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01-05-2002 | Short Communication

A Data Mining Approach for Signal Detection and Analysis

Authors: Andrew Bate, Marie Lindquist, I. Ralph. Edwards, Roland Orre

Published in: Drug Safety | Issue 6/2002

Abstract

The WHO database contains over 2.5 million case reports, analysis of this data set is performed with the intention of signal detection. This paper presents an overview of the quantitative method used to highlight dependencies in this data set.

The method Bayesian confidence propagation neural network (BCPNN) is used to highlight dependencies in the data set. The method uses Bayesian statistics implemented in a neural network architecture to analyse all reported drug adverse reaction combinations.

This method is now in routine use for drug adverse reaction signal detection. Also this approach has been extended to highlight drug group effects and look for higher order dependencies in the WHO data.

Quantitatively unexpectedly strong relationships in the data are highlighted relative to general reporting of suspected adverse effects; these associations are then clinically assessed.

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Title: A Data Mining Approach for Signal Detection and Analysis
Authors: Andrew Bate
Marie Lindquist
I. Ralph. Edwards
Roland Orre
Publication date: 01-05-2002
Publisher: Springer International Publishing
Published in: Drug Safety / Issue 6/2002
Print ISSN: 0114-5916
Electronic ISSN: 1179-1942
DOI: https://doi.org/10.2165/00002018-200225060-00002

At a glance: The STEP trials

Springer Medicine

A Data Mining Approach for Signal Detection and Analysis

Abstract

At a glance: The STEP trials

Springer Medicine

Abstract

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