自组织数据挖掘在卫生总费用预测中的应用  

The application of self-organization of data digging to the prediction of total health expenses

作　　者：刘明霞[1] 任仕泉[1] 

机构地区：[1]四川大学工商管理学院,四川成都610064

出　　处：《卫生经济研究》2003年第12期10-12,共3页

基　　金：国家自然科学基金项目 (70103007);国家社会科学基金项目 (02CTJ001)

摘　　要：本文采用自组织数据挖掘方法建立了卫生总费用的预测模型 ,发现影响我国卫生总费用的重要因素有 :GDP、医疗卫生服务成本、全国保险福利费用总额、全国从业人员数、城镇职工医疗卫生支出、政府卫生支出等。模型预测结果显示 :中国卫生总费用在今后若干年会有比较快的增长 ,需要政府从宏观政策上加强卫生改革的力度来控制医疗卫生服务成本 。The predictive model of total health expenses was established by the method of self-organization of data digging.The important factors that effects on the total health expenses were shown to be GDP,medical service cost,total national insurance expenses,the number of employees,urban employees'health expenditures,governmental health expenditures and so on.Results from the predictive model showed that the total health expenses will increase relatively rapidly in the following years.So the government needs to control medical service cost through strengthening health reform and propel health insurance reform.

关 键 词：卫生总费用 预测模型 自组织数据挖掘 卫生改革 医疗卫生服务成本 中国 卫生事业 

分 类 号：R199.2[医药卫生—卫生事业管理]