Automatic de-identification of French electronic health records: a cost-effective approach exploiting distant supervision and deep learning models

Electronic health records (EHRs) contain valuable information for clinical research; however, the sensitive nature of healthcare data presents security and confidentiality challenges. De-identification is therefore essential to protect personal data in EHRs and comply with government regulations. Na...

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Bibliographic Details
Published in:	BMC medical informatics and decision making Vol. 24; no. 1; pp. 54 - 18
Main Authors:	Azzouzi, Mohamed El, Coatrieux, Gouenou, Bellafqira, Reda, Delamarre, Denis, Riou, Christine, Oubenali, Naima, Cabon, Sandie, Cuggia, Marc, Bouzillé, Guillaume
Format:	Journal Article
Language:	English
Published:	England BioMed Central Ltd 16-02-2024 BioMed Central BMC
Series:	Bmc Medical Informatics and Decision Making
Subjects:	Analysis Annotations Automatic annotation Bioengineering Clinical de-identification Datasets Deep learning Distant supervision Effectiveness Electronic health records Electronic medical records Electronic records Government regulations Knowledge bases (artificial intelligence) Life Sciences Machine learning Medical care, Cost of Medical records Named entity recognition Neural networks Privacy State-of-the-art reviews Word representations France Deep learning Distant supervision Clinical de-identification Named entity recognition Automatic annotation Word representations French language
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