TAMER: A Test-Time Adaptive MoE-Driven Framework for EHR Representation Learning
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866913743535865856 |
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| author | Zhu, Yinghao Zheng, Xiaochen Allam, Ahmed Krauthammer, Michael |
| author_facet | Zhu, Yinghao Zheng, Xiaochen Allam, Ahmed Krauthammer, Michael |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_05661 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | TAMER: A Test-Time Adaptive MoE-Driven Framework for EHR Representation Learning Zhu, Yinghao Zheng, Xiaochen Allam, Ahmed Krauthammer, Michael Machine Learning 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. |
| title | TAMER: A Test-Time Adaptive MoE-Driven Framework for EHR Representation Learning |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2501.05661 |