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具有检索增强生成的大型语言模型增强了贝叶斯网络的专家建模,用于临床决策支持

Mario A Cypko^1,2, Muhammad Agus Salim^3,4, Aditya Kumar^3,4

¹Hahn-Schickard-Gesellschaft für angewandte Forschung e.V., 79110, Freiburg, Germany. cypko@informatik.uni-freiburg.de.

International journal of computer assisted radiology and surgery

|November 3, 2025

概括

将大型语言模型与检索增强生成 (LLM-RAG) 集成,简化了贝叶斯网络 (BN) 建模用于临床决策支持. 这种人工智能方法提高了效率和准确性,同时减少了临床医生的工作量.

科学领域:

医疗保健中的人工智能
临床决策支持系统临床决策支持系统
生物信息学和计算生物学

背景情况:

贝叶斯网络 (BNs) 提供透明和可解释的模型,对于临床决策支持至关重要.
传统的BN建模是劳动密集型的,需要大量的手工努力和专业知识.
提高BN建模的效率和准确性对于更广泛的临床采用至关重要.

研究的目的:

研究大型语言模型 (LLM) 与检索增强生成 (RAG) 的集成,以改进贝叶斯网络 (BN) 建模.
评估LLM-RAG对与BN模型创建相关的效率,准确性和认知工作负载的影响.
评估人工智能辅助的BN建模服务的临床相关性和可用性.

主要方法:

开发一个基于网络的BN建模服务,结合LLM-RAG管道.
利用精心调整的GTE-Large嵌入模型进行知识检索,并通过递归块和查询扩展进行优化.
雇佣了GPT-4和Mixtral 8x7B,分别用于数据解释和建议生成.
通过NASA-TLX与临床医生进行了用户研究,以评估可用性,检索准确性和认知工作负载.

主要成果:

在LLM-RAG管道中,检索准确度 (高达0.9) 和答案相关性得到改善.
优化的检索技术提高了语义块 (0.75至0.85) 的精度,并提高了响应忠实度 (≥0.9).
临床医生在一小时内成功创建了全面的BN模型,该工具减少了认知工作负载 (2/7 NASA-TLX).
虽然直观,但系统偶尔会在遵守预定义的因果结构方面扎,并发现了一些轻微的技术问题.

结论:

集成LLM-RAG显著提高了贝叶斯网络建模的效率和准确性.
未来的工作应该专注于自动化预处理,UI改进,并通过验证和外部数据源扩展RAG.
生成型人工智能为在临床环境中推进专家驱动的知识建模提供了一个有前途的途径.

关键词:

贝叶斯网络是一个贝叶斯网络.医疗保健中的因果模型.临床决策支持系统临床决策支持系统知识工程知识工程知识工程大型语言模型.提取增强生成的提取.

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