机器学习可以优化医学指南开发中的文献选
Wouter Harmsen1, Janke de Groot1, Albert Harkema2
1Knowlegde Institute for the Federation of Medical Specialists, Utrecht, The Netherlands.
Systematic reviews
|July 11, 2024
概括
积极学习工具可以通过改善文献选来加快医疗指南的制定速度. 然而,它们的性能取决于精确的人类输入,因为噪音标签导致机器学习结果不可靠.
科学领域:
- 医疗信息学 医疗信息学
- 基于证据的医学基于证据的医学.
- 机器学习 机器学习
背景情况:
- 医学证据的指数增长需要在准则开发中使用高效的文献选择方法.
- 目前用于证据选择的手动流程是劳动密集型的,可能会阻碍及时更新医疗指南.
研究的目的:
- 评估积极学习 (AL) 的性能和可行性,以支持为医学指南制定选择相关出版物.
- 在这种情况下,研究噪音标签对AL有效性的影响.
主要方法:
- 采用混合方法设计,评估临床医生在14个搜索中对手册文献的选择.
- 模拟将随机阅读与基于AL的查优先级进行比较.
- 绩效指标包括工作节省超过采样在95%的回忆 (WSS@95) 和相关记录的百分比发现在10%的读数 (RRF@10).
- 使用平均发现时间 (ATD) 来识别潜在的噪音标签,并在反射对话中讨论准确性.
主要成果:
- 临床医生的手动标题摘要选择显示了中度的评审者间可靠性 (平均卡帕=0.50).
- 积极学习显示了效率的提高,WSS@95的全文包含率高达75.76%,明显超过手动方法.
- 随着标签噪声的增加,AL的性能恶化,突出显示了人类评分器准确性的影响.
结论:
- 积极学习工具在加速医学指导方针的文献选方面表现有前途.
- AL的有效性严重依赖于人类输入的质量; "杂的标签使杂的机器学习.
- 准则开发人员和研究人员必须确保准确的标签,以最大限度地发挥AL工具的好处.
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