|
|
|
| Application of ARIMA-BP neural network combination forecast model in heath human resource allocation |
| Zhai Xiangming1, Zhu Qiuli2, He Xiaomin1 |
1 School of Public Heath and Management, Binzhou Medical University, Yantai 264003, China;
2 Shandong College of Traditional Chinese Medicine |
|
|
|
|
Abstract Objective By constructing combination forecast models to improve the prediction accuracy, and to provide the basis for the rational allocation of health resources. Methods ARIMA and BP neural network were combined to establish an ARIMA-BP neural network model. ARIMA model is used to predict the linear part of the data of health human resource, and the residual of the model is nonlinear, and then the BP neural network model is used to forecast the nonlinear residual sequence. The prediction of the two models is added as the forecast value of the sequence, and the random error generated by the BP neural network model is used as the prediction error of the ARIMA-BP neural network model, and the final prediction result is obtained. Finally the data of the number of health technical personnel in Shandong Povince from 1995 to 2012 were applied to verify the model, and to forecast the number of health technical personnel in Shandong Province from 2013 to 2015. Results The verification of the number of health technical personnel data of Shandong Povince from 1995 to 2012 showed that the combination forecast model which was formed by using ARIMA model to predict the linear data, and using BP neural network to predict the error of ARIMA, reduced the ARIMA model prediction error and improved the accuracy of prediction. The combined model forecast the numbers of health technicians in 2013, 2014 and 2015 in Shandong Province were 581 297, 635 013 and 686 465 respectively. Conclusion The combination forecasting model can make use of a variety of sample information to a large extent, and consider the problem more systematically than a single comprehensive predictive model to improve prediction accuracy. Therefore, it may be applied in human resources for health.
|
|
Received: 08 July 2015
|
|
|
|
[1] 赵晓雯,刘国祥,孙正春.黑龙江省卫生人力资源现况分析[J].中国医院管理,2006,26(4):45-47.
[2] 应岚,吴欣娟,马丽莉,等.北京地区50所医院护理人力配置现状调查分析[J].中华医院管理杂志,2006,22(6):400-401.
[3] 杨位钦,顾岚.时间序列分析与动态数据建模[M].北京:北京理工大学出版社,1988:342-346.
[4] Liu LM,Hanssens DM.Identification of multiple-input transfer function models[J]. Comm statist Theory Methods,1982,1(3):297-314.
[5] 樊欢欢,张凌云.EVIEWS统计分析与应用[M].北京:机械工业出版社,2009.
[6] 张筠莉,杨祯山.现代医院门诊量的灰色RBF神经网络预测[J].计算机工程与应用,2010,46(29):225-228.
[7] 傅荟璇.MATLAB神经网络应用设计[M].北京:机械工业出版社,2010.
[8] 盛艳波.基于BP神经网络和ARIMA组合模型测浙江省人均国内生产总值[J].商场现代化,2006(23):49-50.
[9] 雷可为,陈瑛.基于BP神经网络和ARIMA组合模型的中国入境游客量预测[J].旅游学刊,2007,22(4):20-24.
[10]陈斌,邵燕华,沈潇.用时间序列模型预测2015年浙江妇幼人口健康水平[J].中国医院统计,2012,19(4):245-248. |
| [1] |
Lian Hengli, Yu Jianqin, Fu Yinghui. Application of ARIMA and SARIMA model in predicting hospital outpatients and discharges[J]. journal1, 2017, 24(2): 81-84. |
| [2] |
Liu Guozhu. Comparative study of ARIMA model and GM (1,1) model in hospital outpatient service forecast[J]. journal1, 2017, 24(1): 5-8. |
| [3] |
. [J]. journal1, 2016, 23(4): 296-298. |
| [4] |
. [J]. journal1, 2016, 23(2): 141-142. |
| [5] |
Rao Yi. A study on the trend of hand-foot-mouth disease in a district of Dongguan based on SARIMA model[J]. journal1, 2016, 23(1): 8-11. |
| [6] |
Zhang Juan, Zhang Hongcheng. Forecast analysis of outpatient number based on seasonal ARIMA model[J]. journal1, 2015, 22(6): 423-426. |
|
|
|
|
