将医学知识图形转化为用于诊断预测的大型语言模型:设计和应用研究
Yanjun Gao1,2, Ruizhe Li3, Emma Croxford2
1Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Denver, CO, United States.
JMIR AI
|February 24, 2025
概括
通过将知识图 (KG) 与用于电子健康记录 (EHR) 的大型语言模型 (LLM) 集成,DR.KNOWS提高了诊断准确性. 这种人工智能系统改善了临床决策支持和诊断推理,优先考虑患者安全.
科学领域:
- 人工智能在医学中的应用
- 临床信息学 临床信息学
- 生物医学数据科学 生物医学数据科学
背景情况:
- 电子健康记录 (EHR) 是至关重要的,但复杂的,有诊断不准确的风险.
- 大型语言模型 (LLM) 是有前途的,但需要在医疗保健中仔细应用.
- 将知识图 (KG) 与LLM集成,可以通过结构化的医疗信息来增强诊断推理.
研究的目的:
- 介绍DR.KNOWS (诊断推理知识图表系统).
- 整合基于统一医疗语言系统的KG与LLMs.
- 通过上下文相关的路径检索,通过EHR数据改进诊断预测.
主要方法:
- 为了嵌入节点,使用了堆图异态网络.
- 采用基于注意力的路径排名器来确定相关的知识路径.
- 评估了DR.KNOWS对现实世界EHR数据集的评估,与基线LLM和QuickUMLS相比.
- 实施了以临床安全指标为重点的人类评估框架.
主要成果:
- DR.KNOWS 改进了诊断概念提取和预测指标.
- 集成KG的LLMs获得了优异的ROUGE-L和概念F1分数.
- 人类评估证实了DR.KNOWS的诊断理性与临床推理一致.
- 通过特定案例的路径选择解决了KG数据偏差.
结论:
- 在临床工作流程中,DR.KNOWS提高了诊断准确性和推理能力.
- 代表了朝着可靠的人工智能驱动的临床决策支持的进展.
- 需要进一步研究KG偏差缓解和概括性.
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