大语言模型在简化冠状动脉外科手术风险评估模型中的应用

doi:10.3969/j.issn.2095-378X.2026.02.003

外科研究与新技术（中英文） ›› 2026, Vol. 15 ›› Issue (2): 107-112.doi: 10.3969/j.issn.2095-378X.2026.02.003

大语言模型在简化冠状动脉外科手术风险评估模型中的应用

董婉悦¹, 王子坤², 朱熹¹, 谢雨欣³, 黄海涛⁴, 张杨杨²

1.南通大学医学院临床医学系, 江苏南通 226007;
2.上海交通大学医学院附属胸科医院心脏外科, 上海 200030;
3.南通大学人工智能与计算机学院人工智能专业, 江苏南通 226000;
4.南通市第一人民医院心脏大血管外科, 江苏南通 226000

收稿日期:2025-09-11 出版日期:2026-06-28 发布日期:2026-07-08
通讯作者: 张杨杨,电子信箱：zhangyangyang_wy@vip.sina.com
作者简介:董婉悦（2001—）,女,本科在读
基金资助:
2024江苏省大学生创新创业训练计划项目（202410304133Y）

Application of large language models to streamline risk assessment of coronary artery surgery

DONG Wanyue¹, WANG Zikun², ZHU Xi¹, XIE Yuxin³, HUANG Haitao⁴, ZHANG Yangyang²

1. Department of Clinical Medicine, Medical College, Nantong University, Nantong 226007, Jiangsu, China;
2. Department of Cardiovascular Surgery, Shanghai Chest Hospital of Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China;
3. Department of Artificial Intelligence, School of Artificial Intelligence and Computer Science, Nantong University, Nantong 226000, Jiangsu, China;
4. Department of Cardiac and Vascular Surgery, Nantong First People’s Hospital, Nantong 226000, Jiangsu, China

Received:2025-09-11 Online:2026-06-28 Published:2026-07-08

摘要/Abstract

摘要： 目的探讨大语言模型（LLMs）在简化冠状动脉旁路移植术（CABG）风险评估的应用可行性和潜力,以及模型的可解释性和临床适用性。方法基于两家心脏中心1 372例CABG患者临床数据,采用OpenAI GPT-o3模型对每例患者术前资料进行语义精简与临床映射,通过少样本提示方式重构评估框架,构建简化手术风险评估模型,并将其与欧洲心血管手术风险评估系统Ⅱ（EuroSCORE Ⅱ）评分模型进行一致性、准确性等比较。结果 LLMs可有效识别手术高相关性风险因子,自动生成结构化手术风险评估提示,所构建的模型在现有数据库中受试者工作特征曲线下面积、灵敏度、特异度等优于EuroSCORE Ⅱ评分模型,并在高风险患者识别上表现突出。结论 LLMs具备辅助构建、简化手术风险评估模型能力,具有临床可适用性和替代现有心脏手术风险评估模型的潜力,可为实现个体化手术风险评估与智能辅助决策提供新的思路。

关键词: 大语言模型, 冠状动脉旁路移植术, 手术风险评估, 人工智能

Abstract: Objective To explore the feasibility and potential of large language models （LLMs） in streamlining risk assessment process of coronary artery bypass grafting （CABG）, with a focus on interpretability and clinic applicability. Methods Using clinical data from 1 372 CABG patients at two cardiac centers, the OpenAI GPT-o3 model was used to process preoperative information to create a simplified risk assessment model via few-shot prompting. This LLM-based model's performance was compared to the European System for Cardiac Operative Risk Evaluation Ⅱ （EuroSCOREⅡ） for consistency and accuracy. Results LLMs excelled at detecting high surgical risks and generating structured risk assessments, surpassing EuroSCOREⅡ in area under the curve （AUC） of receiver operating characteristic, sensitivity, and specificity, thus better identifying high-risk patients. Conclusion LLMs can create and simplify surgical risk assessment models with strong clinical use, potentially complementing or replacing current cardiac surgery risk assessment tools and offering new options for personalized risk evaluation and decision support in clinical settings.

Key words: Large language models, Coronary artery bypass grafting, Surgery risk assessment, Artificial intelligence

中图分类号:

R654.2

董婉悦, 王子坤, 朱熹, 谢雨欣, 黄海涛, 张杨杨. 大语言模型在简化冠状动脉外科手术风险评估模型中的应用[J]. 外科研究与新技术（中英文）, 2026, 15(2): 107-112.

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大语言模型在简化冠状动脉外科手术风险评估模型中的应用

Application of large language models to streamline risk assessment of coronary artery surgery

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