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

ABSTRACT:

Background

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. Named entity recognition (NER) methods have been proposed to remove personal identifiers, with deep learning-based models achieving better performance. However, manual annotation of training data is time-consuming and expensive. The aim of this study was to develop an automatic de-identification pipeline for all kinds of clinical documents based on a distant supervised method to significantly reduce the cost of manual annotations and to facilitate the transfer of the de-identifi

SUBMITTER: Azzouzi ME 

PROVIDER: S-EPMC10870625 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature