A Bayesian neural network method for adverse drug reaction signal generation.
The database of adverse drug reactions (ADRs) held by the Uppsala Monitoring Centre on behalf of the 47 countries of the World Health Organization (WHO) Collaborating Programme for International Drug Monitoring contains nearly two million reports.
It is the largest database of this sort in the world, and about 35 000 new reports are added quarterly.
The task of trying to find new drug-ADR signals has been carried out by an expert panel, but with such a large volume of material the task is daunting.
We have developed a flexible, automated procedure to find new signals with known probability difference from the background data.
Data mining, using various computational approaches. has been applied in a variety of disciplines.
A Bayesian confidence propagation neural network (BCPNN) has been developed which can manage large data sets, is robust in handling incomplete data, and may be used with complex variables.
Using information theory. such a tool is ideal for finding drug ADR combinations with other variables, which are highly associated compared to the generality of the stored data. or a section of the stored data.
The method is transparent for easy checking and flexible for different kinds of search. (...)
Mots-clés Pascal : Toxicité, Médicament, Base donnée, Méthode étude, Détection Bayes, Réseau neuronal, Modèle statistique, OMS
Mots-clés Pascal anglais : Toxicity, Drug, Database, Investigation method, Bayes detection, Neural network, Statistical model, WHO
Notice produite par :
Inist-CNRS - Institut de l'Information Scientifique et Technique
Cote : 98-0402667
Code Inist : 002B02A06. Création : 25/01/1999.