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.

中文翻译：

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经导管主动脉瓣植入死亡率预测决策树的梯度提升

当前心脏手术的预后风险评分基于统计数据，尚未从机器学习中受益。统计预测指标不够稳健，无法正确识别可从经导管主动脉瓣植入术 (TAVI) 中受益的患者。这项研究旨在创建一个机器学习模型来预测 TAVI 后患者的一年死亡率。我们在决策树算法上采用现代梯度提升，专为分类特征而设计。结合最近的模型解释技术，我们开发了一个特征分析和选择阶段，能够识别预测中最重要的特征。在与临床专家解释和讨论特征分析结果之后，我们将我们的预测模型基于最相关的特征。我们在 270 个 TAVI 病例上验证了我们的模型，达到了 0.83 的 AUC。我们的方法优于几种广泛使用的预后风险评分，例如全球心脏病专家广泛采用的逻辑 EuroSCORE II、STS 风险评分和 TAVI2 评分。