对测量记录的护理目标讨论的零射击大型语言模型的评估
Robert Y Lee1,2, Kevin S Li1,3, James Sibley1,4
1Division of Pulmonary, Critical Care, and Sleep Medicine, University of Washington, Seattle, USA.
medRxiv : the preprint server for health sciences
|June 6, 2025
概括
一个没有任务特定培训的大型语言模型 (LLM) 在识别护理目标 (GOC) 文档方面与BERT模型相比地表现得更好. 这证明了LLMs.
科学领域:
- 临床研究中的自然语言处理 (NLP).
- 息护理过程测量.
- 医疗保健中的人工智能 (AI).
背景情况:
- 护理目标 (GOC) 文档是缓和护理过程中的一个关键措施.
- 现有的NLP模型用于中国政府的识别需要广泛的,昂贵的培训数据.
- 较新的大型语言模型 (LLM) 提供了复杂的语言分析的潜力,需要最少的培训.
研究的目的:
- 通过使用零射击提示来识别电子健康记录 (EHR) 记录的中国政府讨论来评估公开的LLM.
- 为了比较零射击的LLM与特定任务的BERT模型的性能.
主要方法:
- 诊断研究比较两个NLP模型:Llama 3.3 (零射击LLM) 和一个特定任务的BERT模型.
- 评估了成年患有慢性生命限制疾病的成年患者 (2018-2023) 的EHR文本体上的模型.
- 用AUC,AUPRC和F1得分测量30天内在笔记和患者水平上的表现.
主要成果:
- 中国政府的文件很少见 (<1%的EHR文本,7.3-9.9%的注释).
- 拉玛 3.3 (零射击) 和BERT (特定任务) 在识别中国政府文件方面表现相似.
- 拉玛3.3和BERT的AUC值分别为0.979和0.981 (笔记级),0.977和0.981 (患者级).
结论:
- 一个零射击的LLM的性能与监督的BERT模型类似,用于识别中国政府的文件.
- 在测量新型临床试验结果方面,LLM显示出有前途,减少了对特定任务培训数据的需求.
- 这种方法有助于对医疗保健研究中的LLM进行严格的评估.
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