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.

Automatic Deidentification of French Electronic Health Records: A Cost-Effective Approach Exploiting Distant Supervision and Deep Learning Models

“…In this work, we address this challenge by processing more than 58 document types. Furthermore, while previous work on French clinical deidentification annotates their corpus manually ( [4], [9]), our approach uses distant supervision, which reduces both the cost and time required for annotation.…”

“…To reuse such records and conduct health data-related studies, the task of deidentification has become essential ( [4], [5], [6]). This is necessary to protect the confidentiality of personal data in EHRs and comply with government regulations set in our case by the French Data Protection Authority, Commission Nationale de l'Informatique et des Libertés -(CNIL) 1 , and the General Data Protection Regulation -(GDPR) 2 .…”

Development and Validation of a Natural Language Processing Algorithm to Pseudonymize Documents in the Context of a Clinical Data Warehouse

“…Although our own dataset contains more than 3,600 annotated documents, our experiments with varying the size of the training set led us to the conclusion that excellent performance is achieved as early as 500 annotated documents, and that performance stops increasing significantly beyond 1,000 documents (►Fig. 6).…”

“…A lot of work has been done on this topic, in several languages, [1][2][3] including French. [4][5][6][7] Different scenarios have been proposed to improve the processing of this task. 4,8 Yet, there is no consensus method or protocol in the community, and more importantly it is very difficult for new actors to benefit from the experience and tools implemented by others, for several reasons.…”

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

“…To reuse such records and conduct health data-related studies, the task of de-identification has become essential [4][5][6]. This is necessary to protect the confidentiality of personal data in EHRs and comply with government regulations set in our case by the French Data Protection Authority, Commission Nationale de l'Informatique et des Libertés-(CNIL), 1 and the General Data Protection Regulation-(GDPR).…”