论文标题
在经导管主动脉瓣植入的死亡率预测的决策树上提高梯度
Gradient Boosting on Decision Trees for Mortality Prediction in Transcatheter Aortic Valve Implantation
论文作者
论文摘要
心脏手术中当前的预后风险评分是基于统计数据，但尚未受益于机器学习。统计预测因子不足以正确识别将受益于经导管主动脉瓣植入（TAVI）的患者。这项研究旨在创建机器学习模型，以预测塔维之后患者的一年死亡率。我们对决策树算法采用现代梯度提升，该算法是专门为分类特征设计的。结合最近用于模型解释的技术，我们开发了一个功能分析和选择阶段，从而确定了预测的最重要功能。在与临床专家解释和讨论了特征分析结果之后，我们将预测模型基于最相关的功能。我们验证了270个TAVI病例的模型，AUC为0.83。我们的方法的表现优于几个广泛的预后风险评分，例如Logistic Euroscore II，STS风险评分和TAVI2-Score，这些评分是由全球心脏病专家广泛采用的。
Current prognostic risk scores in cardiac surgery are based on statistics and do not yet benefit from machine learning. Statistical predictors are not robust enough to correctly identify patients who would benefit from Transcatheter Aortic Valve Implantation (TAVI). This research aims to create a machine learning model to predict one-year mortality of a patient after TAVI. We adopt a modern gradient boosting on decision trees algorithm, specifically designed for categorical features. In combination with a recent technique for model interpretations, we developed a feature analysis and selection stage, enabling to identify the most important features for the prediction. We base our prediction model on the most relevant features, after interpreting and discussing the feature analysis results with clinical experts. We validated our model on 270 TAVI cases, reaching an AUC of 0.83. Our approach outperforms several widespread prognostic risk scores, such as logistic EuroSCORE II, the STS risk score and the TAVI2-score, which are broadly adopted by cardiologists worldwide.
