Medical Fraud and Abuse Detection System Based on Machine Learning

Conghai Zhang, Xinyao Xiao and Chao Wu
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Conghai Zhang: School of Management, Zhejiang University, Hangzhou 310058, China
Xinyao Xiao: School of Material Science and Engineering, Qingdao University of Science and Technology, Qingdao 266042, China
Chao Wu: School of Management, Zhejiang University, Hangzhou 310058, China

IJERPH, 2020, vol. 17, issue 19, 1-11

Abstract: 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.

Keywords: healthcare fraud; medical abuse; anomaly detection (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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