Discovering associations between adverse drug events using pattern structures and ontologies View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2017-12

AUTHORS

Gabin Personeni, Emmanuel Bresso, Marie-Dominique Devignes, Michel Dumontier, Malika Smaïl-Tabbone, Adrien Coulet

ABSTRACT

BACKGROUND: Patient data, such as electronic health records or adverse event reporting systems, constitute an essential resource for studying Adverse Drug Events (ADEs). We explore an original approach to identify frequently associated ADEs in subgroups of patients. RESULTS: Because ADEs have complex manifestations, we use formal concept analysis and its pattern structures, a mathematical framework that allows generalization using domain knowledge formalized in medical ontologies. Results obtained with three different settings and two different datasets show that this approach is flexible and allows extraction of association rules at various levels of generalization. CONCLUSIONS: The chosen approach permits an expressive representation of a patient ADEs. Extracted association rules point to distinct ADEs that occur in a same group of patients, and could serve as a basis for a recommandation system. The proposed representation is flexible and can be extended to make use of additional ontologies and various patient records. More... »

PAGES

29

References to SciGraph publications

  • 2013-06. Pharmacovigilance Using Clinical Notes in CLINICAL PHARMACOLOGY & THERAPEUTICS
  • 2015. Pattern Structures and Concept Lattices for Data Mining and Knowledge Processing in MACHINE LEARNING AND KNOWLEDGE DISCOVERY IN DATABASES
  • 2005. Efficient Mining of Association Rules Based on Formal Concept Analysis in FORMAL CONCEPT ANALYSIS
  • 2001-07-12. Pattern Structures and Their Projections in CONCEPTUAL STRUCTURES: BROADENING THE BASE
  • 2016-05-10. A curated and standardized adverse drug event resource to accelerate drug safety research in SCIENTIFIC DATA
  • 2013-06. Performance of Pharmacovigilance Signal‐Detection Algorithms for the FDA Adverse Event Reporting System in CLINICAL PHARMACOLOGY & THERAPEUTICS
  • 2015-12. Exploring adverse drug events at the class level in JOURNAL OF BIOMEDICAL SEMANTICS
  • 2010. Adverse Drug Reaction Mining in Pharmacovigilance Data Using Formal Concept Analysis in MACHINE LEARNING AND KNOWLEDGE DISCOVERY IN DATABASES
  • 2013. Using Pattern Structures for Analyzing Ontology-Based Annotations of Biomedical Data in FORMAL CONCEPT ANALYSIS
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1186/s13326-017-0137-x

    DOI

    http://dx.doi.org/10.1186/s13326-017-0137-x

    DIMENSIONS

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    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/28830518


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