【摘要】 为了解决医院医保信息管理中数据结构复杂、关系网难以建立、数据分析不全面、审核难度大等问题,文中设计了医院医保信息管理系统。首先,建立数据仓库将医院医保信息进行整合,其次,设计数据挖掘模块对医院医保信息进行处理,最后应用到医院医保信息管理中去。经实验表明,文中设计的挖掘模块中改进Apriori算法的数据处理速度是Apriori算法的十倍以上,新型决策树算法的分类准确率可以达到98%以上,在增量学习中耗时是同类算法的40%以下,使医院医保信息管理的速度和准确率大幅提升。更多还原

【Abstract】 In order to solve the difficulties which includes the complexity of data structure,difficulty to establish the relationship network,incompleteness of data analysis,and audit insufficiency in the medical insurance information management,this study uses data mining technology to optimize the medical insurance information management in hospital. First,the data warehouse is established to integrate the medical insurance information of the hospital,which would be then processed by the data mining module,and finally applies it to the medical insurance information management. The experiment shows that the data processing speed of Apriori algorithm is ten times faster than that of Apriori algorithm in the mining module designed in this study. The classification accuracy of incremental decision tree algorithm can up to more than 98%,and the time consuming is 40% less among incremental learning of the same algorithm,which greatly improves the speed and accuracy of hospital medical insurance information management.更多还原