探索大型语言模型与心力衰竭中传统临床模型的预后效用:试点研究
Luxiang Shang1, Yali Chen2, Rui Li3
1Department of Health Management, Shandong Engineering Laboratory of Health Management, Shandong Institute of Health Management, the First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, China.
International journal of surgery (London, England)
|November 19, 2025
概括
大型语言模型 (LLM) 对结构性心力衰竭 (HF) 数据的预测价值有限,但在使用非结构化临床笔记的风险预测方面表现相似. 这些医疗保健中的AI工具需要进一步验证.
科学领域:
- 人工智能在医学中的应用
- 临床信息学 临床信息学
- 预测分析是一种预测分析.
背景情况:
- 大型语言模型 (LLM) 显示出临床决策支持的潜力.
- 在预测心力衰竭 (HF) 风险方面,LLMs的作用尚未得到充分证实.
- 本研究调查了与传统模型相比,LLM的表现.
研究的目的:
- 评估ChatGPT和DeepSeek对心力衰竭风险预测的预后性能.
- 用结构化临床数据与非结构化出院总结来比较LLM的表现.
- 在临床环境中评估LLM预测的可重现性.
主要方法:
- 两种LLM (ChatGPT,DeepSeek) 在结构化数据 (齐贡高频研究) 和非结构化出院摘要 (MIMIC-IV队列) 上进行了测试.
- 模型预测了心力衰竭患者的90天和1年的结果.
- 将LLM预测与后勤回归模型进行比较,并通过类内相关系数评估可重现性.
主要成果:
- 与传统模型 (AUC 0.63) 相比,LLM 在结构化数据 (AUC 0.56-0.59) 上显示出有限的歧视.
- 聊天GPT表现出与预测出院总结死亡率的临床模型相似的性能 (AUC 0.72 与 0.74).
- 两种LLM都在重复预测中表现出显著的变化,这表明可重复性挑战.
结论:
- 通过结构化临床数据,LLM的预测能力有限.
- 在使用非结构化临床文本进行风险预测方面,LLM表现有前途,与传统方法相似.
- 需要在更大的队列中进行进一步的研究和验证,以确定LLM在HF风险预测中的实用性.
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