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Time series model for forecasting the number of new admission inpatients.
BMC Med Inform Decis Mak. 2018 06 15; 18(1):39.BM

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.

Authors+Show Affiliations

Department of Information, Research Institute of Field Surgery, Daping Hospital of Army Medical University, 10 Changjiang Access Road, Chongqing, 400042, China.Department of Information, Research Institute of Field Surgery, Daping Hospital of Army Medical University, 10 Changjiang Access Road, Chongqing, 400042, China.Department of Information, Research Institute of Field Surgery, Daping Hospital of Army Medical University, 10 Changjiang Access Road, Chongqing, 400042, China.Department of Information, Research Institute of Field Surgery, Daping Hospital of Army Medical University, 10 Changjiang Access Road, Chongqing, 400042, China.Department of Information, Research Institute of Field Surgery, Daping Hospital of Army Medical University, 10 Changjiang Access Road, Chongqing, 400042, China. m13608388426@163.com.

Pub Type(s)

Journal Article

Language

eng

PubMed ID

29907102

Citation

Zhou, Lingling, et al. "Time Series Model for Forecasting the Number of New Admission Inpatients." BMC Medical Informatics and Decision Making, vol. 18, no. 1, 2018, p. 39.
Zhou L, Zhao P, Wu D, et al. Time series model for forecasting the number of new admission inpatients. BMC Med Inform Decis Mak. 2018;18(1):39.
Zhou, L., Zhao, P., Wu, D., Cheng, C., & Huang, H. (2018). Time series model for forecasting the number of new admission inpatients. BMC Medical Informatics and Decision Making, 18(1), 39. https://doi.org/10.1186/s12911-018-0616-8
Zhou L, et al. Time Series Model for Forecasting the Number of New Admission Inpatients. BMC Med Inform Decis Mak. 2018 06 15;18(1):39. PubMed PMID: 29907102.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Time series model for forecasting the number of new admission inpatients. AU - Zhou,Lingling, AU - Zhao,Ping, AU - Wu,Dongdong, AU - Cheng,Cheng, AU - Huang,Hao, Y1 - 2018/06/15/ PY - 2017/07/26/received PY - 2018/05/23/accepted PY - 2018/6/17/entrez PY - 2018/6/17/pubmed PY - 2019/3/20/medline KW - Hybrid model KW - NARNN model KW - New admission inpatients KW - SARIMA model KW - Time series forecasting SP - 39 EP - 39 JF - BMC medical informatics and decision making JO - BMC Med Inform Decis Mak VL - 18 IS - 1 N2 - 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. SN - 1472-6947 UR - https://www.unboundmedicine.com/medline/citation/29907102/Time_series_model_for_forecasting_the_number_of_new_admission_inpatients_ L2 - https://bmcmedinformdecismak.biomedcentral.com/articles/10.1186/s12911-018-0616-8 DB - PRIME DP - Unbound Medicine ER -