医疗保健中的联合机器学习:对临床应用和技术架构的系统审查
Zhen Ling Teo1, Liyuan Jin2, Siqi Li2
1Singapore National Eye Centre, Singapore, Singapore; Singapore Eye Research Institute, Singapore, Singapore.
Cell reports. Medicine
|February 10, 2024
概括
联合学习 (FL) 增强了健康数据的隐私. 虽然广泛研究,但大多数应用仍然是概念验证,需要有限的现实世界的临床翻译.
科学领域:
- * 数字健康 数字健康
- * 机器学习 * 机器学习
- * 医学信息学 医学信息学
背景情况:
- * 对健康数据隐私的日益增长的需求加剧了对联邦学习 (FL) 的兴趣.
- * FL可以实现分布式机器学习,而不会集中敏感的患者数据.
- *系统审查,以评估FL在医疗保健中的当前作用和影响.
结论:
- *联合学习在医疗保健方面显示出前景,但需要克服临床实施的障碍.
- *需要进一步的研究来评估FL在不断变化的数字医疗环境中的实际影响.
- * 应对翻译挑战对于实现FL在数据驱动医疗保健方面的潜力至关重要.
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