Discovering associations between adverse drug events using pattern structures and ontologies

Gabin Personeni 1, 2 Emmanuel Bresso 1 Marie-Dominique Devignes 2 Michel Dumontier 3, 4 Malika Smaïl-Tabbone 1 Adrien Coulet 1, 3
1 ORPAILLEUR - Knowledge representation, reasonning
Inria Nancy - Grand Est, LORIA - NLPKD - Department of Natural Language Processing & Knowledge Discovery
2 CAPSID - Computational Algorithms for Protein Structures and Interactions
Inria Nancy - Grand Est, LORIA - AIS - Department of Complex Systems, Artificial Intelligence & Robotics
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.
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Gabin Personeni, Emmanuel Bresso, Marie-Dominique Devignes, Michel Dumontier, Malika Smaïl-Tabbone, et al.. Discovering associations between adverse drug events using pattern structures and ontologies. Journal of Biomedical Semantics, BioMed Central, 2017, 93, pp.539 - 546. ⟨10.1186/s13326-017-0137-x⟩. ⟨hal-01576341⟩

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