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Medical Fraud and Abuse Detection System Based on Machine Learning

Author

Listed:
  • 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)

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.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:19:p:7265-:d:423780
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    References listed on IDEAS

    as
    1. 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.
    2. Fen-May Liou & Ying-Chan Tang & Jean-Yi Chen, 2008. "Detecting hospital fraud and claim abuse through diabetic outpatient services," Health Care Management Science, Springer, vol. 11(4), pages 353-358, December.
    Full references (including those not matched with items on IDEAS)

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