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评估积极学习策略,用于医院患者安全事件报告的自动分类
Shehnaz Islam1, Myrtede Alfred1, Dulaney Wilson2
1Mechanical and Industrial Engineering, University of Toronto, Toronto, ON, Canada.
积极学习 (AL) 显著减少了分类患者安全事件 (PSE) 报告所需的手工工作. 这种方法通过优化机器学习模型的数据标签来提高医疗保健质量改善的准确性和效率.
科学领域:
- 医疗信息学 医疗信息学
- 机器学习在医疗保健中的应用
- 患者安全研究 患者安全研究
背景情况:
- 患者安全事件 (PSE) 报告对于改善医疗保健质量,趋势分析和组织学习至关重要.
- 由于大量和复杂的分类学,PSE报告的手动分类是劳动密集型的.
- 机器学习 (ML) 用于PSE报告分类需要大型,手动标记的数据集,这构成了重大瓶.
研究的目的:
- 调查主动学习 (AL) 策略在简化PSE报告标签过程中的有效性.
- 为了减少与创建基于ML的PSE报告分类的标记数据集相关的手工工作量.
- 通过优化数据注释,提高PSE报告分类的准确性和效率.
主要方法:
- 采用基于池的积极学习 (AL) 采样,以智能选择 PSE 报告用于人类注释.
- 开发了一个强大的数据集,用于培训ML分类器,通过优先考虑AL识别的信息样本来培训ML分类器.
- 将基于AL的标签与各种文本表示的随机抽样的性能进行比较.
主要成果:
- 积极学习 (AL) 在分类准确性方面显著超过随机抽样.
- 通过AL策略,对标记样品的要求降低了24%至69%,显示出显著的效率提升.
- 开发的方法保持了高的分类准确性,同时最大限度地减少了手动注释的努力.
结论:
- 将主动学习 (AL) 集成到 PSE 报告标签工作流程中,可以大大减少手工工作量.
- AL提供了一种有效的方法,用于建立高质量的标记数据集,用于患者安全中的ML模型.
- 该战略支持对患者安全事件进行更有效,更准确的分析,有助于提高医疗保健质量.
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