sebis团队在ArchEHR-QA 2026研讨会：本地化处理的极限探索？基于单台笔记本的接地式电子健康记录问答系统评估

sebis at ArchEHR-QA 2026: How Much Can You Do Locally? Evaluating Grounded EHR QA on a Single Notebook

March 14, 2026

作者: Ibrahim Ebrar Yurt, Fabian Karl, Tejaswi Choppa, Florian Matthes

cs.AI

摘要

基於電子健康記錄（EHR）的臨床問答系統能幫助醫護人員和患者更有效率地獲取相關醫療資訊。然而，當前許多方法依賴大型雲端模型，由於隱私限制和計算資源需求，這類模型難以在臨床環境中部署。本研究探討在僅使用單台筆記型電腦的條件下，基於EHR的實證問答系統能達到何種效能水平。我們參與了ArchEHR-QA 2026聯合任務的全部四個子任務，並評估了多種專為商用硬體設計的解決方案。所有實驗均在本地端完成，未使用外部API或雲端基礎設施。結果表明，此類系統在聯合任務排行榜上能展現具競爭力的表現：我們的提交在兩個子任務中表現高於平均水平，且發現經過適當配置後，較小模型能接近大型系統的效能。這些發現證實，基於現有模型和商用硬體實現完全本地化運行的隱私保護型EHR問答系統具有可行性。原始碼已開源於https://github.com/ibrahimey/ArchEHR-QA-2026。

English

Clinical question answering over electronic health records (EHRs) can help clinicians and patients access relevant medical information more efficiently. However, many recent approaches rely on large cloud-based models, which are difficult to deploy in clinical environments due to privacy constraints and computational requirements. In this work, we investigate how far grounded EHR question answering can be pushed when restricted to a single notebook. We participate in all four subtasks of the ArchEHR-QA 2026 shared task and evaluate several approaches designed to run on commodity hardware. All experiments are conducted locally without external APIs or cloud infrastructure. Our results show that such systems can achieve competitive performance on the shared task leaderboards. In particular, our submissions perform above average in two subtasks, and we observe that smaller models can approach the performance of much larger systems when properly configured. These findings suggest that privacy-preserving EHR QA systems running fully locally are feasible with current models and commodity hardware. The source code is available at https://github.com/ibrahimey/ArchEHR-QA-2026.