Articlegold2018

Time series model for forecasting the number of new admission inpatients

Lingling Zhou · Ping Zhao · Dongdong Wu · Cheng Cheng · Hao Huang

126 citations0/0 files storedBMC Medical Informatics and Decision Making

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Forecasting Techniques and Applications Healthcare Operations and Scheduling Optimization Time Series Analysis and Forecasting

Abstract

BACKGROUND: Hospital crowding is a rising problem, effective predicting and detecting managment can helpful to reduce crowding. Our team has successfully proposed a hybrid model combining both the autoregressive integrated moving average (ARIMA) and the nonlinear autoregressive neural network (NARNN) models in the schistosomiasis and hand, foot, and mouth disease forecasting study. In this paper, our aim is to explore the application of the hybrid ARIMA-NARNN model to track the trends of the new admission inpatients, which provides a methodological basis for reducing crowding. METHODS: We used the single seasonal ARIMA (SARIMA), NARNN and the hybrid SARIMA-NARNN model to fit and forecast the monthly and daily number of new admission inpatients. The root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) were used to compare the forecasting performance among the three models. The modeling time range of monthly data included was from January 2010 to June 2016, July to October 2016 as the corresponding testing data set. The daily modeling data set was from January 4 to September 4, 2016, while the testing time range included was from September 5 to October 2, 2016. RESULTS: For the monthly data, the modeling RMSE and the testing RMSE, MAE and MAPE of SARIMA-NARNN model were less than those obtained from the single SARIMA or NARNN model, but the MAE and MAPE of modeling performance of SARIMA-NARNN model did not improve. For the daily data, all RMSE, MAE and MAPE of NARNN model were the lowest both in modeling stage and testing stage. CONCLUSIONS: Hybrid model does not necessarily outperform its constituents' performances. It is worth attempting to explore the reliable model to forecast the number of new admission inpatients from different data.

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References

1996Designing a neural network for forecasting financial and economic time series 2016Predicting the Direction of Stock Market Index Movement Using an Optimized Artificial Neural Network Model 2005A comparative study of autoregressive neural network hybrids 2012A new linear & nonlinear artificial neural network model for time series forecasting

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