HitzalMed: Anonymisation of Clinical Text in Spanish

Salvador Lima Lopez, Naiara Perez, Laura García-Sardiña, Montse Cuadros


Abstract
HitzalMed is a web-framed tool that performs automatic detection of sensitive information in clinical texts using machine learning algorithms reported to be competitive for the task. Moreover, once sensitive information is detected, different anonymisation techniques are implemented that are configurable by the user –for instance, substitution, where sensitive items are replaced by same category text in an effort to generate a new document that looks as natural as the original one. The tool is able to get data from different document formats and outputs downloadable anonymised data. This paper presents the anonymisation and substitution technology and the demonstrator which is publicly available at https://snlt.vicomtech.org/hitzalmed.
Anthology ID:
2020.lrec-1.870
Volume:
Proceedings of the 12th Language Resources and Evaluation Conference
Month:
May
Year:
2020
Address:
Marseille, France
Venues:
COLING | LREC
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
7038–7043
Language:
English
URL:
https://www.aclweb.org/anthology/2020.lrec-1.870
DOI:
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.lrec-1.870.pdf