使用基于机器学习的决策树分析预测内血管动脉瘤修复后的长期存活率
Toshiya Nishibe1,2, Tsuyoshi Iwasa1, Masaki Kano2
1Department of Medical Informatics and Management, Hokkaido Information University, Ebetsu, Japan.
Vascular and endovascular surgery
|March 24, 2025
概括
机器学习决策树分析预测了内血管动脉瘤修复 (EVAR) 后的长期存活率. 营养状况不佳是关键预测因素,强调全面的手术前评估,以获得更好的患者结果.
科学领域:
- 血管外科 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:
- 内血管动脉瘤修复 (EVAR) 是对腹腔大动脉动脉瘤 (AAA) 的首选微创治疗方法.
- 确定EVAR后患者长期存活率的预测因素对于改善预后至关重要.
- 机器学习 (ML) 提供了在患者数据中发现复杂模式的潜力,以预测结果.
研究的目的:
- 调查基于ML的决策树分析 (DTA) 在预测AAA的EVAR后长期存活 (超过5年) 的有效性.
- 为了确定影响EVAR后长期生存的关键患者因素.
主要方法:
- 对于AAA进行选择性EVAR的142名患者的回顾性分析.
- 使用了24个变量的数据集,包括人口统计,并发病症和手术细节.
- 使用 Python 3.7 和 scikit-learn. learn 开发并验证了一个决策树分类器.
主要成果:
- 糟糕的营养状况成为长期生存的最重要的预测因素.
- 其他关键预测因素包括免疫力受损,活跃癌症,八十岁状态,慢性病和COPD.
- DTA模型实现了76.1%的准确度,81.8%的灵敏度和0.84.8的AUC.
结论:
- 基于ML的DTA在预测EVAR后的长期生存方面是有效的.
- 综合性手术前评估对于识别高风险患者至关重要.
- 根据DTA提供的个性化管理策略可以改善患者的治疗结果.
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