Prompt Framework for Extracting Scale-Related Knowledge Entities from Chinese Medical Literature: Development and Evaluation Study
MedScaleNER advances the application of LLMs and prompts engineering for specialized NER tasks in Chinese medical literature. By addressing the challenges of unstructured texts and limited annotated data, MedScaleNER's adaptability to various biomedical contexts supports more efficient and reliable knowledge extraction, contributing to broader measurement-based care implementation and improved clinical and research outcomes.
Authors
- Liang Zhao (ORCID: https://orcid.org/0000-0001-6505-290X)
- Wanqing Zhao (ORCID: https://orcid.org/0000-0003-3705-5737)
- Jeffrey S. Li (ORCID: https://orcid.org/0000-0002-0380-4659)
- Jie Hao (ORCID: https://orcid.org/0009-0002-7208-7443)
- Qin Peng (ORCID: https://orcid.org/0000-0002-7710-4639)
- Qing Qian (ORCID: https://orcid.org/0000-0002-9072-586X)
- Jiao Li (ORCID: https://orcid.org/0000-0001-6391-8343)
- Zhenli Chen (ORCID: https://orcid.org/0009-0008-9664-085X)
- Shan Cong (ORCID: https://orcid.org/0000-0002-5981-7678)
- Haixia Sun (ORCID: https://orcid.org/0009-0004-1652-5335)
Institutions
- Harbin Engineering University (CN)
- Chinese Academy of Medical Sciences & Peking Union Medical College (CN)
Publication Details
- Journal
- Journal of Medical Internet Research
- Published
- 2025-03-18
- DOI
- https://doi.org/10.2196/67033
- Citations
- 5
- Primary Topic
- Topic Modeling
- Type
- article
- Field-Weighted Citation Impact
- 12.40