负序列模式挖掘关键技术及其在医保欺诈检测中的应用研究

项目名称： 负序列模式挖掘关键技术及其在医保欺诈检测中的应用研究

项目编号： No.71271125

项目类型： 面上项目

立项/批准年度： 2013

项目学科： 管理科学

项目作者： 董祥军

作者单位： 齐鲁工业大学

项目金额： 54万元

中文摘要： 正序列模式(PSP)挖掘是检测医保欺诈的有效方法之一，作为其研究的重要补充，负序列模式(NSP)挖掘还考虑了未发生的事件，能够为医保欺诈检测提供更全面的决策信息，有重要学术意义和实用价值。本项目对NSP挖掘的关键技术及其在医保欺诈检测中的应用方法进行研究。具体为：1）探讨一种宽松负序列约束机制，既能得到数量更多的NSP以提供更全面的决策信息，又能避免NSP数量爆炸问题；寻找一种宽松约束机制下负序列候选模式的有效生成方法及其支持度的快速计算方法，以得到一个高效的NSP挖掘算法。2）探索一种从大量的正负序列模式中选取决策模式的机制，以弥补正序列模式可能误导决策的不足，为正确决策提供保障。3）探索从医保事务数据到医保序列数据的转换方法，以及医保欺诈检测知识库中正常模式和欺诈模式的提取方法。在此基础上，构建一个医保欺诈检测模型，开发相应软件系统，为有效检测欺诈提供更全面的决策支持。

中文关键词： 序列模式；负序列模式；医保欺诈；重复序列模式；

英文摘要： Positive sequential pattern (PSP) mining is one of the most effective ways to detect medical insurance frauds. As an important supplement to PSP's research，negative sequential pattern (NSP) mining also takes into account non-occurring events. Thus it can provide more comprehensive decision-making information for medical insurance fraud detection. Therefore, NSP mining has important academic significance and practical value. This project will study on key techniques of NSP mining and their applications in medical insurance fraud detection. There are three aspects in details. 1) Probing into a loose negative sequence constraint mechanism that can mine more NSPs so as to provide more comprehensive information for decision-making, but also can avoid NSPs' number explosion. By means of looking for an efficient method to generate negative sequential candidate pattern (NSCP) and a rapid method to calculate NSCP's support to adapt to the loose constraint mechanism, a high performance NSP mining algorithm can be got. 2)Looking for an efficient mechanism for pattern selection from large numbers of positive and negative sequential patterns so as to make up for the imperfection that PSP may mislead decision-making, and to make decision correctly. 3) Exploring a method to convert medical transactional data to sequential da

英文关键词： sequential patterns；negative sequential patterns；medical insurance fraud；repetitive sequential pattern；