This paper is in the following e-collection/theme issue:

Information Retrieval (348) Natural Language Processing (917) Big Data (307) Development and Evaluation of Research Methods, Instruments and Tools (744) Machine Learning (2076) Methods and Instruments in Medical Informatics (189) Generative Language Models Including ChatGPT (708)

Published on in Vol 27 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/67033, first published .
Prompt Framework for Extracting Scale-Related Knowledge Entities from Chinese Medical Literature: Development and Evaluation Study

Prompt Framework for Extracting Scale-Related Knowledge Entities from Chinese Medical Literature: Development and Evaluation Study

Prompt Framework for Extracting Scale-Related Knowledge Entities from Chinese Medical Literature: Development and Evaluation Study

Authors of this article:

Jie Hao1 Author Orcid Image ;   Zhenli Chen1 Author Orcid Image ;   Qinglong Peng2, 3 Author Orcid Image ;   Liang Zhao4 Author Orcid Image ;   Wanqing Zhao1 Author Orcid Image ;   Shan Cong2, 3 Author Orcid Image ;   Junlian Li1 Author Orcid Image ;   Jiao Li1 Author Orcid Image ;   Qing Qian1 Author Orcid Image ;   Haixia Sun1 Author Orcid Image
Article Authors Cited by (4) Tweetations Metrics

Journals

  1. Chen Z, Hao J, Sun H, Zhao L, Li J, Qian Q, Peng Q, Wang X, Cong S, Shen L, Guo Z, Pu S, Lin Y. MedScaleRE-PF: a prompt-based framework with retrieval-augmented generation, chain-of-thought, and self-verification for scale-specific relation extraction in Chinese medical literature. Information Processing & Management 2025;62(6):104278 View
  2. Kang H, Li J, Hou L, Xu X, Zheng S, Li Q. Large Language Model–Enhanced Drug Repositioning Knowledge Extraction via Long Chain-of-Thought: Development and Evaluation Study. JMIR Medical Informatics 2025;13:e77837 View
  3. Xu Y, Long Z, Setyohadi D. Enhancing Diabetes Management With CRIBC: A Novel NER Model for Constructing A Comprehensive Chinese Medical Knowledge Graph. Engineering Reports 2025;7(10) View
  4. Lin X, Yu L, Wang B, Li H. Decoding nested entities from classical Chinese with LLMs. International Journal of Geographical Information Science 2025:1 View