  The objective of our study is to determine whether using English tools to
extract and normalize French medical concepts on translations provides
comparable performance to French models trained on a set of annotated French
clinical notes. We compare two methods: a method involving French language
models and a method involving English language models. For the native French
method, the Named Entity Recognition (NER) and normalization steps are
performed separately. For the translated English method, after the first
translation step, we compare a two-step method and a terminology-oriented
method that performs extraction and normalization at the same time. We used
French, English and bilingual annotated datasets to evaluate all steps (NER,
normalization and translation) of our algorithms. Concerning the results, the
native French method performs better than the translated English one with a
global f1 score of 0.51 [0.47;0.55] against 0.39 [0.34;0.44] and 0.38
[0.36;0.40] for the two English methods tested. In conclusion, despite the
recent improvement of the translation models, there is a significant
performance difference between the two approaches in favor of the native French
method which is more efficient on French medical texts, even with few annotated
documents.
