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Open Access

Heterogeneous Network-Based Chronic Disease Progression Mining

Research Center of Software and Data Engineering, Shandong University, Jinan 250101, China.
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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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Big Data Mining and Analytics
Pages 25-34
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.








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Received: 26 October 2017
Revised: 26 April 2018
Accepted: 03 May 2018
Published: 15 October 2018
© The author(s) 2019