Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.3233/978-1-60750-588-4-949 | Studies in Health Technology and Informatics |
Keywords | Field | DocType |
Natural language processing,Information extraction,Drug prescriptions,Computerized medical records systems,Information storage and retrieval/methods | English units,Data mining,English language,Computer science,Natural language processing,Artificial intelligence,General observation,Medical prescription | Conference |
Volume | Issue | ISSN |
160 | Pt 2 | 0926-9630 |
Citations | PageRank | References |
4 | 0.40 | 3 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Louise Deleger | 1 | 234 | 20.13 |
| Cyril Grouin | 2 | 4 | 0.40 |
| Pierre Zweigenbaum | 3 | 4 | 0.40 |