二尖瓣置换术的机器学习死亡率预测

IF 0.2 Q4 CARDIAC & CARDIOVASCULAR SYSTEMS
Marziyeh HosseiniNezhad, M. Langarizadeh, S. Hosseini
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引用次数: 2

摘要

背景:在过去的几年中,伊朗的二尖瓣置换术有所增加。对于结果的优化,与另一个过程一样,需要对结果进行统计评价,然后是结果预测系统。因此,在本研究中,我们生成了一个基于机器学习(ML)的模型来预测孤立二尖瓣置换术(IMVR)后的住院死亡率。材料与方法:选取2005年2月至2016年8月在某三级心脏医院行IMVR的患者。回顾性收集资料,包括基线特征、超声心动图和手术特征以及患者预后。采用logistic回归(LR)、线性判别分析(LDA)、支持向量机(SVM)、k近邻(KNN)和多层感知器(MLP)等5种监督式机器学习分类器建立住院死亡率预测模型。结果:本研究共分析了1200例IMVRs。研究人群被随机分为训练集(n = 840)和测试集(n = 360)。住院总死亡率为4.2%。LR模型在预测IMVR术后死亡率的22个变量中具有最好的判别性,其受体-工作曲线下面积(AUC)、特异性和敏感性分别为0.68、0.73和0.58。LDA模型的AUC为0.73,而SVM模型为0.56,KNN模型为0.51,MLP模型为0.5。结论:我们开发了一个强大的ml衍生模型来预测IMVR患者的住院死亡率。该模型有希望用于决策,值得进一步的临床验证。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Mortality prediction of mitral valve replacement surgery by machine learning
Background: Mitral valve replacement procedure has increased in the Iran over the last years. For optimization of the results, as the other procedure, it needs statistical evaluation of the results, and then a system for the prediction of outcome. Hence, in this study, we generate a machine learning (ML)-based model to predict in-hospital mortality after isolated mitral valve replacement (IMVR). Materials and Methods: The patients who underwent IMVR from February 2005 to August 2016 were identified in a single tertiary heart hospital. Data were retrospectively gathered including baseline characteristics, echocardiographic and surgical features, and patient's outcome. Prediction models for in-hospital mortality were obtained using five supervised ML classifiers including: logistic regression (LR), linear discriminant analysis (LDA), support-vector machine (SVM), K-nearest neighbors (KNN), and multilayer perceptron (MLP). Results: A total of 1200 IMVRs were analyzed in our study. The study population was randomly divided into a training set (n = 840) and a testing set (n = 360). The overall in-hospital mortality was 4.2%. LR model had the best discrimination for 22 variables in predicting mortality after IMVR, with area under the receiver-operating curve (AUC), specificity, and sensitivity of 0.68, 0.73, and 0.58, respectively. A LDA model had an (AUC) of 0.73, compared to 0.56 for SVM, 0.51 for KNN, and 0.5 for MLP. Conclusions: We developed a robust ML-derived model to predict in-hospital mortality in patients undergoing IMVR. This model is promising for decision-making and deserves further clinical validation.
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来源期刊
Research in Cardiovascular Medicine
Research in Cardiovascular Medicine CARDIAC & CARDIOVASCULAR SYSTEMS-
自引率
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发文量
13
审稿时长
17 weeks
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