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We obtained an overall average micro-F1 score of 0.811 for the multilingual version, 0.807 for the French-only version and 0.797 for the translated version. Our study proposes an original multilabel classification of French clinical notes for patient phenotyping. We show that a multilingual algorithm trained on annotated real clinical notes and UMLS vocabularies leads to the best results. Our study proposes an original multilabel classification of French clinical notes for patient phenotyping. We show that a multilingual algorithm train
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