Exploration of Adverse Drug Reactions in Semantic Vector Space Models of Clinical Text
2012 (English)In: , 2012Conference paper (Refereed)
A novel method for identifying potential side-effects to medications through large-scale analysis of clinical data is here introduced and evaluated. By calculating distributional similarities for medication-symptom pairs based on co-occurrence information in a large clinical corpus, many known adverse drug reactions are successfully identified. These preliminary results suggest that semantic vector space models of clinical text could also be used to generate hypotheses about potentially unknown adverse drug reactions. In the best model, 50% of the terms in a list of twenty are considered to be conceivable side-effects. Among the medication-symptom pairs, however, diagnostic indications and terms related to the medication in other ways also appear. These relations need to be distinguished in a more refined method for detecting adverse drug reactions.
Place, publisher, year, edition, pages
Research subject Computer and Systems Sciences
IdentifiersURN: urn:nbn:se:su:diva-86103OAI: oai:DiVA.org:su-86103DiVA: diva2:586173
ICML 2012, The 29th International Conference on Machine Learning, Edinburgh, Scotland, UK, June 26 – July 1, 2012
The paper was presented at the ICML Workshop on Machine Learning for Clinical Data Analysis.2013-01-112013-01-112013-10-01Bibliographically approved