Named entity recognition for de-identifying Spanish electronic health records
The study's conclusions showcase the practical applicability of the state-of-the-art Transformers models for precise de-identification of clinical notes in real-world medical settings in Spanish, with the potential to improve performance if continual pre-training strategies are implemented.
Authors
- José M. Jerez (ORCID: https://orcid.org/0000-0002-7858-2966)
- Guillermo López-García (ORCID: https://orcid.org/0000-0001-5903-1483)
- Francisco J. Moreno-Barea (ORCID: https://orcid.org/0000-0002-3887-9095)
- Emilio Alba (ORCID: https://orcid.org/0000-0002-3364-2603)
- Francisco J. Veredas (ORCID: https://orcid.org/0000-0003-0739-2505)
- Héctor Mesa (ORCID: https://orcid.org/0000-0002-9656-1456)
- Nuria Ribelles (ORCID: https://orcid.org/0000-0003-3194-7398)
Institutions
- Cedars-Sinai Medical Center (US)
- Hospital Clínico Universitario Virgen de la Victoria (ES)
- Universidad de Málaga (ES)
Publication Details
- Journal
- Computers in Biology and Medicine
- Published
- 2024-12-21
- DOI
- https://doi.org/10.1016/j.compbiomed.2024.109576
- Citations
- 3
- Primary Topic
- Topic Modeling
- Type
- article
- Field-Weighted Citation Impact
- 1.04