从RCT报告中与LLMs共同提取干预,结果和发现
Somin Wadhwa1, Jay DeYoung1, Benjamin Nye2
1Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA.
Proceedings of machine learning research
|September 24, 2025
概括
本研究引入了一种使用大型语言模型 (LLM) 的新人工智能模型,自动从临床试验报告中提取干预,结果和比较器等关键信息,改进基于证据的护理.
科学领域:
- 医疗信息学 医疗信息学
- 自然语言处理自然语言处理.
- 临床试验分析
背景情况:
- 随机对照试验 (RCT) 对于确定干预有效性和为基于证据的护理提供信息至关重要.
- 从非结构化临床试验报告中提取结构化数据是临床医生手动,耗时的过程.
- 自动提取关键试验元素对于有效的证据合成至关重要.
研究的目的:
- 从临床摘要中自动提取干预,结果和比较器 (ICO元素) 开发和评估使用调整指令的大型语言模型 (LLM) 的文本对文本模型.
- 推断临床试验文章中报告的相关结果.
- 提高从科学文献中提取证据的效率和准确性.
主要方法:
- 利用调整指令的大型语言模型 (LLM) 来构建文本对文本模型,以共同提取ICO元素和相关结果.
- 将证据提取作为条件生成任务.
- 进行手动 (专家) 和自动化评估,以评估模型性能与以前的最先进技术 (SOTA) 相比.
主要成果:
- 拟议的基于LLM的模型取得了显著的改进,比以前的SOTA大约增加了20个绝对F1分数.
- 评估表明,将证据提取作为微调LLM的条件生成任务框架的有效性.
- 进行了废弃研究和错误分析,以了解模型性能驱动因素,并确定未来改进的领域.
结论:
- 开发的基于LLM的方法大大提高了从临床试验摘要中自动提取结构化证据的能力.
- 这种方法提供了一种更有效,更准确的方法来综合RCT的发现,支持基于证据的医学.
- 已创建并发布了一个可搜索的RCT结构化发现数据库,直到2022年中期,以方便获取合成试验结果.
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