Medical document anonymization with a semantic lexicon.
Year: 2000
Type: article
Abstract: We present an original system for locating and removing personally-identifying information in patient records. In this experiment, anonymization is seen as a particular case of knowledge extraction. We use natural language processing tools provided by the MEDTAG framework: a semantic lexicon specialized in medicine, and a toolkit for word-sense and morpho-syntactic tagging. The system finds 98-99% of all personally-identifying information.
Source: PubMed Central
Authors: P Ruch, R H Baud, A M Rassinoux, P Bouillon, G Robert
Institutions: University of Geneva, University Hospital of Geneva
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W19235523