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Drug Safety

, Volume 25, Issue 6, pp 393–397 | Cite as

A Data Mining Approach for Signal Detection and Analysis

  • Andrew Bate
  • Marie Lindquist
  • I. Ralph. Edwards
  • Roland Orre
Short Communication

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.

Keywords

Anatomical Therapeutic Chemical Neural Network Architecture Report Drug Adverse Reaction Information Component Suspected Adverse Drug Reaction 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Adis International Limited 2002

Authors and Affiliations

  • Andrew Bate
    • 1
    • 2
  • Marie Lindquist
    • 1
  • I. Ralph. Edwards
    • 1
  • Roland Orre
    • 3
  1. 1.The Uppsala Monitoring Centre, WHO Collaborating Centre for International Drug MonitoringUppsalaSweden
  2. 2.Division of Clinical PharmacologyUmea UniversityUmeaSweden
  3. 3.Department of Mathematical StatisticsStockholm UniversityStockholmSweden

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