Precision Structuring of Free-Text Surgical Record for Enhanced Stroke Management: A Comparative Evaluation of Large Language Models
LLMs, particularly GLM4 and GPT-4, efficiently and accurately structured both general and advanced characteristics from MTB surgical record, outperforming manual extraction methods and demonstrating potential for enhancing clinical data management in AIS treatment.
Authors
- Yuehua Li (ORCID: https://orcid.org/0000-0002-6511-8929)
- Bin Yan (ORCID: https://orcid.org/0000-0001-7515-6649)
- Yao Zeng
- Mengfei Wang
- Lisong Dai (ORCID: https://orcid.org/0000-0003-3608-9732)
- Xiaoer Wei (ORCID: https://orcid.org/0000-0002-1621-1967)
- Yueqi Zhu (ORCID: https://orcid.org/0000-0001-5476-9075)
- Jianyong Wei (ORCID: https://orcid.org/0000-0003-3654-5769)
- Yidong Jin
Institutions
- University of Shanghai for Science and Technology (CN)
- Shenyang University of Technology (CN)
- Shanghai Jiao Tong University (CN)
- Shanghai Sixth People's Hospital (CN)
Publication Details
- Journal
- Journal of Multidisciplinary Healthcare
- Published
- 2024-11-01
- DOI
- https://doi.org/10.2147/jmdh.s486449
- Citations
- 6
- Primary Topic
- Clinical practice guidelines implementation
- Type
- article
- Field-Weighted Citation Impact
- 3.26
Funders
- DeepMind
- National Natural Science Foundation of China