A Classification Approach for Risk Prognosis of Patients on Mechanical Ventricular Assistance.
A Classification Approach for Risk Prognosis of Patients on Mechanical Ventricular Assistance.
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DOI：
10.1109/icmla.2010.50
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发表时间：
2010-12-12
期刊：
影响因子：
--
通讯作者：
Antaki JF
中科院分区：
文献类型：
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作者：
Wang Y;Rosé CP;Ferreira A;McNamara DM;Kormos RL;Antaki JF
心室辅助装置（VAD）治疗的最佳候选人的识别是非常重要的未来广泛应用这种挽救生命的技术。近年来，许多传统的统计模型已经开发了这项任务。在这项研究中，我们比较了三种不同的监督机器学习技术对VAD患者的风险预后：决策树，支持向量机（SVM）和贝叶斯树增强网络，以促进候选人的识别。基于SVM识别出的6个特征，并辅以递归特征消除，最终建立了预测（C4.5）决策树模型。与Lietz等人的流行风险评分相比，该模型在识别高风险患者和早期区分高风险和低风险候选人之间的生存期方面表现更好。
The identification of optimal candidates for ventricular assist device (VAD) therapy is of great importance for future widespread application of this life-saving technology. During recent years, numerous traditional statistical models have been developed for this task. In this study, we compared three different supervised machine learning techniques for risk prognosis of patients on VAD: Decision Tree, Support Vector Machine (SVM) and Bayesian Tree-Augmented Network, to facilitate the candidate identification. A predictive (C4.5) decision tree model was ultimately developed based on 6 features identified by SVM with assistance of recursive feature elimination. This model performed better compared to the popular risk score of Lietz et al. with respect to identification of high-risk patients and earlier survival differentiation between high- and low- risk candidates.