ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2023
De-identifying Norwegian Clinical Text using Resources from Swedish and Danish.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
3 citing papers in PubMed.
- Domain-Specific Pretraining of NorDeClin-Bidirectional Encoder Representations From Transformers forJMIR AI · 2025Article
- Automated redaction of names in adverse event reports using transformer-based neural networks.BMC medical informatics and decision making · 2024Article
- Year 2023 in Biomedical Natural Language Processing: a Tribute to Large Language Models and Generative AI.Yearbook of medical informatics · 2024Article
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
Indexed as
Identifiers
38222432PMC10785939What OpenQuestion holds
Registered trials
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