Mining Anomalies in Medicare Big Data Using Patient Rule Induction Method

Saad Sadiq, Yudong Tao, Yilin Yan, Mei-Ling Shyu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Citations (Scopus)

Abstract

The public health infrastructure delivers proper health care services as part of the basic needs of the general population. The health care system in the United States is rapidly changing in order to provide a better and convenient healthcare system to the public. Unfortunately, this comprehensive expand has also given rise to healthcare frauds in recent years where losses surge up to $1.8 billion in the country. Organizations such as the Center for Medicare Services (CMS) have started providing accesses to comprehensive medical big data to promote the identification of healthcare frauds as an important research topic. In this paper, we will use the Patient Rule Induction Method (PRIM) based bump hunting method to identify the spaces of higher modes and masses to indicate the peak anomalies in the CMS 2014 dataset. By applying our framework, we can find a way to observe anomalies, which can be attributed to frauds in legal medical practices or other interesting insights in the CMS dataset. This will enable us to characterize the attribute space and explain the events incurring losses to the medicare/medicaid program. The proposed framework is compared with several methods to illustrate the efficiency and effectiveness of the proposed framework for fraud detection.

Original languageEnglish (US)
Title of host publicationProceedings - 2017 IEEE 3rd International Conference on Multimedia Big Data, BigMM 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages185-192
Number of pages8
ISBN (Electronic)9781509065493
DOIs
StatePublished - Jun 30 2017
Event3rd IEEE International Conference on Multimedia Big Data, BigMM 2017 - Laguna Hills, United States
Duration: Apr 19 2017Apr 21 2017

Other

Other3rd IEEE International Conference on Multimedia Big Data, BigMM 2017
CountryUnited States
CityLaguna Hills
Period4/19/174/21/17

Fingerprint

Health care
Public health
Big data

Keywords

  • Anomaly detection
  • Fraud detection
  • Medicare fraud detection
  • Patient Rule Induction Method (PRIM)

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems
  • Signal Processing
  • Media Technology

Cite this

Sadiq, S., Tao, Y., Yan, Y., & Shyu, M-L. (2017). Mining Anomalies in Medicare Big Data Using Patient Rule Induction Method. In Proceedings - 2017 IEEE 3rd International Conference on Multimedia Big Data, BigMM 2017 (pp. 185-192). [7966740] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BigMM.2017.56

Mining Anomalies in Medicare Big Data Using Patient Rule Induction Method. / Sadiq, Saad; Tao, Yudong; Yan, Yilin; Shyu, Mei-Ling.

Proceedings - 2017 IEEE 3rd International Conference on Multimedia Big Data, BigMM 2017. Institute of Electrical and Electronics Engineers Inc., 2017. p. 185-192 7966740.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Sadiq, S, Tao, Y, Yan, Y & Shyu, M-L 2017, Mining Anomalies in Medicare Big Data Using Patient Rule Induction Method. in Proceedings - 2017 IEEE 3rd International Conference on Multimedia Big Data, BigMM 2017., 7966740, Institute of Electrical and Electronics Engineers Inc., pp. 185-192, 3rd IEEE International Conference on Multimedia Big Data, BigMM 2017, Laguna Hills, United States, 4/19/17. https://doi.org/10.1109/BigMM.2017.56
Sadiq S, Tao Y, Yan Y, Shyu M-L. Mining Anomalies in Medicare Big Data Using Patient Rule Induction Method. In Proceedings - 2017 IEEE 3rd International Conference on Multimedia Big Data, BigMM 2017. Institute of Electrical and Electronics Engineers Inc. 2017. p. 185-192. 7966740 https://doi.org/10.1109/BigMM.2017.56
Sadiq, Saad ; Tao, Yudong ; Yan, Yilin ; Shyu, Mei-Ling. / Mining Anomalies in Medicare Big Data Using Patient Rule Induction Method. Proceedings - 2017 IEEE 3rd International Conference on Multimedia Big Data, BigMM 2017. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 185-192
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