@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://preview.aclanthology.org/ingest_wac_2008/2023.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."
}