大型语言模型的临床情绪分析提高了对肝综合征的预测

Mason Lai¹, Cynthia Fenton^2,3, Jessica Rubin⁴

¹Department of Medicine, University of California, San Francisco, California.

Gastro hep advances

|October 27, 2025

概括

大型语言模型可以通过分析临床文本情绪来预测肝综合征-急性损伤 (HRS-AKI). 这种方法可以提高预测的准确性,超出了非补偿性肝硬化患者的传统方法.

科学领域:

医疗信息学医疗信息学
人工智能在医学中的应用
临床预测模型临床预测模型

背景情况:

肝综合征-急性损伤 (HRS-AKI) 是不补偿性肝硬化的一种严重并发症,具有具有挑战性的预测.
对于HRS-AKI的传统预测器往往缺乏足够的准确性.
大型语言模型 (LLM) 在临床数据的自然语言处理 (NLP) 中表现有前途.

研究的目的:

确定LLM是否可以从非结构化文本中获得临床情绪.
评估这种情绪分析是否比传统方法改善了HRS-AKI预测.
评估在临床NLP任务中使用GPT-4o的可行性.

主要方法:

进行了一项对314名患有不补偿性肝硬化和AKI的成年患者的队列研究.
微软Azure OpenAI GPT-4o被用于导出HRS-AKI情绪得分和提取临床术语.
后勤回归模型将AUROC与LLM衍生特征进行比较.

主要成果:

较高的情绪分数与HRS-AKI诊断相关 (OR:1.52 / 0.1).
仅使用结构化数据的AUROC是0.63.
结合GPT-4o情绪和NLP术语,AUROC提高到了0.79 (P < .01).

结论:

临床文本包含有价值的数据,这些数据不易通过标准方法提取.
用GPT-4o进行情绪分析和NLP对于临床应用是可行的.
与单独的结构化数据相比,LLM衍生的特征提高了HRS-AKI预测的准确性.

关键词:

艾基 (AKI) 是一个人.脱补偿性肝硬化肝综合征肝综合征大型语言模型情绪分析情绪分析

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