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Updated: Sep 10, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Published on: December 6, 2024

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使用本地大型语言模型提取药物信息

Phillip Richter-Pechanski1, Marvin Seiferling2, Christina Kiriakou3

  • 1Section of Bioinformatics and Systems Cardiology, Klaus Tschira Institute for Integrative Computational Cardiology, Im Neuenheimer Feld 669, 69120 Heidelberg, DE, Germany; Department of Internal Medicine III, University Hospital, Im Neuenheimer Feld 410, 69120 Heidelberg, DE, Germany; German Center for Cardiovascular Research (DZHK) - Partner site Heidelberg/Mannheim, Im Neuenheimer Feld 669, 69120 Heidelberg, DE, Germany; Informatics for Life, Im Neuenheimer Feld 669, 69120 Heidelberg, DE, Germany; Department of Computational Linguistics, Heidelberg University, Im Neuenheimer Feld 325, 69120 Heidelberg, DE, Germany.

Journal of biomedical informatics
|August 23, 2025
PubMed
概括

精心调整的本地大语言模型 (LLM) 从临床文本中实现了最先进的药物提取,提高了准确性和透明度. 这些模型为现实世界医疗环境提供了高效可靠的解决方案.

关键词:
临床NLP精细调整可解释性大型语言模型拉玛药物信息提取

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