快速临床证据探索器:一个生成预训练过的变压器驱动工具,用于自动化瘤学证据提取
Eunyoung Im1, Bomi Kim1,2, Sunghoon Kang3
1College of Nursing, Seoul National University, Seoul, Republic of Korea.
JCO clinical cancer informatics
|December 5, 2025
概括
快速临床证据探索器 (RaCE-X) 使用生成预训练变压器 (GPT) 来自动化文献审查,改进基于证据的研究. 这个工具有效地选摘要,提取数据,并可视化趋势,减少临床医生和研究人员的手工工作负担.
科学领域:
- 生物医学信息学 生物医学信息学
- 医疗保健中的人工智能
- 临床研究信息学
背景情况:
- 科学文献的指数增长对临床实践和研究中的证据综合提出了重大挑战.
- 现有的文献审查工具往往缺乏趋势分析的综合功能,需要手动的工作流程.
- 大型语言模型 (LLM) 显示了自动化文献审查的潜力,但需要专门的应用程序来探索临床证据.
研究的目的:
- 开发和评估快速临床证据探索器 (RaCE-X),一个使用生成预训练变压器 (GPT) 技术的自动化管道.
- 简化抽象查,结构化信息提取和临床文献研究趋势的可视化过程.
- 评估RaCE-X在促进有效获取临床相关证据方面的性能和可用性.
主要方法:
- 使用GPT-4.1 mini对865篇PubMed摘要进行了预定义标准的选,确定了87篇相关文章.
- 对相关摘要进行了结构化信息提取,使用九个领域信息模型,使用性能评估的黄金标准数据集.
- 可用性通过研究后系统可用性问卷 (PSSUQ) 和临床研究协调员的定性反来评估.
主要成果:
- 在抽象查 (F1 = 0.971) 和信息提取 (F1 = 0.983) 中,RaCE-X实现了高性能,没有发现幻觉.
- 可用性测试产生了积极的用户反,低的整体PSSUQ得分为2.8,用户发现该工具是直观的.
- 交互式仪表板有效可视化了提取的信息,有助于解释研究趋势.
结论:
- RaCE-X提供了基于GPT的高效解决方案,用于抽象查,数据提取和趋势分析,加速生物医学证据的合成.
- 该研究证实了利用LLMs的可行性,以显著减少基于证据的研究中的手工工作.
- RaCE-X通过促进更快地识别和总结相关临床研究结果,支持增强基于证据的实践.
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