International audiencePatient 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
