Heterogeneous network-based chronic disease progression mining

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.

[1]  Tjard Schermer,et al.  Prevalence and incidence density rates of chronic comorbidity in type 2 diabetes patients: an exploratory cohort study , 2012, BMC Medicine.

[2]  Arif Khan,et al.  A Framework to Explore the Knowledge Structure of Multidisciplinary Research Fields , 2015, PloS one.

[3]  B. Minaei-Bidgoli,et al.  Using Data Mining to Detect Health Care Fraud and Abuse: A Review of Literature , 2014, Global journal of health science.

[4]  Omar Badawi,et al.  Severity scoring in the critically ill: part 1--interpretation and accuracy of outcome prediction scoring systems. , 2012, Chest.

[5]  Taghi M. Khoshgoftaar,et al.  Predicting Medical Provider Specialties to Detect Anomalous Insurance Claims , 2016, 2016 IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI).

[6]  Behrouz Minaei-Bidgoli,et al.  Improving Fraud and Abuse Detection in General Physician Claims: A Data Mining Study , 2015, International journal of health policy and management.

[7]  Shivani S. Waghade.  A Comprehensive Study of Healthcare Fraud Detection based on Machine Learning , 2018 .

[8]  Arif Khan,et al.  Administrative Claim Data to Learn about Effective Healthcare Collaboration and Coordination through Social Network , 2015, 2015 48th Hawaii International Conference on System Sciences.

[9]  Misop Han,et al.  Variability in Medicare utilization and payment among urologists. , 2015, Urology.

[10]  A. Bottle,et al.  Systematic Review of Comorbidity Indices for Administrative Data , 2012, Medical care.

[11]  Johan A. K. Suykens,et al.  Support Vector Machines: A Nonlinear Modelling and Control Perspective , 2001, Eur. J. Control.

[12]  Duncan Chambers,et al.  Social Network Analysis in Healthcare Settings: A Systematic Scoping Review , 2012, PloS one.

[13]  Hui Xiong,et al.  BP-growth: Searching Strategies for Efficient Behavior Pattern Mining , 2012, 2012 IEEE 13th International Conference on Mobile Data Management.

[14]  C. Steiner,et al.  Comorbidity measures for use with administrative data. , 1998, Medical care.

[15]  James G. Anderson,et al.  Evaluation in health informatics: social network analysis , 2002, Comput. Biol. Medicine.

[16]  Miriam Baglioni,et al.  A New Framework for Distilling Higher Quality Information from Health Data via Social Network Analysis , 2013, 2013 IEEE 13th International Conference on Data Mining Workshops.

[17]  Jiawei Han,et al.  gSpan: graph-based substructure pattern mining , 2002, 2002 IEEE International Conference on Data Mining, 2002. Proceedings..

[18]  Martin Rosvall,et al.  Maps of random walks on complex networks reveal community structure , 2007, Proceedings of the National Academy of Sciences.

[19]  W. Knaus,et al.  Predicting outcome in critical care: the current status of the APACHE prognostic scoring system , 1991, Canadian journal of anaesthesia = Journal canadien d'anesthesie.

[20]  Taghi M. Khoshgoftaar,et al.  A Novel Method for Fraudulent Medicare Claims Detection from Expected Payment Deviations (Application Paper) , 2016, 2016 IEEE 17th International Conference on Information Reuse and Integration (IRI).

[21]  C. Mackenzie,et al.  A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. , 1987, Journal of chronic diseases.