医院参与联合学习:评估可持续性和临床实用性
概括
联合学习 (FL) 在医院中显示出前列腺癌 (PCa) 风险预测的希望. 虽然FL增强了通用性,但本地模型可以具有竞争力,建议针对性合作以获得最佳结果.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的机器学习
- 在瘤学瘤学.
背景情况:
- 前列腺癌 (PCa) 诊断经常涉及侵入性活检,带来风险和潜在的不必要的程序.
- 传统的集中式机器学习模型需要共享敏感的患者数据,这引发了隐私问题.
- 联合学习 (FL) 能够实现跨机构的协作模式培训,而无需直接共享数据,从而保护患者的隐私.
研究的目的:
- 评估联合学习 (FL) 对前列腺癌 (PCa) 风险预测的可行性和性能.
- 通过使用现实世界的异质医疗保健数据,将FL与当地培训和自由骑行策略进行基准测试.
- 分析数据多样性和联盟规模对FL模型预测准确性的影响.
主要方法:
- 利用来自19家医院的现实世界异质数据集进行培训和评估.
- 在三个策略中比较预测性能:本地模型培训,联合学习和联合模型上的自由骑行.
- 评估了数据多样性和参与机构数量的不同对模型通用性和准确性的影响.
主要成果:
- 与本地培训相比,联合学习 (FL) 显示出更好的模型通用性.
- 当地模型的性能与FL模型相美,特别是对于具有大量数据的大型医院.
- 一个较小的高质量数据机构联盟显示出开发强大的预测模型的潜力.
结论:
- 联合学习是一种可行的隐私保护PCa风险预测方法.
- 对大型机构来说,FL参与的直接好处可能是有限的;战略合作是关键.
- 提供了关于在现实世界临床网络中可持续实施FL的建议.
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