The use of Bayesian confidence propagation neural network in pharmacovigilance

The WHO database contains more than 2.8 million case reports of suspected adverse drug reactions reported from 70 countries worldwide since 1968. The Uppsala Monitoring Centre maintains and analyses this database for new signals on behalf of the WHO Programme for International Drug Monitoring. A goal of the Programme is to detect signals, where a signal is defined as "Reported information on a possible causal relationship between an adverse event and a drug, the relationship being unknown or incompletely documented previously." The analysis of such a large amount of data on a case by case basis is impossible with the resources available. Therefore a quantitative, data mining procedure has been developed to improve the focus of the clinical signal detection process. The method used, is referred to as the BCPNN (Bayesian Confidence Propagation Neural Network). This not only assists in the early detection of adverse drug reactions (ADRs) but also further analysis of such signals. The method uses Bayesian statistical principles to quantify apparent dependencies in the data set. This quantifies the degree to which a specific drug- ADR combination is different from a background (in this case the WHO database). The measure of disproportionality used, is referred to as the Information Component (IC) because of its' origins in Information Theory. A confidence interval is calculated for the IC of each combination. A neural network approach allows all drug-ADR combinations in the database to be analysed in an automated manner. Evaluations of the effectiveness of the BCPNN in signal detection are described. To compare how a drug association compares in unexpectedness to related drugs, which might be used for the same clinical indication, the method is extended to consideration of groups of drugs. The benefits and limitations of this approach are discussed with examples of known group effects (ACE inhibitors - coughing and antihistamines - heart rate and rhythm disorders.) An example of a clinically important, novel signal found using the BCPNN approach is also presented. The signal of antipsychotics linked with heart muscle disorder was detected using the BCPNN and reported. The BCPNN is now routinely used in signal detection to search single drug - single ADR combinations. The extension of the BCPNN to discover 'unexpected' complex dependencies between groups of drugs and adverse reactions is described. A recurrent neural network method has been developed for finding complex patterns in incomplete and noisy data sets. The method is demonstrated on an artificial test set. Implementation on real data is demonstrated by examining the pattern of adverse reactions highlighted for the drug haloperidol. Clinically important, complex relationships in this kind of data are previously unexplored. The BCPNN method has been shown and tested for use in routine signal detection, refining signals and in finding complex patterns. The usefulness of the output is influenced by the quality of the data in the database. Therefore, this method should be used to detect, rather than evaluate signals. The need for clinical analyses of case series remains crucial