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基于机器学习的随机森林预测了内血管动脉瘤修复后的3年生存期

Toshiya Nishibe^1,2,3, Tsuyoshi Iwasa¹, Seiji Matsuda¹

¹Department of Medical Management and Informatics, Hokkaido Information University, Ebetsu, Hokkaido, Japan.

Annals of thoracic and cardiovascular surgery : official journal of the Association of Thoracic and Cardiovascular Surgeons of Asia

|May 14, 2025

概括

一个机器学习模型预测了内血管动脉瘤修复 (EVAR) 后的3年生存期. 关键预测因素包括营养状况,免疫力,慢性病和年龄,有助于对腹腔大动脉动脉瘤 (AAA) 的患者风险分层.

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:

内血管动脉瘤修复 (EVAR) 是对腹腔大动脉瘤 (AAA) 的常见治疗方法.
在EVAR后的中期生存率需要改进和更好的预测方法.
准确预测患者的生存率对于优化治疗策略至关重要.

研究的目的:

开发和验证用于预测EVAR后3年生存期的机器学习模型.
确定影响EVAR后生存的关键术前和术后因素.
为了增强对AAA进行EVAR的患者的风险分层.

主要方法:

开发了一个随机森林机器学习模型.
分析了接受EVAR治疗的169名患者的数据,并纳入了23个变量.
模型性能使用5倍交叉验证进行评估,评估AUC,精度,灵敏度,特异性和F1评分.

主要成果:

随机森林模型表现出强大的预测性能,AUC为0.91,准确率为81.1%.
三年死亡率的重要预测因素包括营养状况不佳,免疫力受损,慢性病 (CKD),八岁以上的状态,慢性阻塞性肺病 (COPD),小动脉瘤大小和他类药物的使用.
交叉验证证实了不同数据子集的一致模型性能.

结论:

一个基于机器学习的随机森林模型有效地预测了EVAR后的3年生存期.
确定的风险因素,如营养状况,免疫功能,CKD,晚年,COPD,动脉瘤大小和他类药物的使用对于预测死亡率至关重要.
这个模型可以帮助临床医生评估患者的风险,并为AAA量身定制治疗.

关键词:

腹部大动脉动脉瘤腹部大动脉动脉瘤血管内动脉瘤修复血管内动脉瘤修复重要的特征重要的特征重要的特征机器学习是机器学习.随机的森林随机的森林

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