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关于UMLS在支持大语言模型提出的诊断生成中的作用

Majid Afshar¹, Yanjun Gao¹, Deepak Gupta²

¹University of Wisconsin School of Medicine and Public Health, 750 Highland Ave, Madison, 53726, WI, USA.

Journal of biomedical informatics

|August 14, 2024

概括

大型语言模型 (LLMs) 显示,当加上统一医学语言系统 (UMLS) 知识时,诊断生成得到了改进. 定制UMLS数据和开发更好的评估指标是人工智能驱动医疗保健未来进步的关键.

科学领域:

人工智能在医学中的应用
医疗保健的自然语言处理.
医疗信息学医疗信息学

背景情况:

传统和机器学习诊断系统利用统一医疗语言系统 (UMLS) 的知识.
新兴的大型语言模型 (LLM) 提出了关于它们固有的医学知识和对外部来源的需求的问题.
评估LLM诊断能力和外部医学知识的影响至关重要.

研究的目的:

在流行的法学士课程中探讨与诊断相关的医学知识.
检查用UMLS知识为LLM诊断预测奠定基础的好处.
评估人类判断与基于UMLS的法学士成果指标之间的相关性.

主要方法:

从消费者健康问题和使用消费者QA和问题总结数据集的临床笔记中评估LLM生成的诊断.
通过提示UMLS知识路径完成,探讨了LLM内部知识.
通过将UMLS图路径和临床笔记集成到LLM提示中来评估接地,与基线提示进行比较.

主要成果:

在完成UMLS知识路径方面,GPT-3.5的表现优于Llama2 (F1得分为10.9%).
将LLM预测与UMLS路径接地,提高了这两项任务的性能,特别是SapBERT得分提高了4%.
在标准指标 (ROUGE,SapBERT) 和人类专家判断之间观察到较弱的相关性.

结论:

用UMLS知识增加LLM可以提高诊断生成性能.
定制UMLS数据是必要的,以优化对诊断任务的LLM预测.
开发与人类判断一致的评估指标仍然是一个开放的研究挑战.

关键词:

人工智能的人工智能是人工智能.不同诊断的差异诊断.评估方法的评估方法.知识表示 (计算机)自然语言处理自然语言处理.统一的医疗语言系统.

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