在接受治疗的中枢神经系统癌症患者中,使用大型语言模型在电子健康记录中进行可扩展的症状跟踪
John Y Rhee1,2,3,4, Zachary Tentor2,3, Thomas Sounack2
1Center for Neuro-Oncology, Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA.
Neuro-oncology
|September 19, 2025
概括
大型语言模型 (LLM) 从电子健康记录 (EHR) 中准确地提取患者的症状,从而实现可扩展的现实世界症状负担分析. 这种方法揭示了与临床试验相比,现实世界队列中的症状流行率更高.
科学领域:
- 医疗保健中的计算语言学
- 临床信息学是一种临床信息学.
- 瘤学研究的研究.
背景情况:
- 大型语言模型 (LLM) 提供可扩展的解决方案,用于使用电子健康记录 (EHR) 追踪患者的症状.
- 在临床试验和营销后监测中,验证基于LLM的从EHR中提取症状至关重要.
研究的目的:
- 为了验证基于LLM的从EHR中提取症状的准确性.
- 为了确定特定癌症治疗的现实世界症状流行率.
主要方法:
- 在499个神经瘤学临床笔记中注释了GPT-4o的症状,在专家评价中达到0.97的平均灵敏度和特异性.
- 经过验证的LLM被应用于来自1642名患者的51541条笔记,以评估temozolomide,bevacizumab,lomustine,免疫检查点抑制剂 (ICI) 和甲基酸盐的症状患病率.
主要成果:
- 疲劳是最常见的症状 (83%的患者).
- 接受免疫检查点抑制剂 (ICI) 的患者报告了最多的症状 (平均值=4.68),而甲状腺素患者报告的症状最少 (平均值=2.92).
- 在现实世界中,症状发病率往往超过了类似治疗的临床试验中报告的情况.
结论:
- LLM能够从健康记录中进行可扩展的症状提取,这对于理解症状负担至关重要.
- 这项技术对于在现实世界患者队伍中支持与症状相关的干预措施和研究至关重要.
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