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使用切换分类模型进行基于主动学习的系统审查:抑郁障碍的发病,维持和复发的情况
Jelle Jasper Teijema1, Laura Hofstee1, Marlies Brouwer2
1Department of Methodology and Statistics, Faculty of Social and Behavioral Sciences, Utrecht University, Utrecht, Netherlands.
积极学习辅助系统审查从模型切换策略中受益. 从更轻的模型开始并切换到更重的模型,与使用单个默认模型相比,可以提高审查性能.
科学领域:
- 信息科学 信息科学 信息科学
- 计算机科学 计算机科学
- 医疗信息学 医疗信息学
背景情况:
- 系统审查对于基于证据的实践至关重要.
- 积极学习 (AL) 可以提高系统审查的效率.
- 深度学习 (DL) 模型为AL辅助的系统审查提供了潜力.
研究的目的:
- 用DL模型评估AL辅助系统审查的性能.
- 将DL方法与传统的机器学习 (ML) 方法进行比较.
- 调查模型交换策略对审查效率和准确性的影响.
主要方法:
- 实施了一个卷积神经网络 (CNN) 分类器用于系统性审查.
- 分析了AL辅助系统审查工作流程的性能和稳定性.
- 对比了不同分类器和特征提取器的性能.
- 通过在不同审查阶段比较较轻型和较重型的模型,研究了模型切换策略.
主要成果:
- 更轻的ML模型在早期审查阶段表现出强的表现.
- 更重的DL模型在审查过程的后期阶段显示出更好的性能.
- 模型切换策略通常比单模型方法提高了审查绩效.
结论:
- 模型切换策略对于AL辅助的系统审查工作流程是有益的.
- 建议使用更简单的模型 (例如,天真贝叶斯,逻辑回归) 开始审查.
- 建议在必要时转向更复杂的模型,以启发式规则为指导.
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