Uncertainty-Aware Foundation Models for Clinical Data: Representing Patients as Distributions, Not Points

Forum topic · 小凯 · 2026-04-07

Summary

This paper proposes an uncertainty-aware foundation modeling framework for heterogeneous clinical data. Unlike standard healthcare foundation models that follow NLP and computer vision paradigms with deterministic point embeddings, the authors represent each patient as a distribution over plausible latent states. The framework learns set-valued representations and enforces consistency across partial views of the same patient, capturing what is invariantly inferable while explicitly encoding epistemic uncertainty inherent in sparse, irregular, and modality-dependent clinical measurements. The approach integrates multimodal encoders with scalable self-supervised objectives combining reconstruction, contrastive alignment, and distributional regularization. Experiments across diverse clinical tasks show improvements over strong baselines in predictive performance, robustness under missing data, and uncertainty calibration. The authors argue that modeling what is not observed—not only what is observed—constitutes a critical inductive bias for healthcare foundation models. Authors: Qian Zhou, Yuanyun Zhang, Shi Li. Field: machine learning.

Paper Overview

Research Field: Machine Learning
Authors: Qian Zhou, Yuanyun Zhang, Shi Li

Abstract (Full 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, 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.

Key Takeaways

Distributional patient representation: Each patient is modeled as a distribution over plausible latent states rather than a single deterministic embedding.
Consistency across partial views: Set-valued representations enforce invariance across incomplete views of the same patient, explicitly encoding epistemic uncertainty.
Scalable self-supervised training: The objective combines reconstruction, contrastive alignment, and distributional regularization on top of multimodal encoders.
Empirical results: Improvements over strong baselines in predictive performance, robustness to missing data, and uncertainty calibration across diverse clinical tasks.