使用大型语言模型对败血症队列的综合症分析
Theodore R Pak1,2, Sanjat Kanjilal1,3, Caroline S McKenna1
1Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, Massachusetts.
JAMA network open
|October 24, 2025
概括
大型语言模型 (LLM) 从临床笔记中准确地提取患者的症状,有助于败血症诊断和预测结果. 这项技术有助于识别症状,感染和死亡率之间的关联.
科学领域:
- 临床信息学 临床信息学
- 人工智能在医学中的应用
- 败血症研究 败血症研究
背景情况:
- 从临床笔记中提取患者的症状和症状对于败血症护理至关重要,但对于大规模研究来说具有挑战性.
- 目前的方法很难有效地处理非结构化的临床文本以获取症状数据.
研究的目的:
- 为了评估大语言模型 (LLM) 在从患者入院笔记中提取呈现的迹象和症状的有效性.
- 分析提取的症状与传染病诊断,多抗药性感染和败血症患者的死亡率的关联.
主要方法:
- 这是一项回顾性队列研究,涉及5家医院超过10万名成年患者.
- 使用大型语言模型 (LLaMA 3 8B) 来从入院笔记中提取多达10种症状.
- 验证了LLM提取的症状标签与手动医生审查对比,并使用后勤回归分析了与结果的关联.
主要成果:
- 该LLM在提取症状方面表现出高精度 (99.3%),对98.7%的患者进行了验证标签.
- 提取的症状分为综合征,与特定的感染源和多耐药生物体 (MRSA,MDRGN) 相相关.
- 心肺症状与增加的死亡率有关 (AOR 1.30),而皮肤/软组织症状增加了MRSA风险 (AOR 1.73).
结论:
- 从入院笔记中,LLM可以准确地提取患者的症状和症状,形成与感染类型和患者结果相关的综合征.
- 这种方法可以对症状数据进行大规模分析,从而有可能改善败血症管理和抗生素策略.
- 建议进行进一步的研究,将这些症状数据整合到抗生素选择和患者结果的预测模型中.
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