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周杰辉, 朱融晨, 张玮, 陆俊华, 应豪超, 吴健, 陈为. MedicareVis: 面向医保反欺诈的联合可视分析方法[J]. 计算机辅助设计与图形学学报, 2021, 33(9): 1311-1317. DOI: 10.3724/SP.J.1089.2021.18981
引用本文: 周杰辉, 朱融晨, 张玮, 陆俊华, 应豪超, 吴健, 陈为. MedicareVis: 面向医保反欺诈的联合可视分析方法[J]. 计算机辅助设计与图形学学报, 2021, 33(9): 1311-1317. DOI: 10.3724/SP.J.1089.2021.18981
Zhou Jiehui, Zhu Rongchen, Zhang Wei, Lu Junhua, Ying Haochao, Wu Jian, Chen Wei. MedicareVis: a Joint Visual Analytics Approach for Anti-Fraud in Medical Insurance[J]. Journal of Computer-Aided Design & Computer Graphics, 2021, 33(9): 1311-1317. DOI: 10.3724/SP.J.1089.2021.18981
Citation: Zhou Jiehui, Zhu Rongchen, Zhang Wei, Lu Junhua, Ying Haochao, Wu Jian, Chen Wei. MedicareVis: a Joint Visual Analytics Approach for Anti-Fraud in Medical Insurance[J]. Journal of Computer-Aided Design & Computer Graphics, 2021, 33(9): 1311-1317. DOI: 10.3724/SP.J.1089.2021.18981

MedicareVis: 面向医保反欺诈的联合可视分析方法

MedicareVis: a Joint Visual Analytics Approach for Anti-Fraud in Medical Insurance

  • 摘要: 医保欺诈造成严重经济损失,对医保制度的安全稳定造成巨大冲击.然而,现有工作难以支持对多类欺诈行为的联合分析和探索.基于多维时序相关的医保数据,提出面向医保反欺诈的联合可视分析方法.该方法支持医保数据的时空多角度过滤,以更快地定位欺诈;并通过不同类别、不同主体的欺诈之间的关联分析,挖掘出隐蔽的欺诈行为;与医保领域专家合作,设计并实现一个面向医保反欺诈的可视分析系统MedicareVis,通过真实医保数据上的实例研究与专家访谈,证明该方法在帮助检测欺诈行为关联上的有效性和实用性.

     

    Abstract: Medical insurance fraud causes serious economic losses,which has a great impact on the safety and stability of the medical insurance system.However,existing work does not support the joint analysis and exploration of various types of fraud.Based on the medical insurance data related to multi-dimensional time series,a visual analytics approach for anti-fraud in medical insurance is proposed.It can perform spa-tio-temporal filtering of medical insurance data,locate fraud quickly,and discover hidden frauds by per-forming the correlation analysis between different types and different subjects of fraud.We design and de-velop MedicareVis,a visual analysis system for medical insurance anti-fraud.We demonstrate the usefulness and effectiveness of our approach in helping detect the association of fraud through a case study on re-al-world medical insurance data and interviews with domain experts.

     

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