用大模型从医文里自动提取可信证据，帮医生快速找关键信息。
MedNuggetizer: Confidence-Based Information Nugget Extraction from Medical Documents
- 基于大语言模型反复抽取信息块，再聚类生成可靠证据
- 在前列腺活检抗生素预防场景中验证，专家评估效率显著提升
- 适合临床决策支持、研究文献快速梳理的场景
我们提出MedNuggetizer（https://mednugget-ai.de/；访问需申请），一种面向临床需求的查询驱动式信息块提取与聚类工具，用于帮助医生从医学文档中探索潜在医学证据。该工具依托大语言模型（LLM），对多份医疗文档进行多次信息块抽取，并将结果聚类，以生成跨文档的可靠证据。我们在"前列腺活检前抗生素预防"这一临床场景中进行了验证，采用主要泌尿外科指南和近期PubMed研究作为信息源。领域专家评估表明，MedNuggetizer为临床医生和研究人员提供了高效探索长篇文档并快速提取聚焦查询的可靠医学证据的新方式。
原文摘要 · Abstract (English)
We present MedNuggetizer, https://mednugget-ai.de/; access is available upon request.}, a tool for query-driven extraction and clustering of information nuggets from medical documents to support clinicians in exploring underlying medical evidence. Backed by a large language model (LLM), \textit{MedNuggetizer} performs repeated extractions of information nuggets that are then grouped to generate reliable evidence within and across multiple documents. We demonstrate its utility on the clinical use case of \textit{antibiotic prophylaxis before prostate biopsy} by using major urological guidelines and recent PubMed studies as sources of information. Evaluation by domain experts shows that \textit{MedNuggetizer} provides clinicians and researchers with an efficient way to explore long documents and easily extract reliable, query-focused medical evidence.
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