Performance en classification de données textuelles des passages aux urgences des modèles BERT pour le français

National audienceContextualized language models based on the Transformer architecture such as BERT (Bidirectional Encoder Representations from Transformers) have achieved remarkable performances in various language processing tasks. CamemBERT and FlauBERT are pre-trained versions for French.We used these two models to automatically classify free clinical notes from emergency department visits following a trauma. Their performances were compared to the TF-IDF (Term-Frequency - Inverse Document Frequency) method associated with the SVM (Support Vector Machine) classifier on 22481 clinical notes from the emergency department of the Bordeaux University Hospital. CamemBERT and FlauBERT obtained slightly better results than the TF-IDF/SVM couple for the micro F1-score. These encouraging results allow us to consider further developments in the use of transformers in the automation of emergency department data processing in order to consider the implementation of a national observatory of trauma in France.Les modèles de langue contextualisés basés sur l'architecture Transformer tels que BERT (Bidirectional Encoder Representations from Transformers) ont atteint des performances remarquables dans des diverses tâches de traitement de la langue. CamemBERT et FlauBERT en sont des versions pré-entraînées pour le français. Nous avons utilisé ces deux modèles afin de classer automatiquement des notes cliniques libres issues de visites aux urgences à la suite d'un traumatisme. Leurs performances ont été comparées à la méthode TF-IDF (Term-Frequency-Inverse Document Frequency) associé au classifieur SVM (Support Vector Machine) sur 22481 notes cliniques provenant du service des urgences du CHU de Bordeaux. CamemBERT et FlauBERT ont obtenu des résultats légèrement supérieurs à ceux du couple TF-IDF/SVM pour le micro F1-score. Ces résultats encourageants permettent d'envisager l'utilisation des transformers pour automatiser le traitement des données des urgences dans le cadre de la mise en place d'un observatoire national du traumatisme en France

Development and Validation of Deep Learning Transformer Models for Building a Comprehensive and Real-time Trauma Observatory

BACKGROUND In order to study the feasibility of setting up a national trauma observatory in France, OBJECTIVE we compared the performance of several automatic language processing methods on a multi-class classification task of unstructured clinical notes. METHODS A total of 69,110 free-text clinical notes related to visits to the emergency departments of the University Hospital of Bordeaux, France, between 2012 and 2019 were manually annotated. Among those clinical notes 22,481 were traumas. We trained 4 transformer models (deep learning models that encompass attention mechanism) and compared them with the TF-IDF (Term- Frequency - Inverse Document Frequency) associated with SVM (Support Vector Machine) method. RESULTS The transformer models consistently performed better than TF-IDF/SVM. Among the transformers, the GPTanam model pre-trained with a French corpus with an additional auto-supervised learning step on 306,368 unlabeled clinical notes showed the best performance with a micro F1-score of 0.969. CONCLUSIONS The transformers proved efficient multi-class classification task on narrative and medical data. Further steps for improvement should focus on abbreviations expansion and multiple outputs multi-class classification