用知识图增强的大型语言模型重建生命过程风险路径:一个妊娠期糖尿病到痴呆症的案例研究
Shuang Wang1, Yang Zhang2, Ying Gao2
1National Institute of Health Data Science, Peking University, Beijing 100191, China.
Journal of the American Medical Informatics Association : JAMIA
|December 18, 2025
概括
这项研究开发了一种知识图增强的大型语言模型 (LLM),用于绘制终身暴露结果路径,确定妊娠糖尿病 (GDM) 和痴呆症之间的108个调解者.
科学领域:
- 流行病学 流行病学
- 人工智能的人工智能
- 医疗信息学 医疗信息学
背景情况:
- 对终身暴露结果路径综合复杂的流行病学证据是具有挑战性的.
- 现有的方法难以整合碎片化的文献来推断渐进风险.
- 孕期糖尿病 (GDM) 和痴呆症是理解生命过程风险的关键案例研究.
研究的目的:
- 开发和评估一个知识图增强的大型语言模型 (LLM) 框架.
- 推断终身暴露结果路径,特别是将GDM与痴呆症联系起来.
- 通过因果知识图和LLMs来增强流行病学证据综合.
主要方法:
- 从科学文献中的经验流行病学关联构建了一个因果知识图.
- 将知识图与GPT-4集成使用四种图形检索增强生成 (GRAG) 策略.
- 评估了使用语义三重组,人类专家和基于LLM的审查员对可靠性,新性和临床相关性的GRAG策略.
主要成果:
- 该知识图增强的LLM识别了108个GDM和痴呆症之间的母亲候选调解者.
- 在推断桥梁变量方面,GRAG策略显著优于基线LLM方法.
- 与标准的LLM输出相比,结构化方法提高了准确性,减少了编造.
结论:
- 通过流行病学知识图表来增强LLM,有助于对碎片化的文献进行推理,以重建风险路径.
- 人与人工智能的合作对于解释和应用LLM衍生的临床相关性至关重要.
- 这一框架为生命周期流行病学提供了一个有希望的方法,有助于早期检测和研究设计.
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