Integrating association rules mined from health-care data with ontological information for automated knowledge generation

Heritage, John, McDonald, Sharon and McGarry, Kenneth (2017) Integrating association rules mined from health-care data with ontological information for automated knowledge generation. In: The 17th Annual UK Workshop on Computational Intelligence (UKCI-2017), 6-8 Sep 2017, Cardiff.

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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 sideeffects
and relationships between co-morbid diseases. This information is very useful to medical practitioners to tailor patient prescriptions for optimal therapy.

Item Type: Conference or Workshop Item (Paper)
Subjects: Computing > Artificial Intelligence
Sciences > Chemistry
Divisions: Faculty of Technology
Faculty of Technology > FOT Executive
Faculty of Technology > School of Computer Science
Depositing User: Kenneth McGarry
Date Deposited: 12 Jul 2017 09:17
Last Modified: 17 Apr 2020 18:14
URI: http://sure.sunderland.ac.uk/id/eprint/7534
ORCID for Kenneth McGarry: ORCID iD orcid.org/0000-0002-9329-9835

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