Abstract：
This study aims to use patient feature and catheterization technology feature variables to train the corresponding machine learning (ML) models to predict peripherally inserted central catheters-deep vein thrombosis (PICCs-DVT) and analyze the importance of the two types of features to PICCs-DVT from the aspect of \"input-output\" correlation. To comprehensively and systematically summarize the variables used to describe patient features and catheterization technical features, this study combined 18 literature involving the two types of features in predicting PICCs-DVT. A total of 21 variables used to describe the two types of features were summarized, and feature values were extracted from the data of 1,065 PICCs patients from January 1, 2021 to August 31, 2022, to construct a data sample set. Then, 70% of the sample set is used for model training and hyperparameter optimization, and 30% of the sample set is used for PICCs-DVT prediction and feature importance analysis of three common ML classification models (i.e. support vector classifier [SVC], random forest [RF], and artificial neural network [ANN]). In terms of prediction performance, this study selected four metrics to evaluate the prediction performance of the model: precision (P), recall (R), accuracy (ACC), and area under the curve (AUC). In terms of feature importance analysis, this study chooses a single feature analysis method based on the \"input-output\" sensitivity principle-Permutation Importance. For the mean model performance, the three ML models on the test set are P = 0.92, R = 0.95, ACC = 0.88, and AUC = 0.81. Specifically, the RF model is P = 0.95, R = 0.96, ACC = 0.92, AUC = 0.86; the ANN model is P = 0.92, R = 0.95, ACC = 0.88, AUC = 0.81; the SVC model is P = 0.88, R = 0.94, ACC = 0.85, AUC = 0.77. For feature importance analysis, Catheter-to-vein rate (RF: 91.55%, ANN: 82.25%, SVC: 87.71%), Zubrod-ECOG-WHO score (RF: 66.35%, ANN: 82.25%, SVC: 44.35%), and insertion attempt (RF: 44.35%, ANN: 37.65%, SVC: 65.80%) all occupy the top three in the ML models prediction task of PICCs-DVT, showing relatively consistent ranking results. The ML models show good performance in predicting PICCs-DVT and reveal a relatively consistent ranking of feature importance from the data. The important features revealed might help clinical medical staff to better understand and analyze the formation mechanism of PICCs-DVT from a data-driven perspective.
摘要：
本研究旨在使用患者特征和导管插入技术特征变量来训练相应的机器学习（ML）模型，以预测外周中心静脉导管-深静脉血栓形成（PICCs-DVT），并从“输入-输出”相关性方面分析这两种特征对PICCs-DVT的重要性。全面系统地总结用于描述患者特征和导管插入技术特征的变量，本研究结合了18篇涉及预测PICCs-DVT的两种特征的文献.总结了用于描述这两种类型特征的总共21个变量，和特征值从2021年1月1日至2022年8月31日的1,065名PICCs患者数据中提取，构建数据样本集。然后，70%的样本集用于模型训练和超参数优化，并将30%的样本集用于三种常见ML分类模型(即支持向量分类器[SVC]、随机森林[RF]，和人工神经网络[ANN])。在预测性能方面，本研究选择了四个指标来评估模型的预测性能：精度(P)，召回（R），精度（ACC），和曲线下面积(AUC)。在特征重要性分析方面，本研究选择了一种基于“输入-输出”灵敏度原理-排列重要性的单一特征分析方法。对于平均模型性能，测试集上的三个ML模型分别为P=0.92、R=0.95、ACC=0.88和AUC=0.81。具体来说,RF模型为P=0.95,R=0.96,ACC=0.92,AUC=0.86;ANN模型为P=0.92,R=0.95,ACC=0.88,AUC=0.81;SVC模型为P=0.88,R=0.94,ACC=0.85,AUC=0.77。对于特征重要性分析，导管至静脉率（RF：91.55%，ANN：82.25%，SVC：87.71%），Zubrod-ECOG-WHO评分（RF：66.35%，ANN：82.25%，SVC：44.35%），和插入尝试(射频:44.35%，ANN：37.65％，SVC：65.80%）在PICCs-DVT的ML模型预测任务中均占据前三名，显示出相对一致的排名结果。ML模型在预测PICC-DVT方面表现出良好的性能，并从数据中揭示了特征重要性的相对一致的排名。揭示的重要特征可能有助于临床医务人员从数据驱动的角度更好地理解和分析PICC-DVT的形成机制。
