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基于BP神经网络和时间序列的我国卫生人力资源研究
引用本文:石丛,王健.基于BP神经网络和时间序列的我国卫生人力资源研究[J].中国初级卫生保健,2013(11):22-24.
作者姓名:石丛  王健
作者单位:山东大学卫生管理政策研究中心济南250012
摘    要:目的 从卫生服务需求角度预测我国大陆地区的卫生人力资源总量的变化趋势,分析卫生人力资源规划过程中的缺陷和不足,为卫生行政部门制定公共卫生人力发展规划、优化人力资源配置提供科学依据.方法 通过收集1994-2010年《中国卫生统计年鉴》资料,获得我国大陆卫生人力资源配置总量、卫生服务需求及其影响因素等相关数据.利用ARIMA模型对我国大陆卫生总费用、年末总人口等资料进行经典时间序列模型拟合及预测,利用BP神经网络模型进行卫生人力资源需求总量的预测.结果 1993-2009年我国卫生人力资源总量呈递增趋势,但相比于其他卫生资源增速缓慢.预测得出2010-2014年末我国卫生人力资源需求总量将分别达到1 076.02万人、837.52万人、1 073.37万人、1 076.02万人和1 075.94万人.结论 我国未来卫生人力资源总量缺口依然巨大,急需进一步补充,建议进一步加大卫生人力资源经费投入,在增加卫生人力资源数量的同时,努力提高卫生人力资源质量.

关 键 词:卫生人力资源  人工神经网络模型  时间序列分析

Study of Health Human Resource in China Based on the Model of BP-ANN and Arima
SHI Cong,WANG Jian.Study of Health Human Resource in China Based on the Model of BP-ANN and Arima[J].Chinese Primary Health Care,2013(11):22-24.
Authors:SHI Cong  WANG Jian
Institution:Center for Health Management and Policy, Shandong University, Ji'nan, 250012, China
Abstract:OBJECTIVE To predict the total amount of the health human resource trend in Chinese Mainland and provide the scientific basis for the public health administrative department by analyzing defects and deficiences in the process of health human resorce management. METHODS Collect related material in 2004--2010 Chinese health statistics yearbook to get the amount of health human resource and health services needs in China. Use ARIMA model, neural network model for predictions of total health expenditure, health service need and the influencing factors in Chinese Mainland. RESULTS The total amount of health human resource in 1993 to 2009 showed an increasing trend, but the growth is slow compared to other health resources. It isPredicted that the amounts of our health human resources from 2010 to 2014 are 10 760 200, 8 375 200, 10 733 700, 10 760 200 and 10 759 400. CONCLUSION The gap of our health human resources is still great which needs to be narrowed. It is recommended to further increase health human resources funding, expand the amount of our health human resource while striving to improve the quality of health human resources.
Keywords:health human resource  artificial neural network model  time series analysis
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