Uncertainty-Aware Foundation Models for Clinical Data: Distributions Instead of Point Embeddings

Forum topic · 小凯 · 2026-04-07

Summary

A paper by Qian Zhou, Yuanyun Zhang, and Shi Li proposes an uncertainty-aware framework for healthcare foundation models. Instead of representing each patient as a single point embedding, the model represents each patient as a distribution over plausible latent states. By learning set-valued representations and enforcing consistency across partial views of the same patient, the approach captures what is invariantly inferable from sparse, irregular, and modality-dependent clinical observations while explicitly encoding epistemic uncertainty. The framework integrates multimodal encoders with scalable self-supervised objectives that combine 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, rather than only what is, constitutes a critical inductive bias for foundation models in healthcare.

论文概要

领域: ML
作者: Qian Zhou, Yuanyun Zhang, Shi Li

Chinese Summary (translated)

This paper proposes an uncertainty-aware foundation model framework for clinical data. The framework represents each patient as a distribution over plausible latent states, rather than as a point embedding. 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. The method combines multimodal encoders with scalable self-supervised objectives (reconstruction, contrastive alignment, and distributional regularization). Experiments on multiple clinical tasks show that the approach outperforms strong baselines in predictive performance, robustness to missing data, and uncertainty calibration.

Original Abstract

> 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.