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Deviating time-to-onset in predictive models: detecting new adverse effects from medicines
Uppsala University, Disciplinary Domain of Science and Technology, Biology, Biology Education Centre.
2015 (English)Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Identifying previously unknown adverse drug reactions becomes more important as the number of drugs and the extent of their use increases. The aim of this Master’s thesis project was to evaluate the performance of a novel approach for highlighting potential adverse drug reactions, also known as signal detection. The approach was based on deviating time-to-onset patterns and was implemented as a two-sample Kolmogorov-Smirnov test for non-vaccine data in the safety report database, VigiBase. The method was outperformed by both disproportionality analysis and the multivariate predictive model vigiRank. Performance estimates indicate that deviating time-to-onset patterns is not a suitable approach for signal detection for non-vaccine data in VigiBase.

Place, publisher, year, edition, pages
2015. , 54 p.
Series
UPTEC X, 15 024
Keyword [en]
data mining, Kolmogorov-Smirnov test, predictive model, signal detection, time-to-onset
National Category
Computer and Information Science
Identifiers
URN: urn:nbn:se:uu:diva-257100OAI: oai:DiVA.org:uu-257100DiVA: diva2:828469
Educational program
Molecular Biotechnology Engineering Programme
Examiners
Available from: 2015-06-30 Created: 2015-06-30 Last updated: 2015-06-30Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
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Language
  • de-DE
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  • nn-NB
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  • Other locale
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Output format
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