English(EN) Neural ODE enhanced linear mixed effect models for estimating complex association patterns of time-varying covariates with the marker trajectory

新的神经ODE-LMM模型学习纵向研究中复杂的协变量效应

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-01 04:00

研究人员开发了一种名为神经ODE-LMM的新型统计模型，该模型将神经常微分方程（Neural ODE）集成到线性混合效应模型（LMM）框架中。这种新方法能够灵活地学习时变协变量对健康结果的复杂影响，而无需预先指定函数形式。该模型应用于“三城”（3C）队列研究，揭示了身体质量指数（BMI）、空腹血糖和认知能力下降之间轨迹依赖性关联。 AI

影响引入了一种新颖的统计建模技术，用于分析复杂的纵向数据，有可能改善健康和队列研究的见解。

排序理由该项目描述了学术论文中提出的一种新统计模型。[lever_c_demoted from research: ic=1 ai=0.7]

在 arXiv cs.LG 阅读 →

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Zhe Aurore Li, Quentin Clairon, C\'ecilia Samieri, Rodolphe Thi\'ebaut, M\'elanie Prague, C\'ecile Proust-Lima · 2026-09-01 04:00

用于估计时变协变量与标记轨迹复杂关联模式的神经ODE增强线性混合效应模型

arXiv:2608.29714v1 Announce Type: cross Abstract: Longitudinal cohort studies produce repeated data that enable the assessment of time-varying association patterns between exposures and health outcomes. Classical linear mixed-effects models (LMMs) can accommodate a large variety …

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报道来源 [1]

用于估计时变协变量与标记轨迹复杂关联模式的神经ODE增强线性混合效应模型

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