De-identification of Emergency Medical Records in French: Survey and Comparison of State-of-the-Art Automated Systems - Loick Bourdois; Marta Avalos; Gabrielle Chenais; Frantz Thiessard; Philippe Revel; Cedric Gil-Jardine; Emmanuel Lagarde (Proceedings of the International Florida Artificial Intelligence Research Society Conference)
In France, structured data from emergency room (ER) visits are aggregated at the national level to build a syndromic surveillance system for several health events. For visits motivated by a traumatic event, information on the causes are stored in free-text clinical notes. To exploit these data, an automated de-identification system guaranteeing protection of privacy is required. In this study we review available de-identification tools to de-identify free-text clinical documents in French. A key point is how to overcome the resource barrier that hampers NLP applications in languages other than English. We compare rule-based, named entity recognition, new Transformer-based deep learning and hybrid systems using, when required, a fine-tuning set of 30,000 unlabeled clinical notes. The evaluation is performed on a test set of 3,000 manually annotated notes. Hybrid systems, combining capabilities in complementary tasks, show the best performance. This work is a first step in the foundation of a national surveillance system based on the exhaustive collection of ER visits reports for automated trauma monitoring.