@inproceedings{ribeiro-etal-2023-incognitus,
title = "{INCOGNITUS}: A Toolbox for Automated Clinical Notes Anonymization",
author = "Ribeiro, Bruno and
Rolla, Vitor and
Santos, Ricardo",
editor = "Croce, Danilo and
Soldaini, Luca",
booktitle = "Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations",
month = may,
year = "2023",
address = "Dubrovnik, Croatia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.eacl-demo.22",
doi = "10.18653/v1/2023.eacl-demo.22",
pages = "187--194",
abstract = "Automated text anonymization is a classical problem in Natural Language Processing (NLP). The topic has evolved immensely throughout the years, with the first list-search and rule-based solutions evolving to statistical modeling approaches and later to advanced systems that rely on powerful state-of-the-art language models. Even so, these solutions fail to be widely implemented in the most privacy-demanding areas of activity, such as healthcare; none of them is perfect, and most can not guarantee rigorous anonymization. This paper presents INCOGNITUS, a flexible platform for the automated anonymization of clinical notes that offers the possibility of applying different techniques. The available tools include an underexplored yet promising method that guarantees 100{\%} recall by replacing each word with a semantically identical one. In addition, the presented framework incorporates a performance evaluation module to compute a novel metric for information loss assessment in real-time.",
}
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%0 Conference Proceedings
%T INCOGNITUS: A Toolbox for Automated Clinical Notes Anonymization
%A Ribeiro, Bruno
%A Rolla, Vitor
%A Santos, Ricardo
%Y Croce, Danilo
%Y Soldaini, Luca
%S Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations
%D 2023
%8 May
%I Association for Computational Linguistics
%C Dubrovnik, Croatia
%F ribeiro-etal-2023-incognitus
%X Automated text anonymization is a classical problem in Natural Language Processing (NLP). The topic has evolved immensely throughout the years, with the first list-search and rule-based solutions evolving to statistical modeling approaches and later to advanced systems that rely on powerful state-of-the-art language models. Even so, these solutions fail to be widely implemented in the most privacy-demanding areas of activity, such as healthcare; none of them is perfect, and most can not guarantee rigorous anonymization. This paper presents INCOGNITUS, a flexible platform for the automated anonymization of clinical notes that offers the possibility of applying different techniques. The available tools include an underexplored yet promising method that guarantees 100% recall by replacing each word with a semantically identical one. In addition, the presented framework incorporates a performance evaluation module to compute a novel metric for information loss assessment in real-time.
%R 10.18653/v1/2023.eacl-demo.22
%U https://aclanthology.org/2023.eacl-demo.22
%U https://doi.org/10.18653/v1/2023.eacl-demo.22
%P 187-194
Markdown (Informal)
[INCOGNITUS: A Toolbox for Automated Clinical Notes Anonymization](https://aclanthology.org/2023.eacl-demo.22) (Ribeiro et al., EACL 2023)
ACL
- Bruno Ribeiro, Vitor Rolla, and Ricardo Santos. 2023. INCOGNITUS: A Toolbox for Automated Clinical Notes Anonymization. In Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations, pages 187–194, Dubrovnik, Croatia. Association for Computational Linguistics.