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Reps, Jenna, Garibaldi, Jonathan M., Aickelin, Uwe, Soria, Daniele, Gibson, Jack E. and Hubbard, Richard B. (2012) Comparing data-mining algorithms developed for longitudinal observational databases. In: UKCI 2012, the 12th Annual Workshop on Computational Intelligence, 5-7 Sept 2012, Edinburgh, Scotland.

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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

Item Type:	Conference or Workshop Item (Paper)
RIS ID:	https://nottingham-repository.worktribe.com/output/1009167
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.
Schools/Departments:	University of Nottingham, UK > Faculty of Science > School of Computer Science
Depositing User:	Aickelin, Professor Uwe
Date Deposited:	17 Jun 2013 13:44
Last Modified:	04 May 2020 20:22
URI:	https://eprints.nottingham.ac.uk/id/eprint/2036

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