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De-identification of Emergency Medical Records in French: Survey and Comparison of State-of-the-Art Automated Systems

Abstract : 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.
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https://hal.inria.fr/hal-03241384
Contributor : Marta Avalos Connect in order to contact the contributor
Submitted on : Wednesday, June 2, 2021 - 9:05:51 AM
Last modification on : Tuesday, December 21, 2021 - 2:50:06 PM
Long-term archiving on: : Friday, September 3, 2021 - 6:23:49 PM

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Loick Bourdois, Marta Avalos, Gabrielle Chenais, Frantz Thiessard, Philippe Revel, et al.. De-identification of Emergency Medical Records in French: Survey and Comparison of State-of-the-Art Automated Systems. Florida Artificial Intelligence Research Society, 34 (1), 2021, 2334-0754. ⟨10.32473/flairs.v34i1.128480⟩. ⟨hal-03241384⟩

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