Fraudulent medical behavior detection based on hybrid approach

doi:10.3969/j.issn.1000-5641.2017.05.012

Abstract

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

CLC Number:

TP311

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.

References

[1] SHI Y, SUN C, LI Q, et al. A fraud resilient medical insurance claim system[C]//Thirtieth AAAI Conference on Artificial Intelligence. USA:AAAI Press, 2016:4393-4394.
[2] XIE Z P, LI X Y, WU W Y, et al. An improved outlier detection algorithm to medical insurance[J]. IDEAL, 2016:436-444.
[3] DIONNE G, GAGNé R. Replacement cost endorsement and opportunistic fraud in automobile insurance[J]. Journal of Risk & Uncertainty, 2002, 24(3):213-230.
[4] SKIBA J M. A phenomenological study of the challenges and barriers facing insurance fraud investigators[J]. Journal of Insurance Regulation, 2013:131-136.
[5] KRAUSE J H. A patient-centered approach to health care fraud recovery[J]. Journal of Criminal Law & Criminology, 2006, 96(2):579-619.
[6] LORENZ F A. Healthcare fraud in the United States:Assessing current policy and its role in fraud prevention[J]. California State University Northridge, 2013:221-227.
[7] 李亮. 基于成本-收益理论的社会医疗保险欺诈问题研究[D]. 长沙:湖南大学, 2011.
[8] 王明慧, 陶四海. 我国大病医疗保险实施的影响因素分析[J]. 经营管理者, 2013, 21:298-298.
[9] 夏宏, 汪凯, 张守春. 医疗保险中的欺诈与反欺诈问题[J]. 现代预防医学, 2007, 34(20):3907-3908.
[10] COHEN W W. Fast effective rule induction[J]. Machine Learning Proceedings, 1995, 46(2):115-123.
[11] BIAFORE S. Predictive solutions bring more power to decision makers[J]. Health Management Technology, 1999, 20(10):12.
[12] MARCUSNEWHALL A, HALPERN D, TAN S J. Healthcare and data mining[J]. Health Management Technology, 2000.
[13] 高臻耀, 张敬谊, 林志杰, 等. 一个医保基金风险防控平台中的数据挖掘技术[J]. 计算机应用与软件, 2011, 28(8):120-122.
[14] ROBERTS S J, PENNY W, PILLOT D. Novelty, confidence and errors in connectionist systems[C]//Intelligent Sensors.[S.l.]:IET, 1996:10/1-10/6.
[15] BREUNIG M M, KRIEGEL H P, NG R T, et al. OPTICS-OF:Identifying local outliers[J]. Lecture Notes in Computer Science, 1999, 1704:262-270.
[16] 黄洪宇, 林甲祥, 陈崇成, 等. 离群数据挖掘综述[J]. 计算机应用研究, 2006, 23(8):8-13.
[17] LIU B, YIN J, XIAO Y, et al. Exploiting local data uncertainty to boost global outlier detection[C]//IEEE International Conference on Data Mining.[S.l.]:IEEE Computer Society, 2010:304-313.
[18] ESTER M, KRIEGEL H P, XU X. A density-based algorithm for discovering clusters a density-based algorithm for discovering clusters in large spatial databases with noise[C]//International Conference on Knowledge Discovery and Data Mining. USA:AAAI Press, 1996:226-231.
[19] NG R T, HAN J. Efficient and effective clustering methods for spatial data mining[C]//International Conference on Very Large Data Bases. San Francisco:Margan Kaufmann, 1994:144-155.
[20] ZHANG T, RAMAKRISHNAN R, LIVNY M. BIRCH:An efficient data clustering method for very large databases[J]. ACM SIGMOD Record, 1999, 25(2):103-114.
[21] SUN C F, SHI Y L, LI Q I, et al. A hybrid approach for detecting fraudulent medical insurance claims:(Extended abstract)[C]//Proceedings of the 2016 Interational Conference on Autonomous) Agents & Multiagent Systems. Singapore:IFAAMS, 2016:1287-1288.
[22] MOYANO L G, APPEL A P, SANTANA V F D, et al. GraPhys:Understanding health care insurance data through graph analytics[C]//International Conference Companion on World Wide Web.[S.l.]:International World Wide Web Conferences Steering Committee, 2016:227-230.
[23] BAUDER R A, KHOSHGOFTAAR T M. A novel method for fraudulent medicare claims detection from expected payment deviations (Application Paper)[C]//IEEE, International Conference on Information Reuse and Integration.[S.l.]:IEEE, 2016:11-19.
[24] 关皓文. 基于离群点检测方法的医保异常发现[D]. 济南:山东大学, 2016.
[25] HE Z, XU X, DENG S. Squeezer:An efficient algorithm for clustering categorical data[J]. Journal of Computer Science and Technology, 2002, 17(5):611-624.