Clustering-Based Hybrid Approach for Identifying Quantitative Multidimensional Associations Between Patient Attributes, Drugs and Adverse Drug Reactions.

The activity of post-marketing surveillance results in a collection of large amount of data. The analysis of data is very useful for raising early warnings on possible adverse reactions of drugs. Association rule mining techniques have been heavily explored by the research community for identifying binary association between drugs and their adverse effects. But these techniques perform poorly and miss out several interesting associations when it comes to analysis of multidimensional data which may include multiple patient attributes, drugs and adverse drug reactions. In the present work, a clustering-based hybrid approach has been presented for finding quantitative multidimensional association from the large amount of data. Firstly, it employs clustering technique for segmentation of data into semantically coherent clusters. Furthermore, disproportionality method called proportional reporting ratio is applied on clustered data for generating statistically strong associations. The performance of the proposed methodology has been examined on the data taken from the U.S. Food and Drug Administration Adverse Event Reporting System database corresponding to Aspirin and nine other drugs which are prescribed along with Aspirin. The experimental results show that the proposed approach discovered a number of association rules which are very comprehensive and informative regarding relationship of patient traits and drugs with adverse drug reactions. On comparing experimental results with LPMiner, it is observed that the quantitative association rules discovered by LPMiner are just 8.3% of what have been discovered by the proposed methodology.