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Abstract #19272 Published in IGR 9-3
Bayesian confidence propagation neural network
Bate ADrug Safety 2007; 30: 623-625
A Bayesian confidence propagation neural network (BCPNN)-based technique has been in routine use for data mining the 3 million suspected adverse drug reactions (ADRs) in the WHO database of suspected ADRs of as part of the signal-detection process since 1998. Data mining is used to enhance the early detection of previously unknown possible drug-ADR relationships, by highlighting combinations that stand out quantitatively for clinical review. Now-established signals prospectively detected from routine data mining include topiramate associated glaucoma, and the SSRIs with neonatal withdrawal syndrome. Recent advances in the method and its use will be discussed: (i) the recurrent neural network approach used to analyse cyclo-oxygenase 2 inhibitor data, isolating patterns for both rofecoxib and celecoxib; (ii) the use of data-mining methods to improve data quality, especially the detection of duplicate reports; and (iii) the application of BCPNN to the 2 million patient-record IMS Disease Analyzer.
Dr. A. Bate, Collaborating Centre for International Drug Monitoring, Uppsala Monitoring Centre (UMC), Stora Torget 3, Uppsala, S-753 20, Sweden. andrew.bate@who-umc.org
Classification:
9.4.20 Other (Part of: 9 Clinical forms of glaucomas > 9.4 Glaucomas associated with other ocular and systemic disorders)
