加强系统审查:对积极学习参数组合对生物医学抽象查影响的深入分析
Regina Ofori-Boateng1, Tamy Goretty Trujillo-Escobar2, Magaly Aceves-Martins3
1School of Computing, Robert Gordon University, Aberdeen, AB10 7GE, Scotland, UK.
Artificial intelligence in medicine
|September 28, 2024
概括
积极学习 (AL) 通过优化抽象选来简化系统审查 (SRs). 本研究揭示了AL策略,如初始培训集大小和查询方法,如何影响SR自动化效率.
科学领域:
- 信息科学 信息科学 信息科学
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
背景情况:
- 系统性审查 (SR) 对基于证据的医疗保健至关重要,但耗时且受到越来越多的文献挑战.
- 人工智能 (AI) 技术,特别是主动学习 (AL),为自动化SR流程提供解决方案,特别是抽象选.
- 对于SRs的现有AL软件缺乏明确的理解,即参数变化如何影响疗效.
研究的目的:
- 调查各种主动学习 (AL) 策略对系统审查 (SRs) 自动化的影响.
- 探索特定的AL参数,包括初始培训集大小和查询策略,如何影响SR效率.
- 为SR抽象选和其他应用程序优化AL提供实用见解.
主要方法:
- 使用五个复杂的医疗SR数据集进行实验评估.
- 在SRs的抽象选阶段使用了主动学习 (AL) 技术.
- 采用了通用线性模型 (GLM) 来统计解释发现.
主要成果:
- 确定了AL变量的显著影响,例如特征提取器,初始训练大小和SR自动化分类器.
- 证明特定的AL策略产生了值得注意的观察和实际结论.
- 提供了关于不同AL方法在SR中的疗效的统计解释结果.
结论:
- 关键的AL变量明显影响系统审查自动化的效率和有效性.
- 这些发现为优化SR抽象选中AL部署提供了实际指导.
- 这项研究揭开了AL参数的影响,对SR和更广泛的AI应用产生了影响.
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