在测量记录的护理目标讨论中评估一个零射击的大型语言模型
Robert Y Lee1, Kevin S Li2, James Sibley3
1Division of Pulmonary, Critical Care, and Sleep Medicine (R.Y.L., K.S.L., J.S., T.C., W.B.L., D.G.D., E.K.K.), University of Washington, Seattle, Washington, USA; Cambia Palliative Care Center of Excellence at UW Medicine (R.Y.L., D.G.D., E.K.K.), University of Washington, Seattle, Washington, USA.
Journal of pain and symptom management
|October 8, 2025
概括
大型语言模型 (LLM) 可以像训练模型一样有效地识别护理目标 (GOC) 文档. 这一进步为使用NLP测量息治疗结果提供了一种具有成本效益的方法.
科学领域:
- 临床信息学 临床信息学
- 自然语言处理 (NLP) 是一种自然语言处理.
- 息护理研究 息护理研究
背景情况:
- 治疗目标 (GOC) 讨论在息治疗中至关重要,但识别它们的文档是具有挑战性的.
- 现有的NLP模型需要昂贵的,特定任务的培训数据.
- 大型语言模型 (LLM) 为数据有效的结果测量提供了一个潜在的解决方案.
研究的目的:
- 评估一项零射击的LLM在识别记录的中国政府讨论方面的表现.
- 将LLM的表现与传统的,特定任务的NLP模型进行比较.
主要方法:
- 将Llama 3.3 (零射击LLM) 与在4,642个笔记上训练的特定任务BERT模型进行比较.
- 评估了临床试验记录的模型,这些记录涉及临终疾病的住院患者 (2018-2023年).
- 在笔记和患者水平上使用AUC,AUPRC和F1得分来评估表现.
主要成果:
- 中国政府的文件很少 (<1%的文本,7.3-9.9%的注释).
- 拉玛3.3 (零射击) 和BERT (任务特定) 在识别中国政府文件方面表现相似.
- 关键指标:拉玛 3.3 (AUC 0.979,AUPRC 0.873,F1 0.83) 与伯特 (AUC 0.981,AUPRC 0.874,F1 0.83) 相比,这些指标的使用率较低.
结论:
- 零射击的LLM可以与训练有素的NLP模型相比,识别中国政府的文档.
- 在临床研究中,LLM为测量临床研究结果提供了一种有希望的,数据效率高的方法.
- 这证实了LLM在息护理研究中的新型结果测量.
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