Background: Biomedical natural language processing tasks are best performed with English models, and translation tools have undergone major improvements. On the other hand, building annotated biomedical datasets remains a challenge.
Objective: The aim of our study is to determine whether the use of English tools to extract and normalize French medical concepts on translations provides comparable performance to that of French models trained on a set of annotated French clinical notes.
Methods: We compare two methods: one involving French-language models and one 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 stages (NER, normalization and translation) of our algorithms.
Results: The native French method outperformed the translated English method, with an overall f1 score of 0.51 [0.47;0.55], compared with 0.39 [0.34;0.44] and 0.38 [0.36;0.40] for the two English methods tested.
Conclusions: Despite recent improvements in translation models, there is a significant difference in performance between the two approaches in favor of the native French method, which is more effective on French medical texts, even with few annotated documents.