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Bayesian Confidence Propagation Neural Network

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Abstract

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.

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References

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Acknowledgements

No sources of funding were used to assist in the preparation of this paper. The author has no conflicts of interest that are directly relevant to the content of this paper.

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Authors and Affiliations

  1. WHO Collaborating Centre for International Drug Monitoring, Uppsala Monitoring Centre (UMC), Stora Torget 3, Uppsala, S-753 20, Sweden

    Andrew Bate

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  1. Andrew Bate
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Correspondence to Andrew Bate.

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Bate, A. Bayesian Confidence Propagation Neural Network. Drug-Safety 30, 623–625 (2007). https://doi.org/10.2165/00002018-200730070-00011

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  • DOI: https://doi.org/10.2165/00002018-200730070-00011

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