Detecting hospital fraud and claim abuse through diabetic outpatient services
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DOI: 10.1007/s10729-008-9054-y
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- Dasgupta, Chanda Ghose & Dispensa, Gary S. & Ghose, Sanjoy, 1994. "Comparing the predictive performance of a neural network model with some traditional market response models," International Journal of Forecasting, Elsevier, vol. 10(2), pages 235-244, September.
- McKee, Thomas E. & Lensberg, Terje, 2002. "Genetic programming and rough sets: A hybrid approach to bankruptcy classification," European Journal of Operational Research, Elsevier, vol. 138(2), pages 436-451, April.
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Cited by:
- Farbmacher, Helmut & Löw, Leander & Spindler, Martin, 2022. "An explainable attention network for fraud detection in claims management," Journal of Econometrics, Elsevier, vol. 228(2), pages 244-258.
- Conghai Zhang & Xinyao Xiao & Chao Wu, 2020. "Medical Fraud and Abuse Detection System Based on Machine Learning," IJERPH, MDPI, vol. 17(19), pages 1-11, October.
- Carapinha, João L. & Ross-Degnan, Dennis & Desta, Abayneh Tamer & Wagner, Anita K., 2011. "Health insurance systems in five Sub-Saharan African countries: Medicine benefits and data for decision making," Health Policy, Elsevier, vol. 99(3), pages 193-202, March.
- Arash Rashidian & Hossein Joudaki & Taryn Vian, 2012. "No Evidence of the Effect of the Interventions to Combat Health Care Fraud and Abuse: A Systematic Review of Literature," PLOS ONE, Public Library of Science, vol. 7(8), pages 1-8, August.
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Keywords
Medical insurance fraud; National health insurance; Diabetes mellitus; Data mining; Logistic regression; Neural networks; Classification trees;All these keywords.
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