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The use of Bayesian confidence propagation neural network in pharmacovigilance
Umeå University, Faculty of Medicine, Pharmacology and Clinical Neuroscience.
2003 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

The WHO database contains more than 2.8 million case reports of suspected adverse drug reactions reported from 70 countries worldwide since 1968. The Uppsala Monitoring Centre maintains and analyses this database for new signals on behalf of the WHO Programme for International Drug Monitoring. A goal of the Programme is to detect signals, where a signal is defined as "Reported information on a possible causal relationship between an adverse event and a drug, the relationship being unknown or incompletely documented previously."

The analysis of such a large amount of data on a case by case basis is impossible with the resources available. Therefore a quantitative, data mining procedure has been developed to improve the focus of the clinical signal detection process. The method used, is referred to as the BCPNN (Bayesian Confidence Propagation Neural Network). This not only assists in the early detection of adverse drug reactions (ADRs) but also further analysis of such signals. The method uses Bayesian statistical principles to quantify apparent dependencies in the data set. This quantifies the degree to which a specific drug- ADR combination is different from a background (in this case the WHO database). The measure of disproportionality used, is referred to as the Information Component (IC) because of its' origins in Information Theory. A confidence interval is calculated for the IC of each combination. A neural network approach allows all drug-ADR combinations in the database to be analysed in an automated manner. Evaluations of the effectiveness of the BCPNN in signal detection are described.

To compare how a drug association compares in unexpectedness to related drugs, which might be used for the same clinical indication, the method is extended to consideration of groups of drugs. The benefits and limitations of this approach are discussed with examples of known group effects (ACE inhibitors - coughing and antihistamines - heart rate and rhythm disorders.) An example of a clinically important, novel signal found using the BCPNN approach is also presented. The signal of antipsychotics linked with heart muscle disorder was detected using the BCPNN and reported.

The BCPNN is now routinely used in signal detection to search single drug - single ADR combinations. The extension of the BCPNN to discover 'unexpected' complex dependencies between groups of drugs and adverse reactions is described. A recurrent neural network method has been developed for finding complex patterns in incomplete and noisy data sets. The method is demonstrated on an artificial test set. Implementation on real data is demonstrated by examining the pattern of adverse reactions highlighted for the drug haloperidol. Clinically important, complex relationships in this kind of data are previously unexplored.

The BCPNN method has been shown and tested for use in routine signal detection, refining signals and in finding complex patterns. The usefulness of the output is influenced by the quality of the data in the database. Therefore, this method should be used to detect, rather than evaluate signals. The need for clinical analyses of case series remains crucial.

Place, publisher, year, edition, pages
2003.
Series
Umeå University medical dissertations, ISSN 0346-6612 ; 846
Keyword [en]
adverse drug reactions, pharmacovigilance, signal detection, spontaneous reporting, data mining, Bayesian statistics, BCPNN, neural network
Research subject
Medical Pharmacology
Identifiers
URN: urn:nbn:se:umu:diva-83ISBN: 91-7305-466-6 (print)OAI: oai:DiVA.org:umu-83DiVA: diva2:144650
Public defence
2003-06-07
Available from: 2003-06-07 Created: 2003-06-07Bibliographically approved
List of papers
1. A Bayesian neural network method for adverse drug reaction signal generation
Open this publication in new window or tab >>A Bayesian neural network method for adverse drug reaction signal generation
Show others...
1998 In: European Journal of Clinical Pharmacology, ISSN 0031-6970, Vol. 54, no 4, 315-321 p.Article in journal (Refereed) Published
Identifiers
urn:nbn:se:umu:diva-5204 (URN)
Available from: 2003-06-07 Created: 2003-06-07Bibliographically approved
2. A retrospective evaluation of a data mining approach to aid finding new adverse drug reaction signals in the WHO international database
Open this publication in new window or tab >>A retrospective evaluation of a data mining approach to aid finding new adverse drug reaction signals in the WHO international database
2000 In: Drug Safety, Vol. 23, no 6, 533-542 p.Article in journal (Refereed) Published
Identifiers
urn:nbn:se:umu:diva-5205 (URN)
Available from: 2003-06-07 Created: 2003-06-07Bibliographically approved
3. Data-mining analyses of pharmacovigilance signals in relation to relevant comparison drugs
Open this publication in new window or tab >>Data-mining analyses of pharmacovigilance signals in relation to relevant comparison drugs
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2002 In: European Journal of Clinical Pharmacology, Vol. 58, no 7, 483 - 490 p.Article in journal (Refereed) Published
Identifiers
urn:nbn:se:umu:diva-5206 (URN)
Available from: 2003-06-07 Created: 2003-06-07Bibliographically approved
4. Antipsychotics drugs and heart muscle disorder in international pharmacovigilance: a data mining study
Open this publication in new window or tab >>Antipsychotics drugs and heart muscle disorder in international pharmacovigilance: a data mining study
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2001 In: BMJ, Vol. 322, no 7296, 1207–1209- p.Article in journal (Refereed) Published
Identifiers
urn:nbn:se:umu:diva-5207 (URN)
Available from: 2003-06-07 Created: 2003-06-07Bibliographically approved
5. A Bayesian recurrent neural network approach for finding dependencies in large incomplete data sets
Open this publication in new window or tab >>A Bayesian recurrent neural network approach for finding dependencies in large incomplete data sets
(English)Manuscript (preprint) (Other academic)
National Category
Neurology Pharmacology and Toxicology
Identifiers
urn:nbn:se:umu:diva-5208 (URN)
Available from: 2003-06-07 Created: 2003-06-07 Last updated: 2017-03-27Bibliographically approved

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Citation style
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