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NARX Neural Networks Model for Forecasting Daily Patient Arrivals in the Emergency Department

NARX Neural Networks Model for Forecasting Daily Patient Arrivals in the Emergency Department

Melih Yucesan, Suleyman Mete, Faruk Serin, Erkan Celik, Muhammet Gul
Copyright: © 2020 |Pages: 18
ISBN13: 9781799825814|ISBN10: 1799825817|EISBN13: 9781799825821
DOI: 10.4018/978-1-7998-2581-4.ch001
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MLA

Yucesan, Melih, et al. "NARX Neural Networks Model for Forecasting Daily Patient Arrivals in the Emergency Department." Computational Intelligence and Soft Computing Applications in Healthcare Management Science, edited by Muhammet Gul, et al., IGI Global, 2020, pp. 1-18. https://doi.org/10.4018/978-1-7998-2581-4.ch001

APA

Yucesan, M., Mete, S., Serin, F., Celik, E., & Gul, M. (2020). NARX Neural Networks Model for Forecasting Daily Patient Arrivals in the Emergency Department. In M. Gul, E. Celik, S. Mete, & F. Serin (Eds.), Computational Intelligence and Soft Computing Applications in Healthcare Management Science (pp. 1-18). IGI Global. https://doi.org/10.4018/978-1-7998-2581-4.ch001

Chicago

Yucesan, Melih, et al. "NARX Neural Networks Model for Forecasting Daily Patient Arrivals in the Emergency Department." In Computational Intelligence and Soft Computing Applications in Healthcare Management Science, edited by Muhammet Gul, et al., 1-18. Hershey, PA: IGI Global, 2020. https://doi.org/10.4018/978-1-7998-2581-4.ch001

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Abstract

Regarding measuring of service quality at the emergency departments (ED), essential parameters are length of stay (LOS) and waiting times. Patient arrivals, which is related to LOS and waiting times, is hard to forecast and is affected by many parameters. Therefore, authors employed a Nonlinear Autoregressive Exogenous (NARX) model for forecasting of ED arrivals. NARX models are used extensively in many applications that show non-linear and dynamic behavior, but as far as authors know, the NARX method has not yet been used in the forecast of ED arrivals before. In this study, calendar and climatic variables are defined as input parameters. Patient Arrivals is defined as output parameter. A commercial software, MATLAB, was used to train and test the data set. To find the best network architecture Levenberg-Marquardt (LM) and Bayesian Regularization (BR) algorithms, different lags, and number of neurons were tested. R-squared and mean square error (MSE) are used to evaluate the accuracy of the tested networks.

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