Medical service demand forecasting using a hybrid model based on ARIMA and self-adaptive filtering method

作者：Yihuai Huang, Chao Xu, Mengzhong Ji, Wei Xiang, Da He · 发表于：BMC Medical Informatics and Decision Making · 年份：2020 · DOI：10.1186/s12911-020-01256-1 · 被引用次数：51 · 研究领域：Emergency and Acute Care Studies、Forecasting Techniques and Applications、Machine Learning in Healthcare

BACKGROUND: Accurate forecasting of medical service demand is beneficial for the reasonable healthcare resource planning and allocation. The daily outpatient volume is characterized by randomness, periodicity and trend, and the time series methods, like ARIMA are often used for short-term outpatient visits forecasting. Therefore, to further enlarge the prediction horizon and improve the prediction accuracy, a hybrid prediction model integrating ARIMA and self-adaptive filtering method is proposed. METHODS: The ARIMA model is first used to identify the features like cyclicity and trend of the time series data and to estimate the model parameters. The parameters are then adjusted by the steepest descent algorithm in the adaptive filtering method to reduce the prediction error. The hybrid model is validated and compared with traditional ARIMA by several test sets from the Time Series Data Library (TSDL), a weekly emergency department (ED) visit case from literature study, and the real cases of prenatal examinations and B-ultrasounds in a maternal and child health care center (MCHCC) in Ningbo. RESULTS: For TSDL cases the prediction accuracy of the hybrid prediction is improved by 80-99% compared with the ARIMA model. For the weekly ED visit case, the forecasting results of the hybrid model are better than those of both traditional ARIMA and ANN model, and similar to the ANN combined data decomposition model mentioned in the literature. For the actual data of MCHCC in Ningbo, the...