系统审查的机器学习方法:快速范围审查
Stephanie Roth1, Alex Wermer-Colan2
1Medical Librarian, Lewis B. Flinn Medical Library, ChristianaCare.
Delaware journal of public health
|January 4, 2024
概括
机器学习和自然语言处理通过提高搜索精度和数据抽象来增强医疗系统性审查. 这些人工智能工具加速了证据合成,使研究人员和决策者受益.
科学领域:
- 医学研究 医学研究
- 计算机科学 计算机科学
- 数据科学数据科学数据科学
背景情况:
- 自然语言处理 (NLP) 是机器学习的一个子领域,它分析文本数据.
- NLP对综合来自各种生物医学研究的数据做出了重大贡献.
- 本综述探讨了NLP在优化医学研究中的系统性审查中的作用.
研究的目的:
- 调查机器学习对系统审查方法论的影响.
- 用ML评估搜索策略,文章选择和数据抽象方面的改进.
主要方法:
- 采用了快速范围审查方法.
- 在多个数据库中进行了全面的文献搜索,包括灰色文献和手动搜索.
- 搜索于2023年4月14日结束.
主要成果:
- 最初的搜索确定了23,190项研究.
- 在删除重复后,117项研究 (1.70%) 符合审查的纳入标准.
结论:
- 正在开发机器学习方法,以协助系统审查的所有阶段.
- 将ML工具与人类专业知识相结合,可以提高效率并加快证据生成.
- 通过改进的系统审查,更快地传播证据,支持及时决策.
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