On knowledge discovery in open medical data on the example of the FDA drug adverse event reporting system for alendronate (Fosamax)

Abstract

In this paper, we present a study to discover hidden patterns in the reports of the public release of the Food and Drug Administration (FDA)'s Adverse Event Reporting System (AERS) for alendronate (fosamax) drug. Alendronate (fosamax) is a widely used medication for the treatment of osteoporosis disease. Osteoporosis is recognised as an important public health problem because of the significant morbidity, mortality and costs of treatment. We consider the importance of alendronate (fosamax) for medical research and explore the relationship between patient demographics information, the adverse event outcomes and drug's adverse events. We analyze the FDA's AERS which cover the period from the third quarter of 2005 through the second quarter of 2012 and create a dataset for association analysis. Both Apriori and Predictive Apriori algorithms are used for implementation which generates rules and the results are interpreted and evaluated. According to the results, some interesting rules and associations are obtained from the dataset. We believe that our results can be useful for medical researchers and decision making at pharmaceutical companies. © 2013 Springer-Verlag.