Abstract
The goal of this study is to explore and evaluate the use of a hybrid ARIMA-SVR approach to forecast daily radiology emergency patient flow. Owing to the fact that emergency patient flow is highly uncertain and dynamic, the forecasting problem is regarded as a complicated task. As the emergency patient flow may have both linear and nonlinear patterns, this paper presents a hybrid ARIMA-SVR approach, which hybridizes autoregressive integrated moving average (ARIMA) model and support vector regression (SVR) model to predict emergency patient arrivals. The proposed model is applied to 4 years of daily emergency visits data in the radiology department of a large hospital to justify the performance of the hybrid model against single models. The MAPE, RMSE and MAE of the hybrid model are 7.02%, 19.20 and 14.97, respectively. Furthermore, the hybrid model achieves better prediction performance than its competitors because it can capture the linear and nonlinear patterns simultaneously. Experimental results indicate that the proposed hybrid ARIMA-SVR approach is a promising alternative for forecasting emergency patient flow. These findings are beneficial for efficient patient flow management and scheduling decisions optimization.
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Acknowledgements
The authors gratefully acknowledge the support from the radiology department and the department of operations management of WCH. This work is sponsored by the Nature Science Foundation of China (Grant Nos. 71532007, 71131006, 71172197, 71673011 and 71273036), Central University Fund of Sichuan University (Grant No. skgt201202), and Key Research and Development Plan of Science and Technology Department of Sichuan Province (Grant Nos. 2017GZ0315 and 2017GZ0333).
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Zhang, Y., Luo, L., Yang, J. et al. A hybrid ARIMA-SVR approach for forecasting emergency patient flow. J Ambient Intell Human Comput 10, 3315–3323 (2019). https://doi.org/10.1007/s12652-018-1059-x
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DOI: https://doi.org/10.1007/s12652-018-1059-x