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), Bethesda, USA.
Nature communications
|November 18, 2024
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中的一个主要瓶.
- 有效地将患者与合适的试验相匹配,对于研究进步至关重要.
- 目前用于患者试验匹配的方法通常耗时且劳动密集.
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.
- 开发和评估一个系统,自动化和提高准确性,识别符合条件的临床试验患者.
- 评估TrialGPT在减少患者招募查时间方面的效率和有效性.
主要方法:
- 开发了TrialGPT,一个包含三个模块的框架:TrialGPT-Retrieval用于大规模试验过,TrialGPT-Matching用于标准级患者资格预测,TrialGPT-Ranking用于生成试验级分数.
- 对三个合成患者队列的框架进行了评估,并提供了广泛的试验注释.
- 对患者标准对进行手动评估,并进行了一项用户研究,以评估性能和对查时间的影响.
主要成果:
- 试验GPT-Retrieval回忆了90%以上的相关试验,而处理的初始收集不到6%.
- TrialGPT-Matching在预测患者资格方面实现了87.3%的准确性,其解释与专家的表现相当.
- 试验GPT-排名得分与人类判断有很高的相关性,在排名和排斥任务中表现比竞争模型高43.8%.
- 一项用户研究表明,TrialGPT减少了42.6%的查时间.
结论:
- TrialGPT提供了一种强大而高效的解决方案,用于零射击患者与试验的匹配.
- 该框架显著提高了确定适合患者的临床试验的准确性和速度.
- 在临床研究中,TrialGPT为优化患者招募提供了有前途的进展.
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