TAMER通过动态专家协作提升电子病历预测准确性
TAMER: A Test-Time Adaptive MoE-Driven Framework for EHR Representation Learning
- 采用动态专家模型与测试时自适应结合，应对患者差异和数据分布变化
- 在4个真实病历数据集上，死亡率与再入院风险预测性能均显著提升
- 适合需要个性化、实时更新的临床预测场景
我们提出TAMER，一种面向电子健康记录（EHR）表示学习的测试时自适应混合专家（MoE）驱动框架。TAMER将混合专家架构与测试时自适应（TTA）协同设计，共同缓解EHR建模中患者异质性与分布偏移的交织挑战。MoE通过领域感知的专家专业化聚焦潜在患者亚群，而TTA则在新患者样本引入时实现对健康状态分布演变的实时适应。在四个真实世界EHR数据集上的大量实验表明，TAMER与多种EHR建模范式结合后，能持续提升死亡率与再入院风险预测的性能。TAMER为实际临床环境中动态化、个性化的基于EHR的预测提供了有前景的解决方案。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。