针对临床研究中的矛盾证据，构建新数据集并提出高效提取方法。
Query-driven Document-level Scientific Evidence Extraction from Biomedical Studies
- 基于森林图构建检索增强生成框架URCA，实现文档级证据抽取。
- 在202个标注森林图上，F1得分提升最高达10.3%。
- 适合需要自动化证据合成的医学研究者和系统开发者。
从生物医学研究中提取科学证据以回答临床研究问题（如：干细胞移植是否比安慰剂更有效改善难治性克罗恩病患者的生活质量？）是综合生物医学证据的关键步骤。本文聚焦于存在矛盾证据的临床问题的文档级科学证据提取任务。为此，我们构建了名为CochraneForest的数据集，利用来自Cochrane系统评价的森林图，包含202个标注的森林图、相关临床研究问题、完整研究文本及研究结论。在此基础上，我们提出URCA（统一检索聚类增强）框架，一种专为该任务设计的检索增强生成方法。实验表明，URCA在该任务上的F1分数相比现有最佳方法最高提升10.3%。然而，结果也凸显了CochraneForest的复杂性，确立其作为推进自动化证据合成系统的重要挑战基准。
原文摘要 · Abstract (English)
Extracting scientific evidence from biomedical studies for clinical research questions (e.g., Does stem cell transplantation improve quality of life in patients with medically refractory Crohn's disease compared to placebo?) is a crucial step in synthesising biomedical evidence. In this paper, we focus on the task of document-level scientific evidence extraction for clinical questions with conflicting evidence. To support this task, we create a dataset called CochraneForest, leveraging forest plots from Cochrane systematic reviews. It comprises 202 annotated forest plots, associated clinical research questions, full texts of studies, and study-specific conclusions. Building on CochraneForest, we propose URCA (Uniform Retrieval Clustered Augmentation), a retrieval-augmented generation framework designed to tackle the unique challenges of evidence extraction. Our experiments show that URCA outperforms the best existing methods by up to 10.3% in F1 score on this task. However, the results also underscore the complexity of CochraneForest, establishing it as a challenging testbed for advancing automated evidence synthesis systems.
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