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

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademicpeer review

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Samenvatting

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.
Originele taal-2Engels
TitelICBET 2020: Proceedings of the 2020 10th International Conference on Biomedical Engineering and Technology
UitgeverijAssociation for Computing Machinery, Inc
Pagina's325-329
Aantal pagina's5
ISBN van elektronische versie9781450377249
ISBN van geprinte versie9781450377249
DOI's
StatusGepubliceerd - 1 sep 2020
Evenement10th Internation Conference on Biomedical Engineering and Technology : ICBET 2020 will be held virtually - Tokyo, Japan
Duur: 15 sep 202018 sep 2020

Publicatie series

NaamACM International Conference Proceeding Series

Congres

Congres10th Internation Conference on Biomedical Engineering and Technology
Verkorte titelICBET2020
LandJapan
StadTokyo
Periode15/09/2018/09/20

Trefwoorden

  • cs.LG
  • cs.CY
  • stat.ML

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