[论文] Uncertainty-Aware Foundation Models for Clinical Data

论文概要

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

中文摘要

本文提出了一个面向临床数据的不确定性感知基础模型框架。该框架将每位患者表示为对合理潜在状态分布，而非点嵌入。通过学习集合值表示并强制同一患者的部分视图之间的一致性，模型能够捕捉不变可推断的内容，同时显式编码认知不确定性。该方法结合了多模态编码器和可扩展的自监督目标（重建、对比对齐和分布正则化）。在多个临床任务上的实验表明，该方法在预测性能、缺失数据鲁棒性和不确定性校准方面均优于强基线。

原文摘要

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.

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