Adverse drug event classification of health records using dictionary-based pre-processing and machine learning
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 (Refereed)Text
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.
Research subject Computer and Systems Sciences
IdentifiersURN: urn:nbn:se:su:diva-124652ISBN: 978-1-941643-32-7OAI: oai:DiVA.org:su-124652DiVA: diva2:890487
Sixth International Workshop on Health Text Mining and Information Analysis, LOUHI,17 September 2015 Lisbon, Portugal