相关实验视频

Updated: Jun 12, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

516

一个大型语言模型用于检测放射学报告中的否定表达式

Yvonne Su1, Yonatan B Babore1, Charles E Kahn2,3

  • 1Department of Radiology, Perelman School of Medicine, University of Pennsylvania, 3400 Spruce Street, Philadelphia, 19104, PA, USA.

Journal of imaging informatics in medicine
|September 25, 2024
PubMed
概括

基于变压器的模型 (CAN-BERT) 与基于规则的系统 (medspaCy) 相比,在放射学报告中显著改善了否定检测. 这一进步增强了用于临床决策和医学研究的数据提取.

关键词:
大型语言模型.命名实体认可 命名实体认可自然语言处理自然语言处理.否定表达式 (negex) 检测检测放射学报告 放射学报告 放射学报告

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