多队列联合学习显示了对基于MRI和基于代谢学年龄得分的死亡率预测的协同作用
Pedro Mateus1, Swier Garst2,3, Jing Yu4,5
1Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, The Netherlands.
Journal of healthcare informatics research
|November 13, 2025
概括
联合学习使得跨队伍的BrainAge准确预测成为可能. 脑年龄和代谢年龄分数显示在预测死亡风险方面具有互补价值,捕捉了不同的衰老方面.
科学领域:
- 生物医学数据科学是生物医学数据科学.
- 衰老的研究研究.
- 神经成像和代谢学.
背景情况:
- 生物年龄得分估计了生理衰老,但它们的相互作用尚不清楚.
- 大规模的多模式数据对于研究这些相互作用至关重要,但数据共享受到限制.
- 联合学习为分析分布式,敏感的健康数据提供了一个解决方案.
研究的目的:
- 通过联合学习研究BrainAge (来自脑MRI) 和MetaboAge (来自代谢物) 之间的关系.
- 评估BrainAge.的联合深度学习模型的预测性能.
- 评估BrainAge和MetaboAge在预测痴呆和死亡率方面的互补作用.
主要方法:
- 在三个大型基于人口的队列中使用联合学习来训练用于BrainAge估计的深度学习模型.
- 将联合模型的性能与单个队伍中训练的模型进行了比较.
- 进行了关联和生存分析,以比较BrainAge和MetaboAge对于痴呆和死亡率预测.
主要成果:
- 与本地模型相比,联合的BrainAge模型显著降低了年龄预测误差.
- 协调年龄间隔进一步提高了联合BrainAge的准确性.
- 脑年龄和代谢年龄表现出较弱的关联,但对死亡风险具有互补的预测值.
结论:
- 联合学习对于分析受限研究队列数据是有效的.
- 脑年龄和代谢年龄协同预测所有原因的死亡风险.
- 这些独特的生物年龄分数捕捉了衰老过程的不同方面.
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