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从非结构化数据中获得结构化洞察力:用于SDOH驱动的糖尿病风险预测的大型语言模型.

Sasha Ronaghi, Prerit Choudhary, David H Rehkopf

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |December 3, 2025
    PubMed
    概括

    大型语言模型 (LLM) 可以从患者故事中提取健康的社会决定因素 (SDOH),改善糖尿病管理. 这种方法通过分析非结构化的生活经验来增强风险预测模型.

    科学领域:

    • 医疗信息学 医疗信息学
    • 人工智能在医学中的应用
    • 健康研究的社会决定因素研究

    背景情况:

    • 健康的社会决定因素 (SDOH) 显著影响2型糖尿病 (T2D) 管理,但在临床数据中经常缺失.
    • 目前用于SDOH的结构化查工具缺乏深度以捕捉患者的复杂性.

    研究的目的:

    • 探索使用大型语言模型 (LLM) 来从非结构化的患者叙述中提取结构化的SDOH数据.
    • 评估LLM提取的SDOH特征和糖尿病控制患者叙述的预测能力.
    • 将叙事数据集成到传统的风险预测模型中.

    主要方法:

    • 收集了65名T2D患者 (65岁以上) 的非结构化生活故事采访.
    • 利用LLM与检索增强生成进行定性总结和定量SDOH评级.
    • 在机器学习模型 (Ridge,Lasso,Random Forest,XGBoost) 中应用了结构化的SDOH评级和实验室生物标志物.
    • 从面试文本 (A1C编辑) 直接预测糖尿病控制水平的LLM准确性进行评估.

    主要成果:

    • 从患者叙述中,LLMs成功地提取了结构化的SDOH信息.
    • 来自LLM的SDOH评级和叙事数据显示了糖尿病控制的预测价值.

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  • 在预测糖尿病控制水平方面,LLMs仅从采访文本中获得了60%的准确性.
  • 结论:

    • LLM提供了一种可扩展的方法,将非结构化的SDOH数据转化为可操作的临床见解.
    • 整合LLM处理的叙事数据可以增强现有的临床风险预测模型.
    • 这种方法增强了对T2D管理中的患者体验和社会背景的理解.