Gradient boosting on decision trees for mortality prediction in transcatheter aortic valve implantation

Marco Mamprin, Jo M. Zelis, Pim A. L. Tonino, Svitlana Zinger, Peter H. N. de With

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Abstract

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.
Original languageEnglish
Title of host publicationICBET 2020: Proceedings of the 2020 10th International Conference on Biomedical Engineering and Technology
PublisherAssociation for Computing Machinery, Inc
Pages325-329
Number of pages5
ISBN (Electronic)9781450377249
ISBN (Print)9781450377249
DOIs
Publication statusPublished - 1 Sep 2020
Event10th Internation Conference on Biomedical Engineering and Technology : ICBET 2020 will be held virtually - Tokyo, Japan
Duration: 15 Sep 202018 Sep 2020

Publication series

NameACM International Conference Proceeding Series

Conference

Conference10th Internation Conference on Biomedical Engineering and Technology
Abbreviated titleICBET2020
CountryJapan
CityTokyo
Period15/09/2018/09/20

Keywords

  • Aortic valve disease
  • Machine learning
  • One-year mortality prediction
  • Outcome prediction
  • TAVI

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