用大型语言模型将患者与临床试验相匹配
Qiao Jin1, Zifeng Wang2, Charalampos S Floudas3
1National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), National Institutes of Health (NIH).
ArXiv
|August 14, 2023
概括
使用大型语言模型的新框架TrialGPT显著改善了患者与试验的匹配. 它有效地确定合适的临床试验,减少了超过40%的招聘查时间.
科学领域:
- 临床信息学 临床信息学
- 人工智能在医学中的应用
- 生物医学数据科学 生物医学数据科学
背景情况:
- 患者招募是临床试验成功的一个主要瓶.
- 有效地将患者与合适的临床试验相匹配仍然是一个重大挑战.
研究的目的:
- 引入TrialGPT,这是一个端到端的框架,用于使用大型语言模型进行零射击患者到试验的匹配.
- 评估TrialGPT在改善临床试验患者招募方面的有效性.
主要方法:
- 开发了三个模块的TrialGPT:TrialGPT-Retrieval用于大规模过,TrialGPT-Matching用于标准级资格预测,TrialGPT-Ranking用于试验级评分.
- 在合成患者队列和现实世界的数据上进行评估,与专家性能和现有模型进行比较.
主要成果:
- TrialGPT-Retrieval回忆了90%以上的相关试验,使用<6%的数据集.
- TrialGPT-Matching通过忠实解释实现了87.3%的准确性.
- 在用户研究中,TrialGPT-Ranking在竞争模型中的表现优于43.8%,选时间缩短了42.6%.
结论:
- TrialGPT展示了一种强大而高效的方法来匹配患者与试验.
- 该框架为加速临床试验招聘和改善患者进入试验提供了重大潜力.
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