Data Mining in Spontaneous Reports

Publisher Website
Google Scholar
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.