一个数据集,以满足患者与住院临床过程相关的信息需求
Sarvesh Soni1, Dina Demner-Fushman2
1Division of Intramural Research, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA. sarvesh.soni@nih.gov.
Scientific data
|February 25, 2026
概括
一个名为ArchEHR-QA的新数据集,通过评估人工智能生成的答案与电子健康记录 (EHR) 相比,解决了在医疗保健中评估人工智能 (AI) 的需求. 这项研究将AI的事实性和相关性与患者信息需求进行基准.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 临床数据科学 临床数据科学
背景情况:
- 在住院期间,患者的信息需求至关重要.
- 电子健康记录 (EHR) 和人工智能 (AI) 提供了潜在的解决方案.
- 现有的数据集缺乏可靠性来评估AI在EHR环境中的事实性和相关性.
研究的目的:
- 介绍ArchEHR-QA,这是一个新的专家注释数据集,用于基于EHR的问答 (QA).
- 为了解决数据集的差距,以评估AI对患者特定临床数据的反应.
- 建立AI在理解和响应患者信息需求方面的表现的基准.
主要方法:
- 开发了ArchEHR-QA数据集,其中包括来自重症监护室和急诊室的134例病例.
- 包括患者的问题,临床医生的解释,相关的EHR摘录和注释以及临床医生撰写的答案.
- 通过使用三个提示策略,评估了三种开放式重量大语言模型 (LLM).
主要成果:
- 对事实性 (证据重叠) 和相关性 (与参考答案相似) 的LLM绩效进行评估.
- 通过使用Llama 4,Llama 3和Mixtral.建立了基于EHRQA的初始基准.
- 证明了ArchEHR-QA对评估AI事实性和相关性的实用性.
结论:
- ArchEHR-QA为开发和验证医疗保健中的AI工具提供了至关重要的资源.
- 该数据集可以客观地评估AI在EHR数据中建立响应的能力.
- 未来的工作可以扩展此数据集,以提高AI准确性和患者信息传递.
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