Discovering associations between adverse drug events using pattern structures and ontologies

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. Because ADEs have complex manifestations, we...

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Bibliographic Details
Published in:Journal of biomedical semantics 2017-08, Vol.8 (1), p.29-29, Article 29
Main Authors: Personeni, Gabin, Bresso, Emmanuel, Devignes, Marie-Dominique, Dumontier, Michel, Smaïl-Tabbone, Malika, Coulet, Adrien
Format: Article
Language:English
Subjects:
Algorithms
Analgesics
Analysis
Anticoagulants
Artificial Intelligence
Bioinformatics
Biological Ontologies
Computer Science
Data mining
Datasets
Drug-Related Side Effects and Adverse Reactions
Drugs
Electronic Health Records
Electronic medical records
Genotype & phenotype
Humans
International conferences
Knowledge discovery
Life Sciences
Lupus
Medical records
Medication
Ontology
Patients
Pattern Recognition, Automated
Pharmaceutical sciences
Phenotype
Semantics
Subgroups
Ulcers
Urogenital system
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Summary: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. 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. 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.
ISSN:2041-1480
2041-1480
DOI:10.1186/s13326-017-0137-x