Extracting medication information from French clinical texts.
Much more Natural Language Processing (NLP) work has been performed on the English language than on any other. This general observation is also true of medical NLP, although clinical language processing needs are as strong in other languages as they are in English. In specific subdomains, such as drug prescription, the expression of information can be closely related across different languages, which should help transfer systems from English to other languages. We report here the implementation of a medication extraction system which extracts drugs and related information from French clinical texts, on the basis of an approach initially designed for English within the framework of the i2b2 2009 challenge. The system relies on specialized lexicons and a set of extraction rules. A first evaluation on 50 annotated texts obtains 86.7% F-measure, a level higher than the original English system and close to related work. This shows that the same rule-based approach can be applied to English and French languages, with a similar level of performance. We further discuss directions for improving both systems.
Deléger
Louise
L
Limsi-CNRS, Orsay, France. louise.deleger@limsi.fr
Grouin
Cyril
C
Zweigenbaum
Pierre
P
D011307:Drug Prescriptions; D016247:Information Storage and Retrieval / Q000379:methods*; D009323:Natural Language Processing