Forecasting Patient Visits to Hospitals using a WD&amp;amp;ANN-based Decomposition and Ensemble Model

Lean Yu,Geye Hang,Yang Zhao,K K Lai,Ling Tang

doi:10.12973/ejmste/80308

Abstract

Forecasting the number of patient visits to hospitals has aroused an increasingly large interest from both theoretic and application perspectives. To enhance the accuracy of forecasting hospital visits, this paper proposes a hybrid approach by coupling wavelet decomposition (WD) and artificial neural network (ANN) under the framework of “decomposition and ensemble”. In this model, the WD is first employed to decompose the original monthly data of the number of patient visits to hospitals into several components and one residual term. Then, the ANN as a powerful prediction tool is implemented to fit each decomposed component and generate individual prediction results. Finally, all individual prediction values are fused into the final prediction output by simple addition method. For illustration and verification, four sets of monthly series data of the number of patient visits to hospitals are used as the sample data, and the results show that the proposed model can obtain significantly more accurate forecasting results than all considered popular forecasting techniques.

Highlights

The high efficiency of hospital management depends to some degree on appropriate allocation of material resources and proper physician and nurse staffing because of the limit of those resources and hospital budget pressure
As for autoregressive integrated moving average (ARIMA) and artificial neural network (ANN), It is hard to define whose performance in forecasting is better for the situation differs when the experimental dataset renews and more details are described in the following content
Wavelet decomposition as a common signal processing tool can decompose the original complex data into several components that can be more processed further, which explains the importance of wavelet decomposition in the hybrid model, i.e., WD-forward neural network (FNN)-ADD

Summary

Introduction

The high efficiency of hospital management depends to some degree on appropriate allocation of material resources and proper physician and nurse staffing because of the limit of those resources and hospital budget pressure. Forecasting the number of patient visits to hospitals can be helpful in allocating limited human and material resources of hospitals (Hadavandi et al, 2012). For its great help in hospitals’ resource allocation, forecasting the number of patient visits to hospitals has been paid more and more attention to and achieved a significant status in hospital management (Safar & Alkhezzi, 2016). Milner (1997) modeled attendances at accident and emergency departments by one-off original ARIMA. Zibners (2006) used Box-Jenkins ARIMA to predict patient visits to an academic pediatric emergency department (ED). Kam (2010) used seasonal ARIMA to predict daily number of patient visits to ED. Kadri et al (2014) used ARIMA to forecast daily patient attendances.

Methods

Results

Conclusion