A Bayesian neural network method for adverse drug reaction signal generation¶
Bate, A., Lindquist, M., Edwards, I. R., Olsson, S., Orre, R., Lansner, A., & De Freitas, R. M. (1998). A Bayesian neural network method for adverse drug reaction signal generation. European Journal of Clinical Pharmacology.
Cited by¶
1 citation across 1 artifact.
Each citation links to the sentence it supports in the citing article.
Domain-specific¶
- Adverse Drug Event
- Pharmacovigilance infrastructure — the FDA's FAERS spontaneous reporting system, the WHO's Uppsala Monitoring Centre, signal-detection algorithms such as proportional reporting ratios and Bayesian confidence propagation neural networks — operates on this unified framework to identify drug–event pairs occurring at higher-than-background frequency in the population, triggering label revisions, contraindication additions, and black-box warnings
This sourceThe BCPNN method as run at the WHO Uppsala Monitoring Centre on the international spontaneous-report database, surfacing drug-event pairs reported above the expected background; the regulatory actions that may follow a signal are outside its scope.
Supported in partVerified against the source
- Pharmacovigilance infrastructure — the FDA's FAERS spontaneous reporting system, the WHO's Uppsala Monitoring Centre, signal-detection algorithms such as proportional reporting ratios and Bayesian confidence propagation neural networks — operates on this unified framework to identify drug–event pairs occurring at higher-than-background frequency in the population, triggering label revisions, contraindication additions, and black-box warnings
Verification¶
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