机器学习用于在进行系统审查时识别随机对照试验:开发和评估其对实践的影响
Xuan Qin1,2,3, Minghong Yao1,2,3, Xiaochao Luo1,2,3
1Institute of Integrated Traditional Chinese and Chinese Evidence-based Medicine Center and Cochrane China Center and MAGIC China Center, West China Hospital, Sichuan University, Chengdu, China.
Research synthesis methods
|February 2, 2026
概括
一个新的机器学习 (ML) 模型有效地识别了系统审查 (SR) 的随机对照试验 (RCT),大大节省了时间,提高了查的准确性. 这为研究人员加速了证据合成.
科学领域:
- 医疗信息学 医疗信息学
- 临床研究中的人工智能
- 基于证据的医学 基于证据的医学
背景情况:
- 机器学习 (ML) 模型旨在通过识别随机对照试验 (RCT) 来加快系统审查 (SRs).
- 现有的ML模型在性能和实际应用方面存在局限性,这阻碍了在SRs中广泛采用.
- 准确识别RCT对于高效可靠的证据综合至关重要.
研究的目的:
- 开发和评估一个高回忆合体ML模型,以提高SR标题和摘要选期间的RCT识别.
- 评估ML模型对节省劳动时间的实际影响,并回顾SR工作流程的改进.
- 根据审查员的可用性和时间限制,为应用ML辅助选策略提供建议.
主要方法:
- 开发了一种使用Cochrane RCT数据的高回忆合体学习模型.
- 通过使用注释的RCT数据集对模型进行外部验证.
- 通过ML辅助双重查和ML辅助逐步查情景评估实际影响.
主要成果:
- 拟议的ML模型实现了现有SVM模型的两倍精度,同时保持了0.99.9的召回率.
- ML辅助的双重查导致45.4%的劳动时间节省和更好的回忆 (0.998比0.919).
- ML辅助的逐步查节省了74.4%的劳动时间,性能与手动查相当.
结论:
- 开发的ML模型显著减少了工作量,并保持了在SR查中用于RCT识别的可比回忆.
- 这种方法通过提高标题和摘要选阶段的效率来加速SRs.
- 提供了实施ML辅助查以优化SR流程的实际建议.
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