介绍和比较新型去中心化学习方案与多个数据池,以保护隐私的ECG分类
Martin Baumgartner1,2, Sai Pavan Kumar Veeranki2, Dieter Hayn1,3
1Center for Health & Bioresources, AIT Austrian Institute of Technology, Giefinggasse 4, 1210 Vienna, Austria.
Journal of healthcare informatics research
|August 28, 2023
概括
分散的人工智能 (AI) 方法为医疗应用 (如心电图分类) 提供了增强的隐私. 联合学习算法表现出与集中式模型相当的性能,新的方法取得了最佳结果.
科学领域:
- 医疗信息学 医疗信息学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 人工智能 (AI) 和机器学习 (ML) 在各种领域提供了重大创新.
- 由于数据隐私问题和严格的法律法规,AI/ML的医疗应用面临着挑战.
- 分散的基于知识的方法可以通过避免数据集中来缓解隐私问题.
研究的目的:
- 应用和比较6个分散的机器学习算法,用于12导电图ECG分类与传统的集中方法.
- 评估医疗AI中分类性能和隐私保护之间的权衡.
- 确定最佳的去中心化算法,用于医疗保健中的隐私保护人工智能.
主要方法:
- 实现6个不同的去中心化机器学习算法.
- 将这些算法应用于用于分类任务的12导心电图数据集.
- 将分散方法与标准的集中式机器学习模型进行比较.
主要成果:
- 与集中式模型相比,联合学习 (FL) 显示分类性能略有下降 (AUROC -0.054),同时显著提高了隐私.
- 一种加权的FL变体 (AUROC -0.049) 和一个整体方法 (AUROC -0.035) 超过了标准FL性能.
- 与基线相比,一种新的分批顺序学习方案产生了最佳表现 (AUROC -0.036).
结论:
- 分散机器学习,特别是联合学习,为医学的隐私保护人工智能提供了可行的方法.
- 先进的FL技术,包括加权变体和组合方法,可以提高标准FL性能.
- 批量级的顺序学习方案显示出在保护隐私的ECG分类中提供最佳性能的希望.
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