用多阶段大模型流水线，让患者问题变专业答案，证据精准可追溯。
HealthNLP_Retrievers at ArchEHR-QA 2026: Cascaded LLM Pipeline for Grounded Clinical Question Answering
- 分四步处理：重写问题、排序证据、生成答案、对齐支持句
- 在患者问诊理解任务中排名第一，答案与证据匹配精度高
- 适合医疗AI研究者、临床信息检索系统开发者参考
患者门户已实现个人对电子健康记录（EHR）的直接访问，但单纯获取信息并不保证患者能理解或采取行动。为此，ArchEHR-QA 2026共享任务聚焦于基于EHR的精确问答。本文介绍HealthNLP_Retrievers团队提出的系统，采用由Gemini 2.5 Pro大语言模型驱动的多阶段级联流水线，解析患者自述问题并从长篇临床笔记中检索相关证据。系统包含四个模块：（1）少量样本查询重写单元，用于压缩冗长患者问题；（2）基于启发式的证据评分器，优先提升召回率；（3）基于证据生成的答案生成器，确保回答严格限定于所识别证据；（4）高精度多对多对齐框架，将生成答案与支持性临床句子关联。该级联方法表现优异，在各子任务中分别取得：问题理解第1名、答案生成第5名、证据识别第7名、答案-证据对齐第9名。结果表明，将大语言模型融入结构化多阶段流程，可有效提升回答的准确性、可解释性和专业水准。为保障复现性，源代码已开源至GitHub。
原文摘要 · Abstract (English)
Patient portals now give individuals direct access to their electronic health records (EHRs), yet access alone does not ensure patients understand or act on the complex clinical information contained in these records. The ArchEHR-QA 2026 shared task addresses this challenge by focusing on grounded question answering over EHRs, and this paper presents the system developed by the HealthNLP_Retrievers team for this task. The proposed approach uses a multi-stage cascaded pipeline powered by the Gemini 2.5 Pro large language model to interpret patient-authored questions and retrieve relevant evidence from lengthy clinical notes. Our architecture comprises four integrated modules: (1) a few-shot query reformulation unit which summarizes verbose patient queries; (2) a heuristic-based evidence scorer which ranks clinical sentences to prioritize recall; (3) a grounded response generator which synthesizes professional-caliber answers restricted strictly to identified evidence; and (4) a high-precision many-to-many alignment framework which links generated answers to supporting clinical sentences. This cascaded approach achieved competitive results. Across the individual tracks, the system ranked 1st in question interpretation, 5th in answer generation, 7th in evidence identification, and 9th in answer-evidence alignment. These results show that integrating large language models within a structured multi-stage pipeline improves grounding, precision, and the professional quality of patient-oriented health communication. To support reproducibility, our source code is publicly available in our GitHub repository
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