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Modeling and Forecasting the Daily Number of Emergency Department Visits Using Hybrid Models

Modeling and Forecasting the Daily Number of Emergency Department Visits Using Hybrid Models

Görkem Sarıyer, Ceren Öcal Taşar
Copyright: © 2020 |Pages: 23
ISBN13: 9781799825814|ISBN10: 1799825817|EISBN13: 9781799825821
DOI: 10.4018/978-1-7998-2581-4.ch002
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MLA

Sarıyer, Görkem, and Ceren Öcal Taşar. "Modeling and Forecasting the Daily Number of Emergency Department Visits Using Hybrid Models." Computational Intelligence and Soft Computing Applications in Healthcare Management Science, edited by Muhammet Gul, et al., IGI Global, 2020, pp. 19-41. https://doi.org/10.4018/978-1-7998-2581-4.ch002

APA

Sarıyer, G. & Taşar, C. Ö. (2020). Modeling and Forecasting the Daily Number of Emergency Department Visits Using Hybrid Models. In M. Gul, E. Celik, S. Mete, & F. Serin (Eds.), Computational Intelligence and Soft Computing Applications in Healthcare Management Science (pp. 19-41). IGI Global. https://doi.org/10.4018/978-1-7998-2581-4.ch002

Chicago

Sarıyer, Görkem, and Ceren Öcal Taşar. "Modeling and Forecasting the Daily Number of Emergency Department Visits Using Hybrid Models." In Computational Intelligence and Soft Computing Applications in Healthcare Management Science, edited by Muhammet Gul, et al., 19-41. Hershey, PA: IGI Global, 2020. https://doi.org/10.4018/978-1-7998-2581-4.ch002

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Abstract

In this study, linear regression and neural network-based hybrid models are developed for modelling the daily ED visits. Month and week of the year, day of the week, and period of the day, are used as input variables of the linear regression model. Generated forecasts and the residuals are further processed through a multilayer perceptron model to improve the performance of forecasting. To obtain forecasts for daily number of patient visits, aggregation is used where the obtained periodical forecasts are summed up. By comparing the performances of models in generating periodical and daily forecasts, this chapter not only shows that hybrid model improves the forecasting performance significantly, but also aggregation fits well in practice.

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