A prescription fraud detection model

Computer Methods and Programs in Biomedicine

Volumn 106, Issue 1, 2012, Pages 37-46

  Aral, Karca Duru ^a   Güvenir, Halil Altay ^b   Sabuncuoĝlu, Ihsan ^b   Akar, Ahmet Ruchan ^c  

^a INSEAD   (France)

^b BILKENT UNIVERSITY   (Turkey)

^c ANKARA UNIVERSITY   (Turkey)

Author keywords

Data mining; Health care fraud; Outlier detection; Prescription fraud

Indexed keywords

CARDIAC SURGERY; DATA SETS; DISTANCE-BASED; FALSE POSITIVE RATES; FRAUD DETECTION; HEALTH-CARE SYSTEM; HUMAN EXPERT; INSURANCE COMPANIES; MEDICAL PRESCRIPTION; OFF-LINE ANALYSIS; OUTLIER DETECTION; PRESCRIPTION FRAUD; RANDOM SAMPLE; REAL WORLD DATA; RISK PREDICTIONS; SELF-LEARNING ABILITY; SOCIAL SECURITY; TRUE POSITIVE RATES;

CARDIOVASCULAR SURGERY; COMPUTER CRIME; DATA MINING; HEALTH CARE; HEALTH RISKS; INSURANCE;

CRIME;

CORASPIN; DORZOLAMIDE PLUS TIMOLOL; OXYMETAZOLINE; PSEUDOEPHEDRINE; STAFINE; UNCLASSIFIED DRUG; XYLOMETAZOLINE;

AGE; ARTICLE; COMPUTER INTERFACE; DATA MINING; DRUG COST; FRAUD; HUMAN; NONBIOLOGICAL MODEL; ONLINE SYSTEM; PREDICTION; PRESCRIPTION; PRESCRIPTION FRAUD DETECTION MODEL; RECEIVER OPERATING CHARACTERISTIC; RISK ASSESSMENT;

ADULT; ALGORITHMS; ARTIFICIAL INTELLIGENCE; COMPUTER SIMULATION; DATA MINING; DATABASES, FACTUAL; FRAUD; HUMANS; PRESCRIPTION DRUGS; RISK; SOFTWARE DESIGN; TURKEY;

EID: 84857451503     PISSN: 01692607     EISSN: 18727565     Source Type: Journal    
DOI: 10.1016/j.cmpb.2011.09.003     Document Type: Article

References (36)

1. Levi M., Burrows M. Measuring the impact of fraud in the UK: a conceptual and empirical journey. British Journal of Criminology 2008, 48(3):293-318.
2. Kesselheim A.S., Studdert D.M., Mello M.M. Whistle-blowers' experiences in fraud litigation against pharmaceutical companies. New England Journal of Medicine 2010, 362(19):1832-1839.
3. Wheeler R., Aitken S. Multiple algorithms for fraud detection. Knowledge-Based Systems 2000, 13(2-3):93-99.
4. Viaene S., Derrig R.A., Baesens B., Dedene G. A comparison of state-of-the-art classification techniques for expert automobile insurance claim fraud detection. Journal of Risk and Insurance 2002, 69(3):373-421.
5. Hilas C.S., Mastorocostas P.A. An application of supervised and unsupervised learning approaches to telecommunications fraud detection. Knowledge-Based Systems 2008, 21(7):721-726.
6. Turkish Health Care Syndicate 2008 Health Care Report 2008, (Saĝli{dotless}kta 2008 Raporu, Türk Saĝli{dotless}k Sen).
7. Li J., Huang K., Jin J., Shi J. A survey on statistical methods for health care fraud detection. Journal of Health Care Management Science 2008, 11(3):275-287.
8. USA's National Health Care Anti-Fraud Association Web Page 2009, http://www.nhcaa.org/eweb/StartPage.aspx.
9. Weng X., Shen J. Detecting outlier samples in multivariate time series dataset. Knowledge-Based Systems 2008, 21(8):807-812.
10. Kim H., Pang S., Je H., Kim D., Bang S. Constructing support vector machine ensemble. Pattern Recognition 2003, 36:2757-2767.
11. Barse E., Kvarnstrom H., Jonsson E. Synthesizing test data for fraud detection systems. Proceedings of the 19th Annual Computer Security Applications Conference 2003, 384-395.
12. Syeda M., Zhang Y., Pan Y. Parallel granular neural networks for fast credit card fraud detection. Proceedings of the 2002 IEEE International Conference on Fuzzy Systems 2002.
13. Ghosh R., Reilly D. Credit card fraud detection with a neural-network. Proceedings of the Twenty-Seventh Annual Hawaii International Conference on System Sciences 1994.
14. Maes S., Tuyls K., Vanschoenwinkel B., Manderick B. Credit card fraud detection using Bayesian and neural networks. Proceedings of the 1st International NAISO Congress on Neuro Fuzzy Technologies 2002.
15. Ezawa K., Norton S. Constructing Bayesian networks to predict uncollectible telecommunications accounts. IEEE Expert 1996, 11(5):45-51.
16. Metan G., Sabuncuoglu I., Pierreval H. Real time selection of scheduling rules and knowledge extraction via dynamically controlled data mining. International Journal of Production Research 2010, 48(23):6909-6938.
17. Foster D., Stine R. Variable selection in data mining: building a predictive model for bankruptcy. Journal of American Statistical Association 2004, 99(466):303-313.
18. Belhadji E., Dionne G., Tarkhani F. A model for the detection of insurance fraud. The Geneva Papers on Risk and Insurance 2000, 25(4):517-538.
19. Pejic-Bach M. Profiling intelligent systems applications in fraud detection and prevention: survey of research articles. Proceedings of International Conference on Intelligent Systems, Modelling and Simulation 2010, 80-85.
20. Cortes C., Pregibon D. Signature-based methods for data streams. Data Mining and Knowledge Discovery 2001, 5:167-182.
21. Yamanishi K., Takeuchi J., Williams G., Milne P. On-line unsupervised outlier detection using finite mixtures with discounting learning algorithms. Data Mining and Knowledge Discovery 2004, 8:275-300.
22. Brockett P.L., Derrig R.A., Golden L.L., Levine A., Alpert M. Fraud classification using principal component analysis of RIDITs. Journal of Risk and Insurance 2002, 69(3):341-371.
23. Chan C.L., Lan C.H. A data mining technique combining fuzzy sets theory and Bayesian classifier - an application of auditing the health insurance fee. Proceedings of the International Conference on Artificial Intelligence IC-AI'2001 2001, 402-408. H.R. Arabnia (Ed.).
24. Kim M., Kim T. A neural classifier with fraud density map for effective credit card fraud detection. Proceedings of IDEAL2002 2002, 378-383.
25. He H., Graco W., Yao X. Application of genetic algorithms and k-nearest neighbour method in medical fraud detection. Proceedings of SEAL1998 1999, 74-81.
26. Cortes C., Pregibon D., Volinsky C. Computational methods for dynamic graphs. Journal of Computational and Graphical Statistics 2003, 12(4):950-970.
27. Cahill M., Chen F., Lambert D., Pinheiro J., Sun D. Detecting fraud in the real world. Handbook of Massive Datasets 2002, pp. 911-930.
28. Moreau Y., Lerouge E., Verrelst H., Vandewalle J., Stormann C., Burge P. BRUTUS: a hybrid system for fraud detection in mobile communications. Proceedings of European Symposium on Artificial Neural Networks 1999, 447-454.
29. Williams G., Huang Z. Mining the knowledge mine: the hot spots methodology for mining large real world databases. Lecture Notes in Computer Science 1997, 340-348.
30. Williams G. Evolutionary hot spots data mining: an architecture for exploring for interesting discoveries. Proceedings of PAKDD99 1999.
31. Brause R., Langsdorf T., Hepp M. Neural data mining for credit card fraud detection. Proceedings of 11th IEEE International Conference on Tools with Artificial Intelligence 1999.
32. Ormerod T., Morley N., Ball L., Langley C., Spenser C. Using ethnography to design a Mass Detection Tool (MDT) for the early discovery of insurance fraud. CHI'03 Extended Abstracts on Human Factors in Computing Systems 2003, 650-651.
33. Ortega P., Figueroa C., Ru G. A medical claim fraud/abuse detection system based on data mining: a case study in Chile. Proceedings of DMIN'06 2006, 224-231.
34. Huang S.H., Wulsin L.R., Hua L., Guo J. Dimensionality reduction for knowledge discovery in medical claims database: application to antidepressant medication utilization study. Computer Methods and Programs in Biomedicine 2009, 93(2):115-123.
35. Aydi{dotless}n T., Güvenir H.A. Modeling interestingness of streaming association rules as a benefit-maximizing classification problem. Knowledge Based Systems 2009, 37(2):1713-1718.
36. Major A.J., Riedinger D.R. EFD: a hybrid knowledge/statistical-based system for the detection of fraud. Journal of Risk and Insurance 2002, 69(3):309-324.