# Mining For Adverse Drug Events With Formal Concept Analysis

Alexander ESTACIO-MORENOa,1, Yannick TOUSSAINTa, Cédric BOUSQUETb, c aLORIA, Campus Scientifique. BP 239, 54506 Vandoeuvre. Nancy Cedex, France b INSERM UMR_S 872, Eq 20, Faculté de Médecine - Paris 5, France c University of Saint Etienne, Department of Public Health and Medical Informatics, St-Etienne, France. 

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.

Keywords: Adverse Effects, Formal Concept Analysis, Data analysis-extraction tools, Algorithms, Pharmacy.

## 1. Introduction

Detecting unexpected relationships between drugs and adverse events (AEs) from a pharmacovigilance database is a real challenge. Automated signal detection consists in application of data mining algorithms to help identification of signals, i.e. all statistically significant {drug, AE} couples, which are numerous and should be reduced to the most plausible [1]. Indeed, these potential signals extracted from a database are only assumptions; they should lead to more complex and expensive pharmacological studies. Therefore, it's important not to ignore any relationships but avoid an excess of studying situations.

Several types of relationships can be identified from the database: (i) {drug, AE}
couples that data mining algorithms have mainly focused on; (ii) Relationships between, two drugs and an AE which are potential drug interactions, (iii) Relationships between several AEs and a drug which are potential syndromes, (iv) Relationships involving several drug(s) and AE(s) which are frequently observed in protocols. Among relationships between several drugs, cases where the drug association strengthens a signal compared to both single drugs may be classified as a potential interaction. Cases where there is no signal between one of the drugs and the AE are more difficult to 1 Corresponding Author