- Fernandes M, Vieira SM, Leite F, Palos C, Finkelstein S, Sousa JM. Clinical decision support systems for triage in the emergency department using intelligent systems: a review. Artificial Intelligence in Medicine. 2020;102:101762.
16. Boyle J, Jessup M, Crilly J, Green D, Lind J, Wallis M, et al. Predicting emergency department admissions. Emergency Medicine Journal. 2012;29(5):358-65.
)