Comparing data-mining algorithms developed for longitudinal observational databases

Reps, Jenna; Garibaldi, Jonathan M.; Aickelin, Uwe; Soria, Daniele; Gibson, Jack E.; Hubbard, Richard B.

Abstract

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

Citation

Reps, J., Garibaldi, J. M., Aickelin, U., Soria, D., Gibson, J. E., & Hubbard, R. B. Comparing data-mining algorithms developed for longitudinal observational databases.

• Conference Name: UKCI 2012, the 12th Annual Workshop on Computational Intelligence
• End Date: Sep 7, 2012
• Deposit Date: Jun 17, 2013
• Peer Reviewed: Peer Reviewed
• Public URL: https://nottingham-repository.worktribe.com/output/1009167
• Publisher URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6335771
• Additional Information: Published in: 2012 12th UK Workshop on Computational Intelligence (UKCI), Heriot-Watt University, Edinburgh, UK; 5-7 September 2012, P. De Wilde, G.M. Coghill, A.V. Kononova (eds.), IEEE, 2012, doi: 10.1109/UKCI.2012.6335771. Copyright IEEE.