复习学习:对隐私的真实世界的验证,在医疗机构中保持持续的学习
Jaesung Yoo1, Sunghyuk Choi2, Ye Seul Yang3
1School of Electrical Engineering, Korea University, Republic of Korea.
Computers in biology and medicine
|May 8, 2025
概括
复习学习 (RevL) 通过生成数据样本来保留知识来打击隐私保护深度学习中的灾难性遗忘. 这种持续学习方法显著改善了使用电子健康记录的诊断预测.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 医疗信息学 医疗信息学
背景情况:
- 经过顺序训练的深度学习模型遭受灾难性的遗忘,失去先前的知识.
- 这是使用转移学习 (TL) 保护隐私的深度学习 (PPDL) 面临的重大挑战.
- 有效的知识保留对于医疗应用中的各种数据集至关重要.
研究的目的:
- 介绍Review Learning (RevL),一个低成本的持续学习算法.
- 在PPDL框架内使用电子健康记录 (EHR) 应用RevL进行诊断预测.
- 评估RevL在保留知识和提高绩效方面的有效性.
主要方法:
- RevL从模型中生成合成数据样本,以审查先前学到的知识.
- 该算法通过六个模拟和一个现实世界的机构实验进行了验证.
- 实验使用电子健康记录数据上的二进制分类任务.
主要成果:
- 在对106,508名患者进行的现实实验中,RevL实现了0.710.10的受体操作曲线 (AUC) 下的全球平均面积.
- 仅转移学习 (TL) 实现了0.655.5的AUC.
- 与标准TL相比,RevL表现出优越的知识保留能力.
结论:
- 复习学习 (RevL) 有效地减轻了PPDL的灾难性遗忘.
- 使用私人EHR数据,RevL在现实世界医疗环境中展示了实际的实用性和有效性.
- 这项工作为PPDL研究提供了一个现实的管道,涉及机构间模型转移.
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