Gradient Boosting on Decision Trees for Mortality Prediction in Transcatheter Aortic Valve Implantation

  title={Gradient Boosting on Decision Trees for Mortality Prediction in Transcatheter Aortic Valve Implantation},
  author={Marco Mamprin and Jo M. Zelis and Pim A. L. Tonino and Svitlana Zinger and Peter H. N. de With},
  journal={Proceedings of the 2020 10th International Conference on Biomedical Engineering and Technology},
  • Marco MamprinJ. Zelis P. D. With
  • Published 8 January 2020
  • Computer Science
  • Proceedings of the 2020 10th International Conference on Biomedical Engineering and Technology
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… 

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