A data mining approach for the identification of adverse drug events ADE resulting from drug drug interactions DDI to improve pharmacovigilance

Abstract

In the current study epidemiology study is done by means of newlineliterature survey in groups identified to be at higher potential for DDIs as newlinewell as in other cases to explore patterns of DDIs and the factors affecting newlinethem The structure of the FDA Adverse Event Reporting System FAERS newlinedatabase is studied and analyzed in detail to identify issues and challenges newlinein data mining the drug drug interactions The necessary pre processing newlinealgorithms are developed based on the analysis and the Apriori algorithm is newlinemodified to suit the process Finally the modules are integrated into a tool to identify DDIs The results are compared using standard drug interaction newlinedatabase for validation 31 Percent of the associations obtained were identified to newlinebe new and the match with existing interactions was 69 percent This match newlineclearly indicates the validity of the methodology and its applicability to newlinesimilar databases Formulation of the results using the generic names newlineexpanded the relevance of the results to a global scale The global newlineapplicability helps the health care professionals worldwide to observe newlinecaution during various stages of drug administration thus considerably newlineenhancing pharmacovigilance newline