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Integrating Association Rules Mined from Health-Care Data with Ontological Information for Automated Knowledge Generation

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Advances in Computational Intelligence Systems (UKCI 2017)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 650))

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Abstract

Association rule mining can be combined with complex network theory to automatically create a knowledge base that reveals how certain drugs cause side-effects on patients when they interact with other drugs taken by the patient when they have two or more diseases. The drugs will interact with on-target and off-target proteins often in an unpredictable way. A computational approach is necessary to be able to unravel the complex relationships between disease comorbidities. We built statistical models from the publicly available FAERS dataset to reveal interesting and potentially harmful drug combinations based on side-effects and relationships between co-morbid diseases. This information is very useful to medical practitioners to tailor patient prescriptions for optimal therapy.

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Author information

Authors and Affiliations

  1. School of Pharmacy and Pharmaceutical Sciences, Facuty of Health Sciences and Wellbeing, University of Sunderland, City Campus, Sunderland, UK

    John Heritage & Ken McGarry

  2. Faculty of Computing, University of Sunderland, St Peters Campus, Sunderland, UK

    Sharon McDonald

Authors
  1. John Heritage
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  2. Sharon McDonald
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  3. Ken McGarry
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Corresponding author

Correspondence to Ken McGarry .

Editor information

Editors and Affiliations

  1. Xiamen University, Xiamen Shi, Fujian, China

    Fei Chao

  2. Cardiff University, Cardiff, United Kingdom

    Steven Schockaert

  3. Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong

    Qingfu Zhang

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Heritage, J., McDonald, S., McGarry, K. (2018). Integrating Association Rules Mined from Health-Care Data with Ontological Information for Automated Knowledge Generation. In: Chao, F., Schockaert, S., Zhang, Q. (eds) Advances in Computational Intelligence Systems. UKCI 2017. Advances in Intelligent Systems and Computing, vol 650. Springer, Cham. https://doi.org/10.1007/978-3-319-66939-7_1

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  • DOI: https://doi.org/10.1007/978-3-319-66939-7_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-66938-0

  • Online ISBN: 978-3-319-66939-7

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