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Predicting Adverse Drug Events with Confidence
University of Borås, Faculty of Librarianship, Information, Education and IT. (Strategic Research Program in Data Science)ORCID iD: 0000-0003-0274-9026
University of Stockholm.
University of Borås, Faculty of Librarianship, Information, Education and IT. (Strategic Research Program in Data Science)
University of Borås, Faculty of Librarianship, Information, Education and IT. (Strategic Research Program in Data Science)
2015 (English)In: Thirteenth Scandinavian Conference on Artificial Intelligence / [ed] Sławomir Nowaczyk, IOS Press, 2015Conference paper, Published paper (Refereed)
Abstract [en]

This study introduces the conformal prediction framework to the task of predicting the presence of adverse drug events in electronic health records with an associated measure of statistically valid confidence. The imbalanced nature of the problem was addressed both by evaluating different machine learning algorithms, and by comparing different types of conformal predictors. A novel solution was also evaluated, where different underlying models, each model optimized towards one particular class, were combined into a single conformal predictor. This novel solution proved to be superior to previously existing approaches.

Place, publisher, year, edition, pages
IOS Press, 2015.
Series
Frontiers in Artificial Intelligence and Applications
Keywords [en]
Adverse Drug Events, Class Imbalance, Conformal Prediction, Predicting with Confidence.
National Category
Computer Sciences
Research subject
Bussiness and IT
Identifiers
URN: urn:nbn:se:hb:diva-3807DOI: 10.3233/978-1-61499-589-0-88ISBN: 978-1-61499-589-0 (print)OAI: oai:DiVA.org:hb-3807DiVA, id: diva2:877969
Conference
Thirteenth Scandinavian Conference on Artificial Intelligence
Projects
High-Performance, Data Mining, Drug Effect Detection
Funder
Swedish Foundation for Strategic Research Available from: 2015-12-08 Created: 2015-12-08 Last updated: 2018-01-10Bibliographically approved

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Löfström, TuveLinnusson, HenrikJansson, Karl

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
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Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
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Output format
  • html
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  • asciidoc
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