肺部手术后早期术后低albuminemia的机器学习预测模型:一个回顾性案例匹配比较研究
Wei Mao1, Huer Gao2, Yeyan Hu3
1Department of Shanghai Lung Cancer Center, Shanghai Chest Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
Journal of thoracic disease
|February 9, 2026
概括
机器学习可以准确地预测肺癌患者进行肺部手术的早期术后低albuminemia (EPH). 这种预测模型有助于识别高风险个体,以便更好地进行术后管理.
科学领域:
- 胸部外科手术 胸部外科手术
- 在瘤学瘤学.
- 医疗信息学 医疗信息学
背景情况:
- 术后的低albuminemia与感染和肺部手术后的不良预后等不良结果有关.
- 早期的术后低albuminemia (EPH) 是更常见的,并与并发症相关.
- 目前的风险评估方法有限; 没有机器学习模型用于EPH预测.
研究的目的:
- 开发一种机器学习 (ML) 模型,用于预测肺部手术患者的EPH.
- 为胸部外科提供一种新的风险分层和临床决策工具.
- 改善外科手术期间的管理和患者的治疗结果.
主要方法:
- 从1048名接受选择性肺部手术 (2022-2024) 的肺癌患者回顾收集数据.
- 使用37个变量开发后勤回归 (LR) 模型,通过 1:1 病例对照匹配和交叉验证进行验证.
- 使用AUC,准确度,精度,回忆,F1得分和布里尔得分进行性能评估; SHAP用于解释.
主要成果:
- 基于LR的ML模型实现了0.8543.3的AUC.
- 关键性能指标包括精度 (0.7947),回忆 (0.7309),F1得分 (0.7606) 和准确性 (0.771).
- 布里尔分数为0.1551,表明了良好的校准.
结论:
- 开发的基于LR的ML算法在预测EPH方面表现出色.
- 该模型有效地识别了EPH高风险患者 (血清白蛋白<35g/L在手术后5天内).
- 这种工具为胸部外科手术中术后风险评估提供了宝贵的进步.
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