Gradient Boosting on Decision Trees for Mortality Prediction in Transcatheter Aortic Valve Implantation
This work addresses the need for more accurate mortality prediction in TAVI patients, potentially improving clinical decision-making, but it is incremental as it applies an existing machine learning method to a specific medical domain.
The authors tackled the problem of predicting one-year mortality after Transcatheter Aortic Valve Implantation (TAVI) by developing a machine learning model based on gradient boosting on decision trees, achieving an AUC of 0.83 on 270 cases and outperforming established statistical risk scores like EuroSCORE II.
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.