Investigating the detection of adverse drug events in a UK general practice electronic health-care database

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

Abstract

Data-mining techniques have frequently been developed
for Spontaneous reporting databases. These techniques
aim to find adverse drug events accurately and efficiently. Spontaneous reporting databases are prone to missing information,under reporting and incorrect entries. This often results in a detection lag or prevents the detection of some adverse drug events. These limitations do not occur in electronic healthcare databases. In this paper, existing methods developed for spontaneous reporting databases are implemented on both a
spontaneous reporting database and a general practice electronic health-care database and compared. The results suggests that the application of existing methods to the general practice database may help find signals that have gone undetected when using the spontaneous reporting system database. In addition the general practice database provides far more supplementary information, that if incorporated in analysis could provide a wealth of information for identifying adverse events more
accurately.

Citation

Reps, J., Feyereisl, J., Garibaldi, J. M., Aickelin, U., Gibson, J. E., & Hubbard, R. B. Investigating the detection of adverse drug events in a UK general practice electronic health-care database.

• Conference Name: UKCI 2011, 11th Annual Workshop on Computational Intelligence
• End Date: Sep 9, 2011
• Deposit Date: Jun 18, 2013
• Peer Reviewed: Peer Reviewed
• Public URL: https://nottingham-repository.worktribe.com/output/1011292
• Publisher URL: http://ukci.cs.manchester.ac.uk/files/Proceedings.pdf
• Additional Information: Published in: Proceedings of the 11th UK Workshop on; Computational Intelligence. Manchester : School of Computer Science, University of Manchester, 2011. http://ukci.cs.manchester.ac.uk/files/Proceedings.pdf