Title | ||
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De-identification of Emergency Medical Records in French - Survey and Comparison of State-of-the-Art Automated Systems. |
Abstract | ||
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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. |
Year | DOI | Venue |
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2021 | 10.32473/flairs.v34i1.128480 | FLAIRS Conference |
DocType | Volume | Issue |
Conference | 34 | 1 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
7 |
Name | Order | Citations | PageRank |
---|---|---|---|
Loick Bourdois | 1 | 0 | 0.34 |
Avalos, Marta | 2 | 0 | 0.68 |
Gabrielle Chenais | 3 | 0 | 0.34 |
Frantz Thiessard | 4 | 0 | 0.34 |
Philippe Revel | 5 | 0 | 0.34 |
Cedric Gil-Jardine | 6 | 0 | 0.34 |
Emmanuel Lagarde | 7 | 0 | 0.34 |