Journal Home > Volume 2 , issue 1

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.


menu
Abstract
Full text
Outline
About this article

Heterogeneous Network-Based Chronic Disease Progression Mining

Show Author's information Chenfei SunQingzhong Li( )Lizhen CuiHui LiYuliang 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
Received: 26 October 2017 Revised: 26 April 2018 Accepted: 03 May 2018 Published: 15 October 2018 Issue date: March 2019
References(21)
[1]
S. S. Waghade and A. M. Karandikar, A comprehensive study of healthcare fraud detection based on machine learning, Int. J. Appl. Eng. Res., vol. 13, no. 6, pp. 4175-4178, 2018.
[2]
H. Joudaki, A. Rashidian, B. Minaei-Bidgoli, M. Mahmoodi, B. Geraili, M. Nasiri, and M. Arab, Using data mining to detect health care fraud and abuse: A review of literature, Glob. J. Health Sci., vol. 7, no. 1, pp. 194-202, 2015.
[3]
R. A. Bauder and T. M. Khoshgoftaar, A novel method for fraudulent Medicare claims detection from expected payment deviations (application paper), in Proc. 17th Int. Conf. Information Reuse and Integration (IRI), Pittsburgh, PA, USA, 2016, pp. 11-19.
[4]
H. Joudaki, A. Rashidian, B. Minaei-Bidgoli, M. Mahmoodi, B. Geraili, M. Nasiri, and M. Arab, Improving fraud and abuse detection in general physician claims: A data mining study, Int. J. Health Policy Manag., vol. 5, no. 3, pp. 165-172, 2016.
[5]
J. S. Ko, H. Chalfin, B. J. Trock, Z. Y. Feng, E. Humphreys, S. W. Park, H. B. Carter, K. D. Frick, and M. Han, Variability in Medicare utilization and payment among urologists, Urology, vol. 85, no. 5, pp. 1045-1051, 2015.
[6]
R. A. Bauder, T. M. Khoshgoftaar, A. Richter, and M. Herland, Predicting medical provider specialties to detect anomalous insurance claims, in Proc. 28th Int. Conf. Tools with Artificial Intelligence (ICTAI), San Jose, CA, USA, 2016, pp. 784-790.
[7]
M. E. Charlson, P. Pompei, K. L. Ales, and C. R. MacKenzie, A new method of classifying prognostic comorbidity in longitudinal studies: Development and validation, J. Chron. Dis., vol. 40, no. 5, pp. 373-383, 1987.
[8]
A. Elixhauser, C. Steiner, D. R. Harris, and R. M. Coffey, Comorbidity measures for use with administrative data, Med. Care, vol. 36, no. 1, pp. 8-27, 1998.
[9]
M. T. A. Sharabiani, P. Aylin, and A. Bottle, Systematic review of comorbidity indices for administrative data, Med. Care, vol. 50, no. 12, pp. 1109-1118, 2012.
[10]
D. T. Wong and W. A. Knaus, Predicting outcome in critical care: The current status of the APACHE prognostic scoring system, Can. J. Anaesth., vol. 38, no. 3, pp. 374-383, 1991.
[11]
M. J. Breslow and O. Badawi, Severity scoring in the critically ill: Part 1—Interpretation and accuracy of outcome prediction scoring systems, Chest, vol. 141, no. 1, pp. 245-252, 2012.
[12]
M. Baglioni, S. Pieroni, F. Geraci, F. Mariani, S. Molinaro, M. Pellegrini, and E. Lastres, A new framework for distilling higher quality information from health data via social network analysis, in Proc. 13th Int. Conf. Data Mining Workshops, Dallas, TX, USA, 2013, pp. 48-55.
[13]
J. G. Anderson, Evaluation in health informatics: Social network analysis, Comput. Biol. Med., vol. 32, no. 3, pp. 179-193, 2002.
[14]
S. Uddin, A. Khan, and M. Piraveenan, Administrative claim data to learn about effective healthcare collaboration and coordination through social network, in Proc. 48th Hawaii Int. Conf. System Sciences, Kauai, HI, USA, 2015, pp. 3105-3114.
[15]
S. Uddin, A. Khan, and L. A. Baur, A framework to explore the knowledge structure of multidisciplinary research fields, PLoS One, vol. 10, no. 4, p. e0123537, 2015.
[16]
H. Luijks, T. Schermer, H. Bor, C. Van Weel, T. Lagro-Janssen, M. Biermans, and W. De Grauw, Prevalence and incidence density rates of chronic comorbidity in type 2 diabetes patients: An exploratory cohort study, BMC Med., vol. 10, p. 128, 2012.
[17]
D. Chambers, P. Wilson, C. Thompson, and M. Harden, Social network analysis in healthcare settings: A systematic scoping review, PLoS One, vol. 7, no. 8, p. e41911, 2012.
[18]
X. F. Yan and J. W. Han, gSpan: Graph-based substructure pattern mining, in Proc. 2002 IEEE Int. Conf. Data Mining, Maebashi, Japan, 2002, pp. 721-724.
[19]
M. Rosvall and C. T. Bergstrom, Maps of random walks on complex networks reveal community structure, Proc. Natl. Acad. Sci. USA, vol. 105, no. 4, pp. 1118-1123, 2008.
[20]
X. Y. Li, H. H. Cao, E. H. Chen, H. Xiong, and J. L. Tian, BP-growth: Searching strategies for efficient behavior pattern mining, in Proc. 13th Int. Conf. Mobile Data Management, Bengaluru, India, 2012, pp. 238-247.
[21]
J. A. K. Suykens, Support vector machines: A nonlinear modelling and control perspective, Eur. J. Control, vol. 7, nos. 2&3, pp. 311-327, 2001.
Publication history
Copyright
Acknowledgements
Rights and permissions

Publication history

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

Copyright

© 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).

Rights and permissions

Reprints and Permission requests may be sought directly from editorial office.

Return