从算法创新到临床部署:对限制医疗保健中联合学习的方法差距进行系统审查
1Department of Biotechnology, PES University, Ring Road Campus, BSK III Stage, Bangalore 560085, Karnataka, India.
International journal of medical informatics
|March 5, 2026
概括
联合学习 (FL) 通过实现分布式模型培训而无需数据集中,从而推动生物医学信息学. 然而,目前的FL方法需要在评估和治理方面进行改进,以获得完全的翻译准备.
科学领域:
- 生物医学信息学是生物医学信息学.
- 机器学习是机器学习.
- 数据科学是数据科学.
背景情况:
- 联合学习 (FL) 是一种分布式机器学习方法,可以在分散的数据中进行模型训练,而不会集中敏感信息.
- 由于数据隐私问题和健康数据的分布性质,FL在生物医学信息学中越来越重要.
研究的目的:
- 批判性地审查联合学习作为生物医学信息学中的方法方法.
- 总结FL的概念基础,变体,成功和局限性.
- 确定 FL 在该领域的未来研究方向.
主要方法:
- 按照PRISMA 2020指南进行系统的方法审查.
- 搜索了主要的科学数据库 (PubMed,科学网,Scopus,IEEE Xplore,谷歌学者) 来查找2017年1月至2025年3月的研究.
- 包括建议,扩展或分析生物医学/生命科学数据的FL方法的文章,根据拓,优化,异质性,隐私,评估和翻译准备性对其进行分类.
主要成果:
- 97篇文章满足了来自8412多个记录的列入标准.
- 在FL优化和隐私增强技术中观察到重要的方法创新.
- 在支持设计时间评估和治理的FL方法中发现了有限的进展.
结论:
- 联合学习为生物医学信息学提供了实质性的方法学进步.
- 目前FL的实施部分解决了翻译方面的挑战,突出了方法论与现实应用之间的差距.
- 未来的研究必须将研究设计,可靠的评估,可解释性和治理纳入FL方法,以提高翻译准备.
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