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基于机器学习的分析影响A型大动脉剖析手术持续时间的因素

Dechao Deng^1,2,3, Xiaoming Zhang⁴, Xiangzhen Feng⁴

¹Center for Medical Artificial Intelligence, Shandong University of Traditional Chinese Medicine, Qingdao, China.

Frontiers in public health

|November 17, 2025

概括

机器学习模型预测了斯坦福A型大动脉剖析 (TAAD) 的手术时间. 影响手术持续时间的关键因素包括体外循环和大动脉闭塞时间,指导手术规划.

科学领域:

心血管外科心血管外科
人工智能在医学中的应用
大动脉疾病研究研究

背景情况:

斯坦福A型大动脉解剖 (TAAD) 是上升大动脉的危急状况,需要立即进行手术.
在TAAD手术中预测和理解影响手术持续时间的因素对于患者的治疗结果和资源管理至关重要.

研究的目的:

开发和验证机器学习模型,用于预测TAAD手术中的手术持续时间.
确定影响外科手术时间的关键手术前和手术后临床因素.

主要方法:

一项回顾性队列研究,涉及505名在2017年12月至2023年3月期间接受TAAD手术的患者.
使用47个特征构建了11个机器学习回归模型,用于预测操作持续时间,使用R2,RMSE和MAE评估性能.
使用SHAP分析来提高模型的解释性,并确定关键的预测特征.

主要成果:

复杂的大动脉手术,特别是根和全门置换,与明显更长的外科手术持续时间有关.
ExtraTrees Regressor模型显示了最高的预测准确度,用于操作持续时间.
SHAP分析确定了外体循环的持续时间,大动脉封闭的持续时间,手术内输血以及大动脉门和根的治疗方法作为关键影响因素.

结论:

成功开发了高性能预测模型,以确定TAAD手术中手术持续时间的关键决定因素.
手术策略,特别是复杂的重建和长期的大动脉封闭,大大影响了手术的长度.
这些发现强调了考虑特定患者亚组和优化手术内管理以减轻延长手术时间的重要性.

关键词:

莎普利的添加式解释斯坦福A型大动脉剖析机器学习是机器学习.预测模型预测模型外科手术持续时间

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