学习将患者与临床试验相匹配,使用大型语言模型
Maciej Rybinski1, Wojciech Kusa2, Sarvnaz Karimi1
1CSIRO, Data61, 26 Pembroke Rd, Marsfield, 2122, NSW, Australia.
Journal of biomedical informatics
|October 10, 2024
概括
大型语言模型 (LLM) 通过增强检索管道中的语义分析,显著改善了患者对临床试验的匹配. 虽然基于LLM的重新排名显示出卓越的有效性,但它增加了计算成本,需要平衡实际应用.
科学领域:
- 信息检索 信息检索
- 自然语言处理自然语言处理.
- 临床信息学 临床信息学
背景情况:
- 为临床试验 (CTs) 招募患者往往是低效的.
- 传统的信息检索方法与患者试验匹配的语义复杂性作斗争.
- 大型语言模型 (LLM) 提供先进的语义处理能力.
研究的目的:
- 调查LLM在提高患者对临床试验匹配方面的有效性.
- 将基于LLM的方法与信息检索管道中的传统方法进行比较.
- 分析有效性和计算成本之间的权衡.
主要方法:
- 使用了多阶段的检索管道,包括BM25,基于变压器的排名和基于LLM的方法.
- 雇佣的TREC临床试验2021-23轨道收集用于评估.
- 在查询制定,过,排名和重新排名中比较LLM集成.
主要成果:
- 基于LLM的系统,特别是精细调整的重新排名,在nDCG和Precision中表现优于传统方法.
- 微调的LLM提高了他们识别合格试验的能力.
- 在TREC挑战中,LLM方法通过最先进的系统实现了竞争性表现.
- 使用LLM观察到计算成本增加和效率降低.
结论:
- 临床临床临床试验 (LLM) 在自动化和改进患者对临床试验的匹配方面显示出显著的前景.
- 需要进一步的研究来优化计算成本和检索效率之间的平衡.
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