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Network Analysis of Human Disease Comorbidity Patterns Based on Large-Scale Data Mining

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Bioinformatics Research and Applications (ISBRA 2014)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 8492))

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Abstract

Disease comorbidity is an important aspect of phenotype associations and reflects overlapping pathogenesis between diseases. Existing comorbidity studies usually focused on specific diseases and patient populations. In this study, we systematically mined and analyzed disease comorbidity patterns without restricting disease types and patient populations. We presented a data mining approach and extracted comorbidity patterns from a patient-disease database in the drug adverse event reporting system. The database contains records of 3,354,043 patients. We first demonstrated that the data are not severely biased towards specific patient populations and valuable for comorbidity mining. Then we developed an automatic pipeline to process the data, and applied an association rule mining algorithm to mine comorbidity relationships among multiple diseases. Our approach extracted 8,576 comorbidity patterns for 613 diseases. We constructed a disease comorbidity network from these patterns and demonstrated that the comorbidity clusters reflect genetic associations between diseases. Different from previous studies based on relative risk, which tends to identify comorbidities for rare diseases, our approach extracted many patterns for common diseases. We applied the approach on colorectal cancer, and found interesting relationships between colorectal cancer and metabolic disorders, which may lead to promising pathogenesis discoveries.

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Author information

Authors and Affiliations

  1. Department of Electrical Engineering and Computer Science, School of Engineering, Case Western Reserve University, Cleveland, OH, 44106, USA

    Yang Chen

  2. Division of Medical Informatics, School of Medicine, Case Western Reserve University, Cleveland, OH, 44106, USA

    Yang Chen & Rong Xu

Authors
  1. Yang Chen
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  2. Rong Xu
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Editor information

Editors and Affiliations

  1. Johns Hopkins University, Computer Science Department, Baltimore, MD 21218, USA and National Science Foundation, 1115, CCF, USA

    Mitra Basu

  2. Department of Computer Science, Georgia State University, 30303, Atlanta, GA, USA

    Yi Pan

  3. School of Information Science and Engineering, Central South University, 410083, Changsha, China

    Jianxin Wang

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© 2014 Springer International Publishing Switzerland

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Chen, Y., Xu, R. (2014). Network Analysis of Human Disease Comorbidity Patterns Based on Large-Scale Data Mining. In: Basu, M., Pan, Y., Wang, J. (eds) Bioinformatics Research and Applications. ISBRA 2014. Lecture Notes in Computer Science(), vol 8492. Springer, Cham. https://doi.org/10.1007/978-3-319-08171-7_22

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  • DOI: https://doi.org/10.1007/978-3-319-08171-7_22

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-08170-0

  • Online ISBN: 978-3-319-08171-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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