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基于多种机器学习方法的深静脉血栓形成稳定的预测模型

Yaxi Yu¹, Min Wang¹, Jianxia Song¹

¹Hebei North University, Zhangjiakou, China.

|February 16, 2026

概括

机器学习模型使用临床数据和计算机断层扫描 (CT) 纹理特征有效预测深静脉血栓 (DVT) 稳定性. 后勤回归模型在识别DVT稳定性方面表现出卓越的性能,有助于临床决策.

科学领域:

医疗成像医学成像
机器学习机器学习
血管医学血管医学

背景情况:

深静脉血栓 (DVT) 构成重大风险,稳定性是预测急性肺栓塞 (APE) 等结果的关键因素.
准确预测DVT稳定性对于及时和适当的临床干预至关重要.

研究的目的:

开发和比较多个机器学习 (ML) 模型来预测DVT稳定性.
用临床和计算机断层扫描 (CT) 纹理特征来确定最佳模型,用于DVT稳定性预测.

主要方法:

分析了108名患有DVT的患者,将他们分为稳定 (没有APE) 和不稳定 (有APE) 组.
从血栓区域提取CT纹理特征,并收集临床数据.
四个ML算法 (逻辑回归,SVM,KNN,XGBoost) 经过训练和验证,使用临床和CT纹理特征的组合.

主要成果:

后勤回归 (LR) 模型实现了最高的预测性能.
LR模型指标包括AUC为0.87,准确度为0.79,精度为0.75,回忆力为0.80,特异性为0.87.
与SVM,KNN和XGBoost模型相比,LR模型表现出优越的预测能力.

结论:

整合临床和CT纹理特征的机器学习模型可以可靠地预测DVT稳定性.
开发的ML模型,特别是LR模型,为评估DVT稳定性和指导患者管理提供了有希望的工具.

关键词:

深静脉血栓形成的原因是深静脉血栓形成.机器学习是机器学习.预测模型预测模型稳定的稳定性稳定的稳定性质地分析,质地分析.

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