论文标题
用于估计冠状动脉造影和经皮冠状动脉干预措施中KAP辐射剂量的完整特征选择
Full feature selection for estimating KAP radiation dose in coronary angiographies and percutaneous coronary interventions
论文作者
论文摘要
在介入心脏病学（IC）中，辐射剂量的变化非常重要，由于治疗的复杂性，其估计很困难。为了解决这个问题，这项研究的目的是确定最重要的人口统计学和临床特征，以估计冠状动脉造影术（CA）和经皮冠状动脉干预（PCI）中的KERMA-ARAEA产品（KAP）辐射剂量。该研究使用来自838 CA和PCI程序的临床患者数据进行了回顾。从患者数据中提取了总共59个功能，并使用9种不同的基于过滤器的特征选择方法来从治疗中的KAP辐射剂量方面选择最有用的特征。然后将所选特征用于支持矢量回归（SVR）模型，以评估其在估计辐射剂量时的性能。排名最高的十个特征是：1）FN1AC（CA），2）FN2BA（PCI），3）体重，4）体重，4）后史内变0％，5）多卷赛疾病，6）程序数量3，7）暂停前100％，8）美国心脏协会（8）美国心脏协会（AHA）分数（AHA）得分C，9）presisos 85％和10）。 SVR模型的性能提高（平方平方误差〜450），其功能数量大约高达30个功能。识别CA和PCI KAP最有用的特征是确定适合临床实践复杂性模型的重要步骤。最高的功能可以用作IC程序KAP的个体预测指标，也可以将来将其纳入联合的复杂度评分或不同的估计模型中。
In interventional cardiology (IC) the radiation dose variation is very significant, and its estimation has been difficult due to the complexity of the treatments. In order to tackle this problem, the aim of this study was to identify the most important demographic and clinical features to estimate Kerma-Area Product (KAP) radiation dose in coronary angiographies (CA) and percutaneous coronary interventions (PCI). The study was retrospective using clinical patient data from 838 CA and PCI procedures. A total of 59 features were extracted from the patient data and 9 different filter-based feature selection methods were used to select the most informative features in terms of the KAP radiation dose from the treatments. The selected features were then used in a support vector regression (SVR) model to evaluate their performance in estimating the radiation dose. The ten highest-ranking features were: 1) FN1AC (CA), 2) FN2BA (PCI), 3) weight, 4) post-stenosis 0%, 5) multi-vessel disease, 6) number of procedures 3, 7) pre-stenosis 100%, 8) American Heart Association (AHA) score C, 9) pre-stenosis 85% and 10) gender. The performance of the SVR model increased (mean squared error ~ 450) with the number of features approximately up to 30 features. The identification of the most informative features for CA and PCI KAP is an important step in determining suitable complexity models for clinical practice. The highest-ranking features can be used as individual predictors of IC procedure KAP or can be incorporated into combined complexity score or different estimation models in the future.
