Comparing Data-mining Algorithms Developed for Longitudinal Observational Databases
This work addresses the problem of post-marketing drug surveillance for healthcare researchers, but it is incremental as it tests existing methods without introducing new ones.
The study evaluated four existing data-mining algorithms on longitudinal observational databases to detect adverse drug reactions, finding that none consistently identified known side effects or outperformed others.
Longitudinal observational databases have become a recent interest in the post marketing drug surveillance community due to their ability of presenting a new perspective for detecting negative side effects. Algorithms mining longitudinal observation databases are not restricted by many of the limitations associated with the more conventional methods that have been developed for spontaneous reporting system databases. In this paper we investigate the robustness of four recently developed algorithms that mine longitudinal observational databases by applying them to The Health Improvement Network (THIN) for six drugs with well document known negative side effects. Our results show that none of the existing algorithms was able to consistently identify known adverse drug reactions above events related to the cause of the drug and no algorithm was superior.