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Big Data Mining and Analytics 2019, 2(1): 25-34 https://doi.org/10.26599/BDMA.2018.9020009
Open Access | Issue | Published: 15 October 2018

Heterogeneous Network-Based Chronic Disease Progression Mining

Show Author's Information Chenfei Sun,  Qingzhong Li( ),  Lizhen Cui,  Hui Li,  Yuliang Shi
Research Center of Software and Data Engineering, Shandong University, Jinan 250101, China.
Keywords:
disease progression, heterogeneous network, healthcare insurance fraud
Cite this article:
Sun C, Li Q, Cui L, et al. Heterogeneous Network-Based Chronic Disease Progression Mining. Big Data Mining and Analytics, 2019, 2(1): 25-34. https://doi.org/10.26599/BDMA.2018.9020009
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Abstract Full text About this article

Healthcare insurance fraud has caused billions of dollars in losses in public healthcare funds around the world. In particular, healthcare insurance fraud in chronic diseases is especially rampant. Understanding disease progression can help investigators detect healthcare insurance frauds early on. Existing disease progression methods often ignore complex relations, such as the time-gap and pattern of disease occurrence. They also do not take into account the different medication stages of the same chronic disease, which is of great help when conducting healthcare insurance fraud detection and reducing healthcare costs. In this paper, we propose a heterogeneous network-based chronic disease progression mining method to improve the current understanding on the progression of chronic diseases, including orphan diseases. The method also considers the different medication stages of the same chronic disease. Extensive experiments show that our method can outperform the existing methods by 20% in terms of F-measure.


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Heterogeneous Network-Based Chronic Disease Progression Mining

Show Author's information Chenfei Sun, Qingzhong Li( ), Lizhen Cui, Hui Li, Yuliang Shi
Research Center of Software and Data Engineering, Shandong University, Jinan 250101, China.

Abstract

Healthcare insurance fraud has caused billions of dollars in losses in public healthcare funds around the world. In particular, healthcare insurance fraud in chronic diseases is especially rampant. Understanding disease progression can help investigators detect healthcare insurance frauds early on. Existing disease progression methods often ignore complex relations, such as the time-gap and pattern of disease occurrence. They also do not take into account the different medication stages of the same chronic disease, which is of great help when conducting healthcare insurance fraud detection and reducing healthcare costs. In this paper, we propose a heterogeneous network-based chronic disease progression mining method to improve the current understanding on the progression of chronic diseases, including orphan diseases. The method also considers the different medication stages of the same chronic disease. Extensive experiments show that our method can outperform the existing methods by 20% in terms of F-measure.

Keywords: disease progression, heterogeneous network, healthcare insurance fraud

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Publication history

Received: 26 October 2017
Revised: 26 April 2018
Accepted: 03 May 2018
Published: 15 October 2018
Issue date: March 2019

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© The author(s) 2019

Acknowledgements

This work was partially supported by the National Key Research and Development Plan (No. 2016YFB-1000602), Science and Technology Development Plan Project of Shandong Province (No. 2016GGX101034), Shandong Province Independent Innovation Major Special Project (No. 2016ZDJS01A09), and Taishan Industrial Experts Programme of Shandong Province (Nos. tscy20150305 and tscy20160404).

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