数据挖掘与手动选用于选择将论文纳入系统性审查:一种提高效率的新方法
Elena Ierardi1, J Chris Eilbeck2, Frederike van Wijck3
1Department of Occupational Therapy, and Human Nutrition and Dietetics, School of Health and Life Sciences, Glasgow Caledonian University, Glasgow.
概括
一个新的Python算法通过使用计算机辅助的关键词识别来选摘要,显著加快系统审查的速度,与手动方法相比,节省了73%的时间.
科学领域:
- 医疗信息学 医疗信息学
- 系统审查方法论 系统审查方法论
- 计算生物学 计算生物学
背景情况:
- 系统性审查对于基于证据的医学至关重要,但需要大量的时间和资源.
- 手动抽象选是系统审查过程中的瓶.
- 有关研究的有效识别对于及时的知识合成至关重要.
研究的目的:
- 开发和评估用于关键词识别的计算机辅助算法,以简化系统审查中的抽象选.
- 将基于算法的方法的准确性和效率与传统的手动选进行比较.
- 评估算法的适用性和节省时间的潜力,在不同的系统审查背景下.
主要方法:
- 开发了一个Python算法,用于识别摘要中的关键词,以便进行系统的审查.
- 对算法的性能进行了评估,以确定关键字的准确性和抽象的资格评估.
- 使用算法查所需的时间与手动查进行了比较,这是对中风后手臂损伤的示例系统审查.
- 该算法在第二次不同的系统审查中进行了外部验证.
主要成果:
- 该算法表现出高准确度,在示例审查中仅在2.6%的文档中失败.
- 算法和手动方法都确定了相同的610项研究.
- 计算机辅助选将每篇摘要的选时间减少了73%,每篇摘要节省了1.15分钟.
- 外部验证证实了该算法在识别符合条件的研究以进行不同的审查方面的有效性.
结论:
- 专门构建的软件提供了一种准确且大大节省时间的方法,用于识别有资格进行系统审查的摘要.
- 这种新的计算方法可以适应各种系统性审查,使研究人员,评论员和编辑受益.
- 该算法提供了一个可扩展和高效的解决方案,以加速证据合成和知识传播.
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