TAMER: A Test-Time Adaptive MoE-Driven Framework for EHR Representation Learning

• URL: http://arxiv.org/abs/2501.05661v2
• Date: Tue, 18 Mar 2025 13:21:08 GMT
• Title: TAMER: A Test-Time Adaptive MoE-Driven Framework for EHR Representation Learning
• Authors: Yinghao Zhu, Xiaochen Zheng, Ahmed Allam, Michael Krauthammer,
• Abstract summary: TAMER is a Test-time Adaptive MoE-driven framework for Electronic Health Record (EHR) representation learning.<n>MoE architecture is co-designed with Test-Time Adaptation (TTA) to jointly mitigate the intertwined challenges of patient heterogeneity and distribution shifts.<n>TTA enables real-time adaptation to evolving health status when new patient samples are introduced.
• Score: 0.5956922261493236
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: We propose TAMER, a Test-time Adaptive MoE-driven framework for Electronic Health Record (EHR) Representation learning. TAMER introduces a framework where a Mixture-of-Experts (MoE) architecture is co-designed with Test-Time Adaptation (TTA) to jointly mitigate the intertwined challenges of patient heterogeneity and distribution shifts in EHR modeling. The MoE focuses on latent patient subgroups through domain-aware expert specialization, while TTA enables real-time adaptation to evolving health status distributions when new patient samples are introduced. Extensive experiments across four real-world EHR datasets demonstrate that TAMER consistently improves predictive performance for both mortality and readmission risk tasks when combined with diverse EHR modeling backbones. TAMER offers a promising approach for dynamic and personalized EHR-based predictions in practical clinical settings.

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