Abstract
Accurately predicting the trend of outpatient visits by mathematical modeling can help policy makers manage hospitals effectively, reasonably organize schedules for human resources and finances, and appropriately distribute hospital material resources. In this study, a hybrid method based on empirical mode decomposition and back-propagation artificial neural networks optimized by particle swarm optimization is developed to forecast outpatient visits on the basis of monthly numbers. The data outpatient visits are retrieved from January 2005 to December 2013 and first obtained as the original time series. Second, the original time series is decomposed into a finite and often small number of intrinsic mode functions by the empirical mode decomposition technique. Third, a three-layer back-propagation artificial neural network is constructed to forecast each intrinsic mode functions. To improve network performance and avoid falling into a local minimum, particle swarm optimization is employed to optimize the weights and thresholds of back-propagation artificial neural networks. Finally, the superposition of forecasting results of the intrinsic mode functions is regarded as the ultimate forecasting value. Simulation indicates that the proposed method attains a better performance index than the other four methods.
Highlights
Obtaining healthcare in China is currently challenging because the growth rate of healthcare agencies is far lower than the rise in patient needs
This study aims to assess the forecasting accuracy of back-propagation ANN (BPANN) models coupled with EMD for outpatient visits
The study protocol and utilization of outpatient visit data were reviewed from a hospitals in Nanning City, China, and no ethical issue was identified
Summary
Obtaining healthcare in China is currently challenging because the growth rate of healthcare agencies is far lower than the rise in patient needs. Forecasting the number of outpatient visits will increase the efficiency of planning and the delivery of outpatient management. This ability can help healthcare administrators oversee hospitals effectively, reasonably organize schedules for human resources and finances, and properly distribute hospital material resources. Forecasting the number of outpatient visits has become an important issue in public health and has motivated many researchers to establish mathematical models to realize such predictions, especially in China. Ching proposed a fuzzy time series method based on the weighted-transitional matrix, as well as the expectation method and grade-selection approach, to forecast the number of outpatient visits[1].
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