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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...
| Autores principales: | , , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Elsevier
2023
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| Materias: | |
| 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 |