自动识别涉及机器学习医疗设备的安全事件
Ying Wang1, David Lyell1, Enrico Coiera1
1Australian Institute of Health Innovation, Macquarie University, Australia.
Studies in health technology and informatics
|January 25, 2024
概括
自动化文本分类器可以有效地识别罕见的机器学习 (ML) 医疗设备安全事件. 在分类器中使用通用设备类型显著提高了检测这些关键安全事件的准确性.
科学领域:
- 医疗器械安全 医疗器械安全
- 机器学习在医疗保健中的应用
- 监管科学是一种监管科学.
背景情况:
- 机器学习 (ML) 越来越多地用于医疗设备,导致安全问题.
- 目前的安全事件分析依赖于手动的,回顾性的专家审查,这些审查是低效和昂贵的.
研究的目的:
- 在FDA的MAUDE数据库中开发和评估用于识别罕见ML相关安全事件的自动化文本分类器.
- 在分层分类器中评估不同特征集的有效性.
主要方法:
- 开发和测试了四个分层文本分类器,使用不同的特征集:报告文本,设备品牌,通用设备类型和组合信息.
- 用真实世界的数据分布和外部数据集来评估性能来评估分类器.
主要成果:
- 包含通用设备类型的分层分类器表现出最高的有效性,在分层数据上达到85%的F1得分,在外部数据上达到100%的精度.
- 外部数据集中的所有真正积极的ML安全事件都被三个开发的分类器识别出来.
结论:
- 自动化文本分类,特别是使用通用设备类型,为监控向FDA MAUDE系统报告的ML安全事件提供了一种高度有效的方法.
- 这些分类器的整体结果可以直接用于实时监测ML设备安全.
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