大型语言模型用于预测急性冠状动脉综合征的一年主要不良心血管事件
Xiaoming Zhang1,2,3, Luxiang Shang3, Siyu Hou4
1Department of Cardiology, Central Hospital Affiliated to Shandong First Medical University, Jinan, Shandong 250013, China.
iScience
|February 9, 2026
概括
大型语言模型 (LLM) 在急性冠状动脉综合征 (ACS) 患者中难以预测主要不良心血管事件 (MACE). 当前的LLM高估了风险,缺乏传统临床评分对心血管预后的可靠性.
科学领域:
- 心脏病学 心脏病学
- 人工智能的人工智能
- 临床信息学 临床信息学
背景情况:
- 有效的风险分层对于管理急性冠状动脉综合征 (ACS) 至关重要.
- 一般用途的大型语言模型 (LLM) 越来越多地被用于复杂的临床推理任务.
- 心血管预后预测需要对患者数据进行准确的评估.
研究的目的:
- 评估当前LLM在预测ACS患者的一年重大心血管不良事件 (MACE) 的可靠性.
- 将ChatGPT 4o,DeepSeek R1和Grok 3的性能与传统的临床评分进行比较.
- 为了确定LLMs在临床翻译中的限制,用于心血管预后.
主要方法:
- 对三个LLM (ChatGPT 4o,DeepSeek R1,Grok 3) 的定量评估.
- 使用LM-ACS队列 (903名参与者) 和MIMIC数据库 (64名参与者) 的29个指导方针建议特征预测一年的MACE.
- 通过ROC分析和与十个独立运行中的GRACE和TIMI分数进行比较来评估性能.
主要成果:
- 所有评估的LLM都表现出严重的风险高估和大量的输出变化.
- 没有LLM在预测一年的MACE方面显著超过传统的临床分数.
- 聊天GPT 4o表现低于像GRACE和TIMI这样的既定得分,表明临床准确性的局限性.
结论:
- 目前的通用LLM不适合独立预测急性冠状动脉综合征的预后.
- 在心脏病学中,LLM的关联推理能力对可靠的临床翻译提出了根本性的挑战.
- 需要进一步的研究来提高LLM的准确性,并减少临床决策支持的可变性.
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