Mining for adverse drug events with formal concept analysis
  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.
