Time series model for forecasting the number of new admission inpatients.

Journal: BMC medical informatics and decision making
Published Date: Jun 15, 2018

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.

Authors

  • Lingling Zhou
    Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430001, China. zllgwy@126.com.
  • Ping Zhao
    Department of Information, Research Institute of Field Surgery, Daping Hospital of Army Medical University, 10 Changjiang Access Road, Chongqing, 400042, China.
  • Dongdong Wu
    Department of Information, Research Institute of Field Surgery, Daping Hospital of Army Medical University, 10 Changjiang Access Road, Chongqing, 400042, China.
  • Cheng Cheng
    School of Artificial Intelligence and Automation, MOE Key Lab of Intelligent Control and Image Processing, Huazhong University of Science and Technology, Wuhan 430074, China.
  • Hao Huang
    School of Information Science and Engineering, Xinjiang University, Shangli Road, Urumqi 830046, China.

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