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使用检索增强生成来捕获精密瘤学分子驱动的治疗关系

Kory Kreimeyer¹, Jenna V Canzoniero^1,2, Maria Fatteh^1,2

¹Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins School of Medicine, Baltimore, MD, USA.

Studies in health technology and informatics

|August 23, 2024

概括

检索增强生成 (RAG) 可以通过使用大语言模型 (LLM) 来帮助精确瘤学快速找到癌症治疗信息. 这种人工智能方法成功地从可信数据中重现了超过80%的治疗关系.

科学领域:

在瘤学瘤学.
人工智能的人工智能
生物信息学是一种生物信息学.

背景情况:

精确瘤学依赖于对治疗决策的广泛文献的专家审查.
手动文献审查是耗时的,并且可能是临床实践中的瓶.
生成型人工智能为简化信息检索提供了潜在的解决方案.

研究的目的:

评估提取增强生成 (RAG) 在支持精密瘤治疗讨论中的有效性.
评估未经训练的大型语言模型 (LLM) 使用RAG. 提取治疗关系的能力.
为了确定RAG是否可以减少涉及证据基础治疗选择的劳动力.

主要方法:

实施了RAG管道,将出版物中的相关文本块提供给大型语言模型 (LLM).
该系统使用可信数据源 (OncoKB) 来获取癌症治疗信息.
一个现成的,未经训练的Llama 2模型被用简单的问题查询,以测试信息检索能力.

主要成果:

该RAG管道成功地从OncoKB数据源中检索了治疗关系.
未经训练的Llama 2模型重现了已知治疗关系的80%以上.
这表明了RAG在没有模型微调的情况下有效提取信息的潜力.

结论:

检索增强生成 (RAG) 通过加速获得治疗证据来提高精确瘤学的显著前景.
与RAG集成的LLM可以通过利用精选的科学文献有效地回答临床问题.
这种人工智能驱动的方法有可能优化癌症护理中的临床决策.

关键词:

大型语言模型精确瘤学精确瘤学获取 - 增强后代的获取

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