Explainable SHAP-XGBoost models for in-hospital mortality after myocardial infarction

BACKGROUND: A lack of explainability in published machine learning (ML) models limits clinicians’ understanding of how predictions are made, in turn undermining uptake of the models into clinical practice. OBJECTIVE: The purpose of this study was to develop explainable ML models to predict in-hospit...

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Detalles Bibliográficos
Autores principales: Tarabanis, Constantine, Kalampokis, Evangelos, Khalil, Mahmoud, Alviar, Carlos L., Chinitz, Larry A., Jankelson, Lior
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
Materias:
Original Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10435947/
https://www.ncbi.nlm.nih.gov/pubmed/37600443
http://dx.doi.org/10.1016/j.cvdhj.2023.06.001

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10435947/
https://www.ncbi.nlm.nih.gov/pubmed/37600443
http://dx.doi.org/10.1016/j.cvdhj.2023.06.001

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