Development and validation of a natural language processing algorithm to pseudonymize documents in the context of a clinical data warehouse
This work addresses the critical issue of patient privacy in clinical data sharing for research, though it is incremental as it builds on existing methods for pseudonymization.
The study tackled the problem of de-identifying clinical reports to enable research access while protecting patient privacy, achieving an overall F1-score of 0.99 with a hybrid system combining deep learning and manual rules.
The objective of this study is to address the critical issue of de-identification of clinical reports in order to allow access to data for research purposes, while ensuring patient privacy. The study highlights the difficulties faced in sharing tools and resources in this domain and presents the experience of the Greater Paris University Hospitals (AP-HP) in implementing a systematic pseudonymization of text documents from its Clinical Data Warehouse. We annotated a corpus of clinical documents according to 12 types of identifying entities, and built a hybrid system, merging the results of a deep learning model as well as manual rules. Our results show an overall performance of 0.99 of F1-score. We discuss implementation choices and present experiments to better understand the effort involved in such a task, including dataset size, document types, language models, or rule addition. We share guidelines and code under a 3-Clause BSD license.