ArchEHR-QA 2026中的sebis研究：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

基于电子健康记录(EHR)的临床问答系统能够帮助临床医生和患者更高效地获取相关医疗信息。然而，当前许多方法依赖大型云端模型，由于隐私限制和计算资源要求，这类模型难以在临床环境中部署。本研究探索了在仅使用单台笔记本电脑的受限条件下，基于EHR的问答系统性能能达到何种高度。我们参与了ArchEHR-QA 2026共享任务的全部四个子任务，评估了多种适用于商用硬件的解决方案。所有实验均在本地完成，未使用任何外部API或云基础设施。结果表明，此类系统在共享任务排行榜上能取得具有竞争力的表现：我们的提交结果在两个子任务中超过平均水平，并发现经过适当配置后，较小规模的模型可以接近大型系统的性能。这些发现表明，基于现有模型和商用硬件实现完全本地的隐私保护型EHR问答系统具有可行性。源代码已发布于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.