从现实数据中提取TNFi切换原因和轨迹,使用大型语言模型
Brenda Y Miao1, Marie Binvignat1,2,3, Augusto Garcia-Agundez4
1Bakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, CA 94143, United States.
JAMIA open
|November 14, 2025
概括
大型语言模型 (LLM) 可以自动审查电子健康记录,以识别瘤亡因子抑制剂 (TNFi) 切换模式和切换原因.
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
- 现实世界的证据生成
背景情况:
- 瘤亡因子抑制剂 (TNFi) 对于治疗炎症状况至关重要.
- 了解TNFi切换模式对于优化患者护理至关重要.
- 对治疗变化的手动图表审查是耗时和资源密集的.
研究的目的:
- 评估大型语言模型 (LLM) 能够自动识别TNFi治疗切换的能力.
- 使用基于LLM的图表审查来确定TNFi交换机背后的原因.
- 将高级LLM与开源模型和传统方法的性能进行比较.
主要方法:
- 一项观察性研究使用了从2012年到2023年的非识别电子健康记录 (EHR) 数据.
- 使用 GPT-4 来从临床笔记中提取 TNFi 治疗开关和原因.
- 绩效与八个开源LLM,结构化EHR数据和专家注释进行了对比.
主要成果:
- 在识别停止药物 (微F1=0.75),开始药物 (0.80) 和转换原因 (0.83) 方面,GPT-4表现出强的表现.
- 像Starling-7B-beta和Llama-3-8B这样的开源模型显示出具有竞争力的结果.
- 转换TNFi的主要原因是缺乏疗效 (56.9%),不良事件 (13.5%) 和保险/成本 (10.8%).
结论:
- 包括GPT-4和可部署的开源模型在内的LLM可以有效地从临床笔记中提取复杂的治疗信息.
- 使用LLM的自动图表审查可促进对现实数据的可扩展分析.
- 这项技术支持治疗模式的增强现实世界证据生成.
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