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Published on: September 20, 2018
Extracting medication information from French clinical texts
Louise Deléger1, Cyril Grouin, Pierre Zweigenbaum
1Limsi-CNRS, Orsay, France. louise.deleger@limsi.fr
Studies in Health Technology and Informatics
|September 16, 2010
Summary
This study adapted an English medication extraction system for French clinical texts. The rule-based system achieved 86.7% F-measure, demonstrating cross-lingual transferability for medical Natural Language Processing (NLP).
Area of Science:
- Natural Language Processing (NLP)
- Medical Informatics
- Computational Linguistics
Background:
- English dominates Natural Language Processing (NLP) research, including medical NLP.
- Clinical language processing needs are significant across multiple languages.
- Drug prescription information shows cross-lingual similarities, enabling system transfer.
Purpose of the Study:
- To implement a medication extraction system for French clinical texts.
- To adapt an existing English-based NLP approach for a new language.
- To evaluate the system's performance and cross-lingual applicability.
Main Methods:
- Utilized a rule-based approach with specialized lexicons.
- Adapted a system initially designed for English (i2b2 2009 challenge).
- Applied the system to extract drugs and related information from French clinical texts.
Main Results:
- Achieved an 86.7% F-measure in the first evaluation on 50 annotated texts.
- Outperformed the original English system's reported performance.
- Demonstrated comparable performance to related work in French medical NLP.
Conclusions:
- A rule-based NLP approach is effective for both English and French medication extraction.
- Successful cross-lingual transfer of medical NLP systems is feasible.
- Further improvements for both English and French systems are discussed.
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