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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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通过使用检索增强生成的大型语言模型改进自动化深度表型化.

Brandon T Garcia1,2,3, Lauren Westerfield1,4, Priya Yelemali1

  • 1Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, 77030, USA.

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概括
此摘要是机器生成的。

通过使用提取增强生成,RAG-HPO通过准确地分配人类表型本体学 (HPO) 术语来改善罕见遗传疾病诊断,超越现有的工具.

关键词:
临床基因组学 临床基因组学生成性AI是一种人工智能.发电预训练式变压器 (GPT) 是一种人类现象型本体学 (HPO)大型语言模型 (LLM)拉玛 - - 拉玛三世自然语言处理 (NLP) 是一种自然语言处理.现型化 (Phenotyping) 是一种表现方式.检索增强生成 (RAG) 进行检索.

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科学领域:

  • 计算生物学和生物信息学
  • 基因组学和遗传疾病研究研究.
  • 在临床环境中使用自然语言处理 (NLP).

背景情况:

  • 精确诊断罕见遗传疾病需要精确的表型和基因型分析.
  • 人类表型本体学 (HPO) 为临床表型提供了一个标准化的语言.
  • 现有的HPO工具 (Doc2HPO,ClinPhen) 难以完成不完整的任务,需要手动审查;LLM容易产生幻觉.

研究的目的:

  • 介绍RAG-HPO,一个基于Python的新工具,利用Retrieval-Augmented Generation (RAG) 进行准确的HPO术语赋值.
  • 提高 LLM 在 HPO 术语提取中的准确性,而无需微调,解决当前方法的局限性.

主要方法:

  • RAG-HPO使用了一个动态向量数据库,包含超过54,000个表型短语,映射到HPO ID.
  • 工作流包括LLM提取表型短语,对向量数据库进行语义相似性匹配,以及基于LLM的HPO术语赋值.
  • 性能与Doc2HPO,ClinPhen和FastHPOCR进行了比较,使用120个案例报告与1792个手动分配的HPO术语.

主要成果:

  • RAG-HPO,由Llama-3 70B提供动力,在120个案例报告中实现了0.84的平均精度,0.78的召回率和0.80的F1得分.
  • 这些结果明显超过了传统工具 (p<0.00001).
  • 假阳性HPO术语的识别率很低 (15.8%),幻觉的几率很小 (2.7%).

结论:

  • RAG-HPO是一个用户友好的,可适应的工具,显著优于标准的HPO匹配工具.
  • 它的增强精度和回忆加速了对罕见疾病背后的遗传机制的识别.
  • RAG-HPO代表了用于遗传研究和临床基因组学的表型分析的实质性进展.