Bruno Ribeiro


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INCOGNITUS: A Toolbox for Automated Clinical Notes Anonymization
Bruno Ribeiro | Vitor Rolla | Ricardo Santos
Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations

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.