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

  • John Heritage
  • Sharon McDonald
  • Ken McGarryEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 650)

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.

Keywords

Comorbidity Side-effect Association rules Support Confidence Pharmaco-epidemiology 

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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.School of Pharmacy and Pharmaceutical Sciences, Facuty of Health Sciences and WellbeingUniversity of SunderlandSunderlandUK
  2. 2.Faculty of ComputingUniversity of SunderlandSunderlandUK

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