A prescription fraud detection model
| dc.citation.epage | 46 | en_US |
| dc.citation.issueNumber | 1 | en_US |
| dc.citation.spage | 37 | en_US |
| dc.citation.volumeNumber | 106 | en_US |
| dc.contributor.author | Aral, K. D. | en_US |
| dc.contributor.author | Güvenir H. A. | en_US |
| dc.contributor.author | Sabuncuoğlu, T. | en_US |
| dc.contributor.author | Akar, A. R. | en_US |
| dc.date.accessioned | 2016-02-08T09:47:41Z | |
| dc.date.available | 2016-02-08T09:47:41Z | |
| dc.date.issued | 2012 | en_US |
| dc.department | Department of Computer Engineering | en_US |
| dc.department | Department of Industrial Engineering | en_US |
| dc.description.abstract | Prescription 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.identifier.doi | 10.1016/j.cmpb.2011.09.003 | en_US |
| dc.identifier.issn | 0169-2607 | |
| dc.identifier.uri | http://hdl.handle.net/11693/21531 | |
| dc.language.iso | English | en_US |
| dc.publisher | Elsevier Ireland Ltd. | en_US |
| dc.relation.isversionof | http://dx.doi.org/10.1016/j.cmpb.2011.09.003 | en_US |
| dc.source.title | Computer Methods and Programs in Biomedicine | en_US |
| dc.subject | Data mining | en_US |
| dc.subject | Health care fraud | en_US |
| dc.subject | Outlier detection | en_US |
| dc.subject | Prescription fraud | en_US |
| dc.subject | Computer interface | en_US |
| dc.title | A prescription fraud detection model | en_US |
| dc.type | Article | en_US |
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