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Article

Medical Fraud and Abuse Detection System Based on Machine Learning

1
School of Management, Zhejiang University, Hangzhou 310058, China
2
School of Material Science and Engineering, Qingdao University of Science and Technology, Qingdao 266042, China
*
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2020, 17(19), 7265; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph17197265
Received: 6 August 2020 / Revised: 19 September 2020 / Accepted: 21 September 2020 / Published: 5 October 2020
(This article belongs to the Special Issue Machine Learning and Analytics for Medical Care and Health Service)
It is estimated that approximately 10% of healthcare system expenditures are wasted due to medical fraud and abuse. In the medical area, the combination of thousands of drugs and diseases make the supervision of health care more difficult. To quantify the disease–drug relationship into relationship score and do anomaly detection based on this relationship score and other features, we proposed a neural network with fully connected layers and sparse convolution. We introduced a focal-loss function to adapt to the data imbalance and a relative probability score to measure the model’s performance. As our model performs much better than previous ones, it can well alleviate analysts’ work. View Full-Text
Keywords: healthcare fraud; medical abuse; anomaly detection healthcare fraud; medical abuse; anomaly detection
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MDPI and ACS Style

Zhang, C.; Xiao, X.; Wu, C. Medical Fraud and Abuse Detection System Based on Machine Learning. Int. J. Environ. Res. Public Health 2020, 17, 7265. https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph17197265

AMA Style

Zhang C, Xiao X, Wu C. Medical Fraud and Abuse Detection System Based on Machine Learning. International Journal of Environmental Research and Public Health. 2020; 17(19):7265. https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph17197265

Chicago/Turabian Style

Zhang, Conghai, Xinyao Xiao, and Chao Wu. 2020. "Medical Fraud and Abuse Detection System Based on Machine Learning" International Journal of Environmental Research and Public Health 17, no. 19: 7265. https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph17197265

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