不同机器学习方法在预测内血管动脉瘤修复后的长期存活率方面的比较
Toshiya Nishibe1,2, Tsuyoshi Iwasa1, Namie Toda1
1Department of Medical Informatics and Management, Hokkaido Information University, Ebetsu, Japan.
Vascular and endovascular surgery
|December 23, 2025
概括
随机森林机器学习模型在预测内血管动脉瘤修复 (EVAR) 后的5年生存率方面表现有希望,识别了营养不良和晚年等关键风险因素,以改善患者分层.
科学领域:
- 血管外科 血管外科
- 医疗信息学 医疗信息学
- 生物统计学 生物统计学
背景情况:
- 腹腔大动脉动脉瘤内血管动脉瘤修复后的长期存活 (EVAR) 是一个重要的临床挑战,特别是在患有并发症的老年患者中.
- 准确预测死亡风险对于优化患者选择和术前评估至关重要.
研究的目的:
- 为了比较不同机器学习 (ML) 模型在EVAR后预测5年全因死亡率方面的表现.
- 确定影响EVAR后长期生存的关键临床变量.
主要方法:
- 在2013年至2018年期间接受选择性EVAR的142名患者的回顾性分析.
- 三种监督的ML模型的开发和内部验证 (5倍交叉验证):随机森林 (RF),极端梯度提升 (XGBoost) 和支持向量分类 (SVC).
- 使用准确度,灵敏度,特异性,精度,F1得分和AUC评估模型性能;分析RF和XGBoost的特征重要性.
主要成果:
- 随机森林 (RF) 模型表现出最一致的性能,交叉验证AUC为0.77.
- 极端梯度提升 (XGBoost) 实现了更高的训练精度,但交叉验证性能较低 (AUC 0.68).
- 死亡率的关键预测因素包括营养状况不佳,八十岁的年龄,免疫力受损和活跃的癌症.
结论:
- 基于树的ML模型,特别是RF,是预测EVAR后长期生存的有效工具.
- 将已识别的临床预测因子纳入术前评估,可以改善EVAR患者的风险分层.
- 未来的研究应该专注于外部验证,并纳入时间到事件模型,以提高预后准确性.
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