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Combining association rule mining and network analysis for pharmacosurveillance

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Abstract

Retailers routinely use association mining to investigate trends in the use of their products. In the medical world, association mining is mostly used to identify associations between symptoms and diseases, or between drugs and adverse events. In comparison, there is a relative paucity of work that focuses on relationships between drugs exclusively. In this work, we use the Medical expenditure panel survey to examine relationships between drugs in the United States. In addition to examining the rules generated by association mining, we introduce the notion of a target drug network and demonstrate via different drugs that it can offer additional medical insight. For example, we were able to find drugs that are commonly taken together despite containing the same active compound. Future work can expand on the concept of target drug network, for example, by annotating the networks with the compounds and intended uses of each drug, to yield additional insight for pharmacosurveillance as well as pharmaceutical companies.

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Notes

  1. Data files and codebooks can be downloaded from http://meps.ahrq.gov/mepsweb/data_stats/download_data_files.jsp. Statistical summaries of the data can be accessed via http://meps.ahrq.gov/mepsweb/data_stats/quick_tables_search.jsp?component=1&subcomponent=0 and selecting Year 2011.

  2. Up-to-date National Diabetes Statistics are maintained by the CDC at http://www.cdc.gov/diabetes/statistics/prev/national/figpersons.htm.

  3. Up-to-date National Asthma Prevalence is maintained by the CDC at http://www.cdc.gov/asthma/most_recent_data.htm.

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Acknowledgments

EB, IP, NHB, ABA and WH would like to thank the Department of Computer Science, Troy University for providing physical infrastructure. PJG is grateful to the Department of Computer Science, Northern Illinois University for research support. VM expresses his gratitude to the Department of Computer Science, Lakehead University for research support.

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Authors and Affiliations

  1. Department of Computer Science, Troy University, University Ave, Troy, AL, 36082, USA

    Eugene Belyi, Indravadan Patel, Naga Harish Balabhadrapathruni, Aymen Ben Abdallah & Wedyan Hameed

  2. Department of Computer Science, Lakehead University, 955 Oliver Road, Thunder Bay, ON, P7B 5E1, Canada

    Vijay K. Mago

  3. Department of Computer Science, Northern Illinois University, 1425 W. Lincoln Highway, DeKalb, IL, 60115, USA

    Philippe J. Giabbanelli

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  1. Eugene Belyi
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  2. Philippe J. Giabbanelli
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Correspondence to Vijay K. Mago.

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There are no competing interests. This study is based on public access data provided by medical expenditure panel survey. The interpretation and conclusions of the results are those of the researchers only.

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Belyi, E., Giabbanelli, P.J., Patel, I. et al. Combining association rule mining and network analysis for pharmacosurveillance. J Supercomput 72, 2014–2034 (2016). https://doi.org/10.1007/s11227-016-1714-y

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