预测持续认知衰退:多模式机器学习方法的概括性,包括结构MRI和非大脑数据
medRxiv : the preprint server for health sciences
|February 6, 2026
概括
机器学习模型使用多式联络数据预测认知衰退. 虽然这些模型显示了对新数据集的部分通用性,但在不同网站或队列中应用时,性能会下降.
科学领域:
- 神经科学是一个神经科学.
- 人工智能的人工智能
- 老年学是一门学科.
背景情况:
- 老龄化中的认知衰退显示出显著的个体变化.
- 预测认知衰退的连续性有助于个性化干预.
- 整合多种数据的机器学习模型显示出预测认知衰退的前景.
研究的目的:
- 复制和验证用于预测持续认知衰退的机器学习模型.
- 评估这些模型在独立数据集和采集站点的通用性.
- 评估结构磁共振成像 (MRI) 数据对模型性能的影响.
主要方法:
- 使用多目标随机森林回归模型.
- 模型预测了临床痴呆症评分表 (CDR-SOB) 和迷你精神状态检查 (MMSE) 的年度下降率.
- 模型性能在数据集内和跨数据集 (ADNI,OASIS-3) 中进行了评估.
主要成果:
- 整合结构性MRI数据改善了ADNI队列中的模型性能,与之前的OASIS-3发现一致.
- 在未见的数据集上测试时,模型表现出统计学上显著的性能退化.
- 在关键特征上训练的模型的表现与在外部验证中使用所有特征的模型相比,表明特征冗余.
结论:
- 持续认知衰退预测的多模式机器学习模型显示了部分概括性.
- 跨数据集的性能退化凸显了现实应用中的挑战.
- 仅使用结构性MRI特征的单模模型在外部没有很好地泛化.
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