title: Mining for adverse drug events with formal concept analysis.
abstract: The pharmacovigilance databases consist of several case reports involving drugs and adverse events (AEs). Some methods are applied consistently to highlight all signals, i.e. all statistically significant associations between a drug and an AE. These methods are appropriate for verification of more complex relationships involving one or several drug(s) and AE(s) (e.g; syndromes or interactions) but do not address the identification of them. We propose a method for the extraction of these relationships based on Formal Concept Analysis (FCA) associated with disproportionality measures. This method identifies all sets of drugs and AEs which are potential signals, syndromes or interactions. Compared to a previous experience of disproportionality analysis without FCA, the addition of FCA was more efficient for identifying false positives related to concomitant drugs.
authors.affiliation: LORIA, Campus Scientifique, BP 239, 54506 Vandoeuvre, Nancy Cedex, France.
authors.forename: Alexander
authors.initials: A
authors.lastname: Estacio-Moreno
authors.forename: Yannick
authors.initials: Y
authors.lastname: Toussaint
authors.forename: Cédric
authors.initials: C
authors.lastname: Bousquet
mesh_terms: D016907:Adverse Drug Reaction Reporting Systems; D000465:Algorithms; D001185:Artificial Intelligence; D004347:Drug Interactions; D006801:Humans; D016247:Information Storage and Retrieval; D011358:Product Surveillance, Postmarketing