为多模态数据将神经编码器集成至贝叶斯广义线性混合模型中
Integrating Neural Encoders in Bayesian Generalized Linear Mixed Models for Multimodal Data
AI总结：
针对传统广义线性混合模型无法适配高维多模态数据的问题，该研究联合训练模态专属神经编码器与GLMM目标，通过方差校正随机梯度MCMC实现推断，兼顾效应可解释性与多模态纵向数据的可扩展不确定性分析。
摘要：
广义线性混合模型（GLMM）的可扩展贝叶斯推断可对相关纵向数据开展具备不确定性感知的分析，但现有可扩展方法大多假设预测变量为低维表格数据，无法直接适配图像、文本等高维模态。我们针对这一局限，联合GLMM目标学习一个或多个模态专属的神经编码器，随后在习得表征的条件下对GLMM参数执行经方差校正的随机梯度MCMC。该条件贝叶斯设计将有监督表征学习与针对群体水平效应、受试者特异性异质性、模态水平随机斜率的后验不确定性量化相结合。最终得到的模型可为结构化协变量与习得模态保留可解释的固定效应和随机效应，同时可流畅扩展至大型纵向数据集。在仿真研究中，我们的方法经协方差校正后，可从全数据MCMC基准中恢复后验均值与方差估计值。我们进一步通过仿真中的参数水平区间覆盖度与留出数据上的预测校准度对不确定性开展评估。在青光眼进展与青少年心理健康场景的应用表明，该框架可在不损失预测性能的前提下，在个体与群体层面实现对各模态相对重要性的精细化评估。
英文摘要：
Scalable Bayesian inference for generalized linear mixed models (GLMMs) provides uncertainty-aware analysis of correlated longitudinal data, but existing scalable approaches largely assume low-dimensional tabular predictors and do not directly accommodate high-dimensional modalities such as images and text. We address this limitation by learning one or more modality-specific neural encoders jointly with a GLMM objective, then performing variance-corrected stochasticgradient MCMC for the GLMM parameters conditional on the learned representation. This conditional-Bayes design combines supervised representation learning with posterior uncertainty quantification for population-level effects, subjectspecific heterogeneity, and modality-level random slopes. The resulting model preserves interpretable fixed and random effects for structured covariates and learned modalities while scaling gracefully to large longitudinal datasets. In simulation studies, our method recovers posterior means and variance estimates from full-data MCMC benchmarks after covariance correction. We further evaluate uncertainty through parameter-level interval coverage in simulations and predictive calibration on held-out data. Applications to glaucoma progression and adolescent mental health demonstrate that the framework allows nuanced assessment of the relative importance of each modality on both individual and population levels without sacrificing predictive performance.
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