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使用基于机器学习的决策树分析来预测血管内动脉修复后动脉瘤囊收缩的预测
Toshiya Nishibe1, Tsuyoshi Iwasa2, Seiji Matsuda2
1Faculty of Medical Informatics, Hokkaido Information University, Ebetsu, Japan; Department of Cardiovascular Surgery, Tokyo Medical University, Tokyo, Japan.
The Journal of surgical research
|January 10, 2025
概括
一个机器学习模型预测腹腔大动脉动脉瘤囊收缩在内血管大动脉修复后. 关键预测因素包括手术前脉冲波速度,II型内脏漏洞和吸烟状态,有助于风险分层.
科学领域:
- 血管外科 血管外科
- 医疗信息学 医疗信息学
- 生物医学工程 生物医学工程
背景情况:
- 腹腔大动脉动脉瘤 (AAA) 对健康构成重大风险.
- 内血管大动脉修复 (EVAR) 是AAA的常见治疗方法.
- 在EVAR后预测动脉瘤囊收缩对于患者管理至关重要.
研究的目的:
- 开发一种简单的风险分层模型,用于预测在接受AAAEAR的患者中动脉瘤囊收缩的风险.
- 为了实现这一预测,利用基于机器学习的决策树分析.
主要方法:
- 分析了119名接受AAA选择性EVAR治疗的患者队列.
- 动脉瘤囊收缩的预测因素通过无变量分析 (P <0.05) 确定.
- 然后将这些预测因素纳入决策树分析.
主要成果:
- 手术前的脉冲波速度 (PWV),手术前的II型内腔漏洞和目前的吸烟是显著的预测因素.
- 决策树分析发现PWV是最相关的预测因素,其次是II型内和吸烟.
- 该模型生成了6个终端节点,收缩概率从5.6%到63.6%不等.
结论:
- 建立了一个决策树模型,包括手术前的PWV,手术后的II型内孔,以及当前的吸烟.
- 这种模型有效地预测了对AAA进行EVAR的患者动脉瘤囊收缩.
- 该分类模型可以帮助识别具有动脉瘤囊收缩概率高或低的患者.
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