A multi-relational association mining algorithm for screening suspected adverse drug reactions

Existing association mining algorithms generally assume that the data is in a single table (relation). One approach to mining multi-relational data tables is to convert the data into a single table and then apply the existing algorithms. However, the converted table may be too large to fit into memory. Moreover, these algorithms often need structures to store large intermediate data, which further restricts them by available memory. In this study, we developed an efficient SQL-based algorithm that directly dealt with multi-relational data tables that need less allocated memory. We also investigated how database indexes and the number of connections affect the performance of such an algorithm. The proposed algorithm was tested using data from the FDA's (Food and Drug Administration) spontaneous reporting system. The data collected was used for detecting potential adverse drug reactions (ADRs) which represent a serious worldwide problem. Our experiment results indicate that the algorithm performs well and is scalable in terms of the number of association rules that are evaluated and the size of the data.