title: Data mining in spontaneous reports.
abstract: The increasing size of spontaneous report data sets and the increasing capability for screening such data due to increases in computational power has led to a recent increase in interest and use of data mining on such data. While data mining plays an important role in the analysis of spontaneous reports, there is general debate on how and when data mining should be best performed. While the cornerstone principles for data mining of spontaneous reports have been in place since the 1960s, several significant changes have occurred to make their use widespread. Superficially the Bayesian methods seem unnecessarily complex, particularly given the nature of the data, but in practice implementation in Bayesian framework gives clear benefits. There are difficulties evaluating the performance of the methods, but they work and save resources in managing large data sets. The use of neural networks allows more sophisticated pattern recognition to be performed.
authors.affiliation: The Uppsala Monitoring Centre, WHO Collaborating Centre for International Drug Monitoring, Sweden. andrew.bate@whoumc.org
authors.forename: Andrew
authors.initials: A
authors.lastname: Bate
authors.forename: I R
authors.initials: IR
authors.lastname: Edwards
mesh_terms: D016907:Adverse Drug Reaction Reporting Systems; D000465:Algorithms; D001499:Bayes Theorem; D016000:Cluster Analysis; D003625:Data Collection; D003627:Data Interpretation, Statistical; D064420:Drug-Related Side Effects and Adverse Reactions; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated