Uncertainty-Aware Foundation Models for Clinical Data
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 i...
| Authors | Qian Zhou, Yuanyun Zhang, Shi Li |
|---|---|
| Primary category | cs.LG |
| Published | 5 April 2026 |
| Last updated at source | 5 April 2026 |
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