TAMER: A Test-Time Adaptive MoE-Driven Framework for EHR Representation Learning
Journal:
arXiv
Published Date:
Jan 10, 2025
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.
