医疗保健中的联合学习:用于结构化数据分析的工程和统计方法的基准比较
Siqi Li1, Di Miao1, Qiming Wu1
1Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore, Singapore.
统计联合学习 (FL) 算法提供较少偏差的模型系数,而工程FL方法可能会提供稍微更好的预测性能. 这种比较有助于医疗保健FL应用程序选择.
科学领域:
- 医疗信息学 医疗信息学
- 机器学习 机器学习
- 统计建模 统计建模
背景情况:
- 联合学习 (FL) 对医疗保健合作中的数据隐私至关重要.
- 工程学和统计学领域都开发了FL算法,但统计学方法的认可较少.
- 需要对这两个FL领域进行全面的比较.
研究的目的:
- 进行第一个基于工程和统计的联合学习框架的全面比较.
- 用真实世界和模拟数据评估它们在医疗保健应用中的表现.
主要方法:
- 评估了7个FL框架 (工程和统计).
- 将FL方法与本地和集中后勤回归和拉索模型进行比较.
- 利用模拟和现实世界的紧急部门数据进行评估.
主要成果:
- 统计FL算法产生了明显更少偏差的模型系数估计.
- 基于工程的FL方法偶尔会产生具有略高预测性能的模型.
- 工程FL模型有时在预测方面表现优于集中和统计FL模型.
结论:
- 统计FL在系数估计准确度方面表现出色,而工程FL可能提供边际预测优势.
- 根据研究特征提供了选择FL方法的建议.
- 提高对各种FL方法在医疗保健中的认识和整合至关重要.
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