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使用机器学习来预测超上绕道术后的结果.

Ben Li1, Naomi Eisenberg2, Derek Beaton3

  • 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.

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机器学习模型准确地预测了外周动脉疾病 (PAD) 的上外围手术后的结果. 这些算法优于物流回归,可以指导风险缓解策略,以防止不良事件.

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机器学习是机器学习.结果结果的结果.周围动脉疾病是什么?预测 预测 预测上侧绕道 (Suprainguinal bypass) 是一种超上侧绕道.

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科学领域:

  • 血管外科 血管外科
  • 机器学习应用 机器学习应用
  • 结果预测结果预测

背景情况:

  • 周围动脉疾病 (PAD) 的上绕道术具有显著的外科风险.
  • 现有的预测该程序后结果的工具有限.
  • 需要改进风险分层和管理策略.

研究的目的:

  • 开发和评估机器学习 (ML) 算法,用于预测超上绕道后的结果.
  • 将ML模型的性能与传统的物流回归进行比较.
  • 为了识别上周道绕道术后不良事件的关键预测因素.

主要方法:

  • 利用血管质量倡议数据库 (2003-2023) 来获取患者数据.
  • 开发了6个ML模型 (包括XGBoost,随机森林,后勤回归) 使用手术前,手术内和手术后的变量.
  • 主要结局:主要不良四肢事件 (MALE) 或1年后死亡;使用接收器操作特征曲线 (AUROC) 下面的区域进行评估.

主要成果:

  • XGBoost模型显示出卓越的预测性能,AUROC为0.92 (手术前),显著超过后勤回归 (AUROC 0.67).
  • 在所有阶段,XGBoost的表现仍然很高:0.93 (手术内) 和0.98 (术后).
  • 关键预测因素包括慢性肢体威胁性缺血,先前的手术,并发症和功能状态.

结论:

  • 开发了准确的ML模型来预测1年的MALE或超周道后的死亡.
  • 这些ML算法比后勤回归提供了更好的性能.
  • 这些模型显示了指导外科手术期间风险缓解以获得更好的患者结果的潜在实用性.