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A Hybrid Approach for Forecasting Patient Visits in Emergency Department
Author(s) -
Xu Qinneng,
Tsui KwokLeung,
Jiang Wei,
Guo Hainan
Publication year - 2016
Publication title -
quality and reliability engineering international
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.913
H-Index - 62
eISSN - 1099-1638
pISSN - 0748-8017
DOI - 10.1002/qre.2095
Subject(s) - autoregressive integrated moving average , outlier , computer science , exponential smoothing , artificial neural network , econometrics , time series , moving average , operations research , artificial intelligence , engineering , machine learning , mathematics , computer vision
An accurate forecast of patient visits in emergency departments (EDs) is one of the key challenges for health care policy makers to better allocate medical resources and service providers. In this paper, a hybrid autoregressive integrated moving average–linear regression (ARIMA–LR) approach, which combines ARIMA and LR in a sequential manner, is developed because of its ability to capture seasonal trend and effects of predictors. The forecasting performance of the hybrid approach is compared with several widely used models, generalized linear model (GLM), ARIMA, ARIMA with explanatory variables (ARIMAX), and ARIMA–artificial neural network (ANN) hybrid model, using two real‐world data sets collected from hospitals in DaLian, LiaoNing Province, China. The hybrid ARIMA–LR model is shown to outperform existing models in terms of forecasting accuracy. Moreover, involving a smoothing process is found helpful in reducing the interference by holiday outliers. The proposed approach can be a competitive alternative to forecast short‐term daily ED volume. Copyright © 2016 John Wiley & Sons, Ltd.