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使用机器学习来预测开放腹腔大动脉瘤修复后的结果

Ben Li¹, Badr Aljabri², Raj Verma³

¹Department of Surgery, University of Toronto, Toronto, ON, Canada; Division of Vascular Surgery, St. Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada; Institute of Medical Science, University of Toronto, Toronto, ON, Canada; Temerty Centre for Artificial Intelligence Research and Education in Medicine (T-CAIREM), University of Toronto, Toronto, ON, Canada.

Journal of vascular surgery

|August 27, 2023

概括

机器学习模型准确地预测了开放腹腔大动脉瘤 (AAA) 修复后的结果,优于传统方法. 这些工具可以指导外科手术期间的管理并改善患者的治疗结果.

科学领域:

心血管外科心血管外科
医疗信息学医疗信息学
医疗保健中的机器学习

背景情况:

预测开放腹腔大动脉瘤 (AAA) 修复的结果是具有挑战性的,因为缺乏有效的术后管理工具.
机器学习 (ML) 为改善复杂外科手术过程中的结果预测提供了一个潜在的解决方案.

研究的目的:

开发和评估机器学习算法,用于预测开放AAA修复后的结果.
将ML模型的性能与预测结果的传统后勤回归进行比较.

主要方法:

利用了血管质量倡议 (VQI) 数据库 (2003-2023年),其中包括12027名接受选择性开放AAA修复的患者.
训练了6个ML模型 (包括XGBoost,随机森林,后勤回归),使用52个手术前变量,并使用10倍交叉验证进行评估.
通过使用接收器操作特征曲线 (AUROC) 下面的区域,校准图表和Brier分数来评估模型性能,主要的结果是主要的心血管不良事件 (MACE).

主要成果:

XGBoost模型在预测住院MACE方面表现出卓越的性能,AUROC为0.93,显著超过后勤回归 (AUROC0.71).
对于二次结果,XGBoost实现了0.84-0.94之间的AUROC,显示了良好的校准和0.05.05的Brier分数.
对于MACE的关键预测因素包括冠状动脉疾病,美国麻醉学会分类和近位位,在子组中表现强.

结论:

机器学习模型,特别是XGBoost,可以使用手术前数据准确预测开放AAA修复后的结果.
这些ML算法比后勤回归提供了显著的改进,可以帮助指导风险减轻策略.
开发的模型有潜力增强外科手术期间的管理,并改善开放AAA修复的患者结果.

关键词:

机器学习机器学习开放腹腔大动脉动脉瘤修复结果的结果.预测预测预测

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