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Adverse drug event classification of health records using dictionary-based pre-processing and machine learning
Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics.
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
2015 (English)In: Proceedings of the Sixth International Workshop on Health Text Mining and Information Analysis: LOUHI / [ed] Cyril Grouin, Thierry Hamon, Aurélie Névéol, Pierre Zweigenbaum, New York: The association for computational linguistics , 2015, 121-130 p.Conference paper, Published paper (Refereed)
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Text
Abstract [en]

A method to find adverse drug reactions in electronic health records written in Swedish is presented. A total of 14,751 health records were manually classified into four groups. The records are normalised by pre-processing using both dic- tionaries and manually created word lists. Three different supervised machine learning algorithm were used to find the best results; decision tree, random forest and LibSVM. The best performance on a test dataset was with LibSVM obtaining a pre- cision of 0.69 and a recall of 0.66, and a F-score of 0.67. Our method found 865 of 981 true positives (88.2%) in a 3-class dataset which is an improvement of 49.5% over previous approaches.

Place, publisher, year, edition, pages
New York: The association for computational linguistics , 2015. 121-130 p.
National Category
Information Systems
Research subject
Computer and Systems Sciences
Identifiers
URN: urn:nbn:se:su:diva-124652ISBN: 978-1-941643-32-7 (print)OAI: oai:DiVA.org:su-124652DiVA: diva2:890487
Conference
Sixth International Workshop on Health Text Mining and Information Analysis, LOUHI,17 September 2015 Lisbon, Portugal
Available from: 2016-01-04 Created: 2016-01-04 Last updated: 2016-06-30

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