TY - JOUR AU - Zhou, Lingling AU - Zhao, Ping AU - Wu, Dongdong AU - Cheng, Cheng AU - Huang, Hao PY - 2018 DA - 2018/06/15 TI - Time series model for forecasting the number of new admission inpatients JO - BMC Medical Informatics and Decision Making SP - 39 VL - 18 IS - 1 AB - 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. SN - 1472-6947 UR - https://doi.org/10.1186/s12911-018-0616-8 DO - 10.1186/s12911-018-0616-8 ID - Zhou2018 ER -