提出可解释的生成判别联合模型，解决临床多模态数据缺失与小样本难题。
Interpretable Generative and Discriminative Learning for Multimodal and Incomplete Clinical Data
- 采用贝叶斯框架融合生成与判别建模，自动补全缺失数据视图。
- 在真实临床数据上有效捕捉生物、心理、社会三类模态间复杂交互关系。
- 适合医疗领域研究者，尤其关注数据不完整时的可解释模型应用。
现实临床问题常涉及多模态数据，通常伴随视图缺失和队列样本量有限，对机器学习算法构成重大挑战。本文提出一种贝叶斯方法，高效应对这些难题并提供可解释性解决方案。该方法结合（1）生成式建模以捕捉跨视图关系，采用半监督策略；（2）判别式任务导向建模，识别对特定下游目标相关的信息。这种双轨生成-判别框架既提供整体理解，也实现任务特异性洞察，从而实现缺失视图的自动补全，并支持跨数据源的鲁棒推断。应用于多模态临床数据时，该算法成功捕获并解耦生物、心理及社会人口学模态间的复杂交互作用。
原文摘要 · Abstract (English)
Real-world clinical problems are often characterized by multimodal data, usually associated with incomplete views and limited sample sizes in their cohorts, posing significant limitations for machine learning algorithms. In this work, we propose a Bayesian approach designed to efficiently handle these challenges while providing interpretable solutions. Our approach integrates (1) a generative formulation to capture cross-view relationships with a semi-supervised strategy, and (2) a discriminative task-oriented formulation to identify relevant information for specific downstream objectives. This dual generative-discriminative formulation offers both general understanding and task-specific insights; thus, it provides an automatic imputation of the missing views while enabling robust inference across different data sources. The potential of this approach becomes evident when applied to the multimodal clinical data, where our algorithm is able to capture and disentangle the complex interactions among biological, psychological, and sociodemographic modalities.
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