Discovering ADE associations from EHRs using pattern structures and ontologies

Gabin Personeni 1, 2 Marie-Dominique Devignes 1, 2 Michel Dumontier 3 Malika Smaïl-Tabbone 1 Adrien Coulet 1
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 : Patient Electronic Health Records (EHRs) 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, while taking into account domain knowledge formalized in medical ontologies. Results obtained with three different settings show that this approach is flexible and allows extraction of association rules at various levels of generalization.
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Contributor : Adrien Coulet <>
Submitted on : Wednesday, September 21, 2016 - 9:37:52 AM
Last modification on : Tuesday, December 18, 2018 - 4:40:22 PM
Long-term archiving on : Thursday, December 22, 2016 - 12:41:35 PM

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Gabin Personeni, Marie-Dominique Devignes, Michel Dumontier, Malika Smaïl-Tabbone, Adrien Coulet. Discovering ADE associations from EHRs using pattern structures and ontologies. Phenotype Day, Bio-Ontologies SIG, ISMB, Jul 2016, Orlando, United States. ⟨hal-01369448⟩

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