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利用开源的大型语言模型在资源有限的环境中提取临床信息.

Luc Builtjes1, Joeran Bosma1, Mathias Prokop1

  • 1Department of Radiology and Nuclear Medicine, Radboud University Medical Center, 6525GA Nijmegen, The Netherlands.

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概括

开源大型语言模型 (LLM) 在从荷兰临床文本中提取信息时显示出强大的零射击性能. 像Llama-3.3-70B这样的模型非常出色,特别是在回归任务中,为NLP挑战提供了可行的替代方案.

关键词:
人工智能的人工智能是人工智能.信息的存储和检索.大型语言模型.自然语言处理自然语言处理.

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科学领域:

  • 自然语言处理 (NLP) 是一种自然语言处理.
  • 人工智能 (AI) 是一种人工智能.
  • 临床信息学 临床信息学

背景情况:

  • 从荷兰医疗报告中提取临床信息是具有挑战性的,因为语言障碍和资源有限.
  • 开源生成型大语言模型 (LLM) 为自动化文本分析提供了潜在的解决方案.

研究的目的:

  • 评估开源生成LLM在从荷兰医疗报告中提取临床信息上的零射击性能.
  • 通过使用DRAGON基准来评估LLM在分类,回归和命名实体识别 (NER) 任务中的能力.

主要方法:

  • 开发了llm_extractinator框架,用于可扩展的,开源的临床文本信息提取.
  • 在零射击环境中对28个DRAGON基准任务进行了9个多语言LLM的评估.
  • 研究了英语语文翻译对模型性能的影响.

主要成果:

  • 拉玛-3.3-70B获得了最高的实用性得分 (0.760),其他模型如Phi-4-14B和Qwen-2.5-14B也表现良好.
  • 在17个任务中,LLM的表现超过或与微调的罗伯塔基线相匹配,特别是在回归和结构化分类方面.
  • 在所有模型中,命名实体识别 (NER) 性能始终较低,而向英语翻译通常会降低性能.

结论:

  • 开源生成的LLM在临床NLP任务中表现出显著的零射击能力,特别是在结构化推理中.
  • 围绕14B参数的模型提供了性能和计算成本的良好平衡,尽管较大的模型领先.
  • 母语支持至关重要,因为向英语翻译会对表现产生负面影响,这凸显了低资源和多语言环境的价值.