nach oben

Drug Safety

Erschienen in:

01.05.2002 | Short Communication

A Data Mining Approach for Signal Detection and Analysis

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

Erschienen in: Drug Safety | Ausgabe 6/2002

Einloggen, um Zugang zu erhalten

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.

Edwards IR, Biriell C. Harmonisation in pharmacovigilance. Drug Saf 1994; 10(2): 93–102PubMedCrossRef

Olsson S. The role of the WHO programme on international drug monitoring in coordinating worldwide drug safety efforts. Drug Saf 1998; 19(1): 1–10PubMedCrossRef

Bate A, Orre R, Lindquist M, et al. Explanation of data mining methods [online]. Available from URL: http://www.bmj.com/cgi/content/full/322/7296/1207/DC1 [Accessed 2001 Oct 15]

Bate A, Orre R, Lindquist M, et al. Pattern recognition using a recurrent neural network and its application to the WHO database [abstract]. Pharmacoepidemiol Drug Saf 2001; 10(S1): S163

Orre R, Lansner A, Bate A, et al. Bayesian neural networks with confidence estimations applied to data mining. Computational Statistics Data Anal 2000; 34(8): 473–93CrossRef

Lindquist M, Edwards IR, Bate A, et al. From association to alert: a revised approach to international signal analysis. Pharmacoepidemiol Drug Saf 1999; 8: S15–25PubMedCrossRef

Bate A, Lindquist M, Edwards IR, et al. A Bayesian neural network method for adverse drug reaction signal generation. Eur J Clin Pharmacol 1998; 54: 315–21PubMedCrossRef

Lindquist M, Stahl M, Bate A, et al. A retrospective evaluation of a data mining approach to aid finding new adverse drug reaction signals in the WHO international database. Drug Saf 2000; 23(6): 533–42PubMedCrossRef

Bate A, Lindquist M, Orre R, et al. Automated classification of signals as group effects or drug specific on the WHO database [abstract]. 8th Annual Meeting European Society of Pharmacovigilance; 2000 Sep; Verona; Elsevier, 2000

10.

Guidelines for ATC classification and DDD assignment. 3rd ed. Oslo: WHO Collaborating Centre for Drug Statistics Methodology, 2000

Titel: A Data Mining Approach for Signal Detection and Analysis
verfasst von: Andrew Bate
Marie Lindquist
I. Ralph. Edwards
Roland Orre
Publikationsdatum: 01.05.2002
Verlag: Springer International Publishing
Erschienen in: Drug Safety / Ausgabe 6/2002
Print ISSN: 0114-5916
Elektronische ISSN: 1179-1942
DOI: https://doi.org/10.2165/00002018-200225060-00002

Springer Medizin

Abstract

Bitte loggen Sie sich ein, um Zugang zu diesem Inhalt zu erhalten

Weitere Artikel der Ausgabe 6/2002

Automated Signal Generation in Prescription-Event Monitoring

From Pharmacovigilance to Pharmacoperformance

Signal Generation in the New Zealand Intensive Medicines Monitoring Programme

Causal Association in Pharmacovigilance and Pharmacoepidemiology

Signal Selection and Follow-Up in Pharmacovigilance

Use of Measures of Disproportionality in Pharmacovigilance