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De-identifying Norwegian Clinical Text using Resources from Swedish and Danish

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https://hdl.handle.net/10037/32958
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article.pdf (194.7Kb)
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Dato
2023
Type
Journal article
Tidsskriftartikkel

Forfatter
Lamproudis, Anastasios; Mora, Sara; Olsen Svenning, Therese; Torsvik, Torbjørn; Chomutare, Taridzo Fred; Ngo, Phuong Dinh; Dalianis, Hercules
Sammendrag
The lack of relevant annotated datasets represents one key limitation in the application of Natural Language Processing techniques in a broad number of tasks, among them Protected Health Information (PHI) identification in Norwegian clinical text. In this work, the possibility of exploiting resources from Swedish, a very closely related language, to Norwegian is explored. The Swedish dataset is annotated with PHI information. Different processing and text augmentation techniques are evaluated, along with their impact in the final performance of the model. The augmentation techniques, such as injection and generation of both Norwegian and Scandinavian Named Entities into the Swedish training corpus, showed to increase the performance in the de-identification task for both Danish and Norwegian text.This trend was also confirmed by the evaluation of model performance on a sample Norwegian gastro surgical clinical text.
Beskrivelse
Source at https://knowledge.amia.org/event-data.
Sitering
Lamproudis, Mora, Olsen Svenning, Torsvik, Chomutare, Ngo, Dalianis. De-identifying Norwegian Clinical Text using Resources from Swedish and Danish. AMIA Annual Symposium Proceedings. 2023;2023:456-464
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  • Artikler, rapporter og annet (informatikk) [477]
Copyright 2023 The Author(s)

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