基于混合方法的医疗欺诈行为检测

doi:10.3969/j.issn.1000-5641.2017.05.012

华东师范大学学报(自然科学版) ›› 2017, Vol. 2017 ›› Issue (5): 125-137.doi: 10.3969/j.issn.1000-5641.2017.05.012

基于混合方法的医疗欺诈行为检测

潘松松, 张伟佳

华东师范大学, 计算机科学与软件工程学院, 上海 200062

收稿日期:2017-06-20 出版日期:2017-09-25 发布日期:2017-09-25
作者简介:潘松松,女,硕士研究生,研究方向为数据挖掘.E-mail:pss_ahnu@163.com
基金资助:
国家重点研发计划（2016YFB1000904）

Fraudulent medical behavior detection based on hybrid approach

PAN Song-song, ZHANG Wei-jia

School of Computer Science and Software Engineering, East China Normal University, Shanghai 200062, China

Received:2017-06-20 Online:2017-09-25 Published:2017-09-25

摘要/Abstract

摘要： 随着医保制度的不断完善，医保覆盖率的不断扩大，医保基金的正常运转已经与人民大众的切身利益密切相关.然而，频繁就医、分解住院和异常费用支出等欺诈行为的频繁发生，极大地威胁着医保基金的正常运转.本文先使用随机森林的方法分病种进行特征选择，然后通过基于Clustering-Based Local Outlier Factor（CBLOF）的方法以及改进的CBLOF方法检测异常的结算费用.同时通过基于规则的方法检测频繁就医和分解住院行为.通过在真实医保结算数据上进行实验，实验结果证明了方法的可行性和有效性.最后，本文给出了医保基金监督平台的系统框架，通过该平台对透视分析的结果进行可视化展示.

关键词: 异常检测, 局部异常因子, CBLOF, 分解住院

Abstract: With continuous improvement of medical insurance system, coverage of medical insurance continues to expand. The normal operation of medical insurance funds has been closely related with the vital interests of the people. However, frequent occurrence of fraudulent behaviors such as frequent hospitalization, hospitalization decomposition, abnormal fees threaten the normal operation of funds. This paper firstly used random forest method to select different features according to different diseases. Then the paper applied CBLOF-based and improved CBLOF methods to detect abnormal fees. What's more, we utilized rule-based method to identity frequent hospitalization and hospitalization decomposition. Extensive experiments on real medical claim datasets demonstrate the effectiveness and efficiency of our proposal. Finally, this paper proposed a medical insurance fund supervisory system, which can display results of pivot analysis with the help of Echarts.

Key words: outlier detection, local outlier factor, CBLOF, hospitalization decomposition

中图分类号:

TP311

潘松松, 张伟佳. 基于混合方法的医疗欺诈行为检测[J]. 华东师范大学学报(自然科学版), 2017, 2017(5): 125-137.

PAN Song-song, ZHANG Wei-jia. Fraudulent medical behavior detection based on hybrid approach[J]. Journal of East China Normal University(Natural Sc, 2017, 2017(5): 125-137.

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基于混合方法的医疗欺诈行为检测

Fraudulent medical behavior detection based on hybrid approach

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