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系统审查中的选自动化:工具及其机器学习能力的分析

Elias Sandner^1,2, Christian Gütl², Igor Jakovljevic^1,2

¹IT-Department, CERN, Geneva, Switzerland.

Studies in health technology and informatics

|April 29, 2024

概括

机器学习 (ML) 工具可以显著减少系统审查中的人类工作量,特别是在耗时的标题和摘要选阶段. 为了谨慎应用这些自动化系统,需要进一步的研究.

科学领域:

* 信息科学信息科学
* 医疗信息学医疗信息学
* 医学审查自动化

背景情况:

*系统审查对于基于证据的实践至关重要,但需要大量的劳动力.
* 标题和摘要选阶段在系统审查中是一个重要的瓶.
* 数字工具,特别是机器学习,提供了简化这一过程的潜在解决方案.

研究的目的:

* 评估机器学习 (ML) 在支持系统审查的标题和摘要选阶段的作用.
* 在系统性审查中分析ML驱动的自动化当前状态.
* 确定有前途的ML工具和未来的研究方向.

主要方法:

*进行了系统的文献审查,以确定相关研究.
* 分析了五种有前途的机器学习工具,以确定它们在减少工作量和机器学习应用中的有效性.
* 详细介绍了当前的自动化能力和研究成果.

主要成果:

* 机器学习工具在减轻系统审查所需的人力劳动方面表现有前途.
*分析的重点是工具减少工作负载的能力及其特定的ML方法.
* 该研究强调了现有的自动化能力和关键研究发现.

结论:

* 机器学习为提高系统审查效率提供了巨大的潜力.
*需要进一步的研究来完善和验证 ML 在这个领域的应用.
*在系统性审查中,建议对当前ML系统的实施采取谨慎的方法.

关键词:

机器学习机器学习研究工具研究工具系统审查自动化自动化文字分类文本分类标题和摘要的选

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