Span-based annotation framework for LLM-based clinical named entity recognition: development and validation using Korean emergency department notes
This study introduced a novel span-based annotation framework for LLM-based clinical NER verified by three independent experiments. In multilingual and real-world clinical settings, LLMs have proven in handling complex entity spans that include word-level and phrase-level annotations, particularly for long and attribute-rich entities.
Authors
- Eun Hye Jang (ORCID: https://orcid.org/0009-0005-8685-211X)
- Sangji Lee
- Hyungpil Moon (ORCID: https://orcid.org/0000-0002-1091-0716)
- Javier Aguirre (ORCID: https://orcid.org/0009-0007-6802-944X)
- Won Chul (ORCID: https://orcid.org/0000-0002-2778-2992)
Institutions
- Samsung Medical Center (KR)
- Sungkyunkwan University (KR)
Publication Details
- Journal
- JAMIA Open
- Published
- 2025-11-03
- DOI
- https://doi.org/10.1093/jamiaopen/ooaf157
- Primary Topic
- Topic Modeling
- Type
- article
- Field-Weighted Citation Impact
- 0.00