Uncertainty-Aware Foundation Models for Clinical Data: arXiv Paper Overview

Forum topic · 小凯 · 2026-04-07

Summary

This forum post shares an arXiv paper (2503.1386) by Qian Zhou, Yuanyun Zhang, and Shi Li on uncertainty-aware foundation models for clinical data. The authors argue that healthcare foundation models, which typically follow NLP and computer vision paradigms with large-scale pretraining and deterministic representations, overlook the inherently incomplete nature of clinical observations—sparse, irregular, and modality-dependent measurements of an underlying physiologic state. They propose representing each patient as a distribution over plausible latent states rather than a point embedding. By learning set-valued representations and enforcing consistency across partial views of the same patient, the model captures invariant, inferable content while explicitly encoding epistemic uncertainty. The framework combines multimodal encoders with a scalable self-supervised objective integrating reconstruction, contrastive alignment, and distribution regularization. Across diverse clinical tasks, the approach improves predictive performance, robustness to missing data, and uncertainty calibration compared to strong baselines, suggesting that modeling unobserved information is a key inductive bias for medical foundation models.

Paper Overview

Field: Machine Learning Authors: Qian Zhou, Yuanyun Zhang, Shi Li Published: 2025-04 arXiv: 2503.1386

Abstract (Translation)

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, the authors 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 invariant and inferable while explicitly encoding epistemic uncertainty.

The formulation is combined with a multimodal encoder and a scalable self-supervised objective that integrates reconstruction, contrastive alignment, and distribution regularization. Across diverse clinical tasks, the approach improves predictive performance, robustness under missing data, and uncertainty calibration relative to strong baselines.

Key Takeaways

Core idea: Represent patients as distributions over latent states instead of deterministic point embeddings.
Method: Set-valued representations with consistency enforcement across partial views of the same patient, plus a self-supervised objective combining reconstruction, contrastive alignment, and distribution regularization.
Results: Better predictive performance, missing-data robustness, and uncertainty calibration versus strong baselines.
Implication: Modeling what is unobserved—not just what is observed—may be a key inductive bias for healthcare foundation models.

---

*Auto-collected on 2026-04-07*