解决重建系统审查数据集的挑战:一个案例研究和一个噪音标签过程序
Rutger Neeleman1, Cathalijn H C Leenaars2, Matthijs Oud3
1Department of Methodology and Statistics, Faculty of Social and Behavioral Sciences, Utrecht University, Utrecht, The Netherlands.
Systematic reviews
|February 17, 2024
概括
本研究重建了边界性人格障碍治疗的数据集,以评估系统审查中的机器学习. 积极学习显著减少了82.30%的选时间,提高了效率.
科学领域:
- 医疗信息学 医疗信息学
- 精神病学是一个精神病学.
- 机器学习 机器学习
背景情况:
- 系统审查和元分析是耗时的.
- 机器学习 (ML) 可以提高选效率.
- 完全标记的数据集对于在系统审查中评估ML模型至关重要.
研究的目的:
- 创建一个全面的数据集,对边界性人格障碍治疗进行系统审查.
- 评估积极学习在重建查数据中的效率.
- 评估ML在加速系统审查过程中的潜力.
主要方法:
- 来自Oud等人复制的搜索策略. (2018) 用于边界性人格障碍治疗.
- 实施了噪音标签过器 (NLF) 程序,使用主动学习来验证选标签.
- 使用重建的数据集进行了模拟研究.
主要成果:
- 为了系统审查,重新构建了一个全面的数据集.
- 与随机阅读相比,主动学习减少了82.30%的查时间.
- 在NLF程序后没有发现任何额外的相关记录.
结论:
- 积极学习是重建查数据集和提高系统审查效率的可行方法.
- 该研究强调了数据可用性对于ML模型评估的重要性.
- 为数据集重建提供了建议,并引入了决策树.
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