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Predicting Adverse Drug Events with Confidence
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
2015 (English)In: Thirteenth Scandinavian Conference on Artificial Intelligence / [ed] Sławomir Nowaczyk, IOS Press, 2015, 88-97 p.Conference paper, Published paper (Refereed)
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Text
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. 88-97 p.
Series
Frontiers in Artificial Intelligence and Applications, ISSN 0922-6389 ; 278
National Category
Information Systems
Research subject
Computer and Systems Sciences
Identifiers
URN: urn:nbn:se:su:diva-123964DOI: 10.3233/978-1-61499-589-0-88ISBN: 978-1-61499-588-3 (print)ISBN: 978-1-61499-589-0 (print)OAI: oai:DiVA.org:su-123964DiVA: diva2:878608
Conference
Thirteenth Scandinavian Conference on Artificial Intelligence, Halmstad, Sweden, 4-5 November 2015
Available from: 2015-12-09 Created: 2015-12-09 Last updated: 2017-01-16Bibliographically approved

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