This study aims to investigate the factors affecting daily demand in an emergency department (ED) and to provide a forecasting tool in a public hospital for horizons of up to seven days.
In this study, first, the important factors to influence the demand in EDs were extracted from literature then the relevant factors to the study are selected. Then, a deep neural network is applied to constructing a reliable predictor.
Although many statistical approaches have been proposed for tackling this issue, better forecasts are viable by using the abilities of machine learning algorithms. Results indicate that the proposed approach outperforms statistical alternatives available in the literature such as multiple linear regression, autoregressive integrated moving average, support vector regression, generalized linear models, generalized estimating equations, seasonal ARIMA and combined ARIMA and linear regression.
The authors applied this study in a single ED to forecast patient visits. Applying the same method in different EDs may give a better understanding of the performance of the model to the authors. The same approach can be applied in any other demand forecasting after some minor modifications.
To the best of the knowledge, this is the first study to propose the use of long short-term memory for constructing a predictor of the number of patient visits in EDs.
The authors would like to thank Dr Lucas Coimbra, coordinator of emergency department in Risoleta Tolentino Neves Hospital for assistance with data collection. The authors also thank the research coordinator of the Brazilian ministry of education (Coordenação de Aperfeiçoamento de Pessoal de Nível Superior – CAPES, Process n. 88882.316153/2013-01), for the financial support received to conduct this research.
Yousefi, M., Yousefi, M., Fathi, M. and Fogliatto, F.S. (2019), "Patient visit forecasting in an emergency department using a deep neural network approach", Kybernetes, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/K-10-2018-0520Download as .RIS
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