Paying attention to cardiac surgical risk: An interpretable machine learning approach using an uncertainty-aware attentive neural network

Machine learning (ML) is increasingly applied to predict adverse postoperative outcomes in cardiac surgery. Commonly used ML models fail to translate to clinical practice due to absent model explainability, limited uncertainty quantification, and no flexibility to missing data. We aimed to develop a...

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Detalles Bibliográficos
Autores principales: Penny-Dimri, Jahan C., Bergmeir, Christoph, Reid, Christopher M., Williams-Spence, Jenni, Cochrane, Andrew D., Smith, Julian A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2023
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10468047/
https://www.ncbi.nlm.nih.gov/pubmed/37647308
http://dx.doi.org/10.1371/journal.pone.0289930

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10468047/
https://www.ncbi.nlm.nih.gov/pubmed/37647308
http://dx.doi.org/10.1371/journal.pone.0289930

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