将健康数据模型的分散学习方法与非分散的替代方法进行比较:系统审查协议
José Miguel Diniz1,2, Henrique Vasconcelos1, Júlio Souza1,3
1CINTESIS-Centre for Health Technology and Services Research, Faculty of Medicine, University of Porto, Porto, Portugal.
分散学习模式为医疗保健提供了有希望的隐私保护解决方案,使数据驱动干预能够在不损害患者保密的情况下实现. 这一系统性审查将它们的性能与传统方法进行比较,指导未来的应用.
科学领域:
- 医疗信息学 医疗信息学
- 机器学习 机器学习
- 数据 隐私 数据 隐私 数据
背景情况:
- 医疗保健成本上升和人口老龄化需要基于数据的干预措施.
- 传统的数据挖掘需要大量的数据集,这带来了隐私挑战和复杂的法律合规问题.
- 分散学习 (DL) 可以在没有数据调动的情况下创建健康模型,解决隐私问题.
研究的目的:
- 用去中心化学习 (例如,联合学习,区块链) 与集中或本地方法开发的健康数据模型的性能进行比较.
- 评估不同分散式学习模型架构的隐私妥协和资源利用情况.
- 综合关于在医疗保健中应用隐私保护技术的证据.
主要方法:
- 在注册研究协议 (PROSPERO 393126) 之后进行系统审查.
- 在生物医学和计算数据库中进行全面搜索.
- 使用CHARMS和PROBAST工具进行数据提取和偏差评估,报告所有影响措施.
主要成果:
- 数据提取和分析计划于2023年2月28日至2023年7月31日进行.
- 该审查将总结医疗保健中最先进的DL模型,并将其与本地和集中方法进行比较.
- 预期的结果将澄清共识和异质性,指导未来对隐私保护健康应用的研究.
结论:
- 该审查将介绍医疗保健中隐私保护技术的现状.
- 调查结果将为专业人士,数据科学家和决策者提供健康技术评估和基于证据的决策信息.
- 该研究旨在指导开发和应用新的工具,以加强患者隐私和未来的研究.
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