TheraMind:一个多LLM组合,通过案例报告挖掘加速药物重用在肺癌中
Vrushket More1, Lyra Lu2, Zeyu Ding1
1School of Computing, Watson College of Engineering & Applied Science, Binghamton University, Binghamton, NY, USA.
NPJ precision oncology
|January 28, 2026
概括
一个人工智能系统,TheraMind,从临床病例报告中有效地识别了非小细胞肺癌 (NSCLC) 的药物重用机会. 这种方法加速了使用现实世界的证据发现新的癌症疗法.
科学领域:
- 人工智能在医学中的应用
- 在瘤学瘤学.
- 药理学 药理学是指药理学的学科.
背景情况:
- 临床病例报告是药物重新用途的未充分利用的证据来源.
- 由于文献量和复合多样性,对相关报告的系统识别具有挑战性.
研究的目的:
- 开发和评估TheraMind,一种使用大型语言模型 (LLM) 的AI系统,用于自动识别和分析非小细胞肺癌 (NSCLC) 药物重用病例报告.
- 评估基于LLM的方法在从临床文献中挖掘现实世界的证据的有效性.
主要方法:
- 对18种候选药物进行了1023份PubMed索引病例报告的选.
- 使用协调数据提取与标准化的四个问题提示.
- 采用基于规则的分类器,单一模型验证器和LLM的多数投票组合 (GPT-40-mini,Gemini-2.0-Flash,LLaMA-3-8B).
主要成果:
- 该LLM组合在检测临床相关报告方面实现了92%的回忆率和99.7%的特异性.
- 结构化的输出包括患者人口统计,治疗反应和病例摘要.
- 展示了一个可扩展的LLM驱动的框架,用于挖掘非结构化临床文献.
结论:
- TheraMind提供了一种可扩展的解决方案,以加速对NSCLC的药物重新定位.
- 临床病例报告的LLM驱动分析有效地提取现实世界的证据.
- 这一框架加强了对潜在的新药指示的系统识别.
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