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dc.contributor.authorAral, K. D.en_US
dc.contributor.authorGüvenir H. A.en_US
dc.contributor.authorSabuncuoğlu, T.en_US
dc.contributor.authorAkar, A. R.en_US
dc.date.accessioned2016-02-08T09:47:41Z
dc.date.available2016-02-08T09:47:41Z
dc.date.issued2012en_US
dc.identifier.issn0169-2607
dc.identifier.urihttp://hdl.handle.net/11693/21531
dc.description.abstractPrescription fraud is a main problem that causes substantial monetary loss in health care systems. We aimed to develop a model for detecting cases of prescription fraud and test it on real world data from a large multi-center medical prescription database. Conventionally, prescription fraud detection is conducted on random samples by human experts. However, the samples might be misleading and manual detection is costly. We propose a novel distance based on data-mining approach for assessing the fraudulent risk of prescriptions regarding cross-features. Final tests have been conducted on adult cardiac surgery database. The results obtained from experiments reveal that the proposed model works considerably well with a true positive rate of 77.4% and a false positive rate of 6% for the fraudulent medical prescriptions. The proposed model has the potential advantages including on-line risk prediction for prescription fraud, off-line analysis of high-risk prescriptions by human experts, and self-learning ability by regular updates of the integrative data sets. We conclude that incorporating such a system in health authorities, social security agencies and insurance companies would improve efficiency of internal review to ensure compliance with the law, and radically decrease human-expert auditing costs. © 2011 Elsevier Ireland Ltd.en_US
dc.language.isoEnglishen_US
dc.source.titleComputer Methods and Programs in Biomedicineen_US
dc.relation.isversionofhttp://dx.doi.org/10.1016/j.cmpb.2011.09.003en_US
dc.subjectData miningen_US
dc.subjectHealth care frauden_US
dc.subjectOutlier detectionen_US
dc.subjectPrescription frauden_US
dc.subjectComputer interfaceen_US
dc.titleA prescription fraud detection modelen_US
dc.typeArticleen_US
dc.departmentDepartment of Computer Engineeringen_US
dc.departmentDepartment of Industrial Engineeringen_US
dc.citation.spage37en_US
dc.citation.epage46en_US
dc.citation.volumeNumber106en_US
dc.citation.issueNumber1en_US
dc.identifier.doi10.1016/j.cmpb.2011.09.003en_US
dc.publisherElsevier Ireland Ltd.en_US


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