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Updated: Sep 19, 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

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预测30天的术后死亡率和美国麻醉师协会使用检索增强的大型语言模型来预测身体状态:开发和验证研究.

Ying-Hao Chen1, Shanq-Jang Ruan1, Pei-Fu Chen2,3

  • 1Department of Electronic and Computer Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan.

Journal of medical Internet research
|June 3, 2025
PubMed
概括

使用大型语言模型 (LLM) 的检索增强生成 (RAG) 显著改善了30天术后死亡率和美国麻醉学会 (ASA) 身体状况分类的预测. 这种人工智能方法提高了准确性,特别是在罕见的高风险病例中,有助于临床决策支持.

关键词:
几次射击提示提示的提示机器学习是机器学习.在外科手术期间的护理.预测模型 预测模型手术风险分层的分层化非结构化的临床数据.

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