{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/tamer-a-test-time-adaptive-moe-driven","title":"TAMER: A Test-Time Adaptive MoE-Driven Framework for EHR Representation Learning","arxiv_id":"2501.05661","date":"2025-01-10","proceeding":null,"authors":["Yinghao Zhu","Xiaochen Zheng","Ahmed Allam","Michael Krauthammer"],"abstract":"We propose TAMER, a Test-time Adaptive MoE-driven framework for EHR Representation learning. TAMER combines a Mixture-of-Experts (MoE) with Test-Time Adaptation (TTA) to address two critical challenges in EHR modeling: patient population heterogeneity and distribution shifts. The MoE component handles diverse patient subgroups, 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. Code is publicly available at https://github.com/yhzhu99/TAMER.","url_abs":"https://arxiv.org/abs/2501.05661v1","url_pdf":"https://arxiv.org/pdf/2501.05661v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"tamer-a-test-time-adaptive-moe-driven","repo_url":"https://github.com/yhzhu99/tamer","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"mixture-of-experts","task_name":"Mixture-of-Experts"},{"task_slug":"representation-learning","task_name":"Representation Learning"},{"task_slug":"test-time-adaptation","task_name":"Test-time Adaptation"}],"methods":[{"method_slug":"moe","method_name":"MoE"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2501.05661","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}