Healthcare foundation models have largely followed paradigms from natural language processing and computer vision, emphasizing large scale pretraining and deterministic representations over heterogeneous clinical data. However, clinical observations are inherently incomplete, reflecting sparse, irregular, and modality dependent measurements of an underlying physiologic state. In this work, we propose a framework for uncertainty aware foundation modeling that represents each patient not as a point embedding, but as a distribution over plausible latent states. By learning set valued representations and enforcing consistency across partial views of the same patient, the model captures what is invariantly inferable while explicitly encoding epistemic uncertainty. We integrate this formulation with multimodal encoders and scalable self supervised objectives, combining reconstruction, contrastive alignment, and distributional regularization. Across diverse clinical tasks, our approach improves predictive performance, robustness under missing data, and uncertainty calibration relative to strong baselines. These results suggest that modeling what is not observed rather than only what is constitutes a critical inductive bias for healthcare foundation models.

翻译：医疗健康基础模型在很大程度上沿袭了自然语言处理与计算机视觉领域的范式，强调大规模预训练以及在异构临床数据上生成确定性表征。然而，临床观测数据本质上是残缺的，仅能反映潜在生理状态下稀疏、不规则且依赖测量手段的特征。本研究提出一种不确定性感知的基础建模框架，该框架将每位患者表示为潜在状态的概率分布，而非单一嵌入点。通过学习集合式表征并强制同一患者部分视图间的一致性，模型能捕获恒定可推断的特征，同时明确编码认知不确定性。我们将该公式与多模态编码器及可扩展的自监督目标相整合，融合重构、对比对齐与分布正则化。在多种临床任务中，相较于强基线方法，本方法在预测性能、数据缺失鲁棒性及不确定性校准方面均有提升。这些结果表明，对未观测状态进行建模（而非仅关注已观测数据），是医疗健康基础模型的关键归纳偏置。