Can machine learning unravel unsuspected, clinically important factors predictive of long-term mortality in complex coronary artery disease? A call for ‘big data’

AIMS: Risk stratification and individual risk prediction play a key role in making treatment decisions in patients with complex coronary artery disease (CAD). The aim of this study was to assess whether machine learning (ML) algorithms can improve discriminative ability and identify unsuspected, but...

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Detalles Bibliográficos
Autores principales: Ninomiya, Kai, Kageyama, Shigetaka, Garg, Scot, Masuda, Shinichiro, Kotoku, Nozomi, Revaiah, Pruthvi C, O’leary, Neil, Onuma, Yoshinobu, Serruys, Patrick W
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2023
Materias:
Short Report
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10232230/
https://www.ncbi.nlm.nih.gov/pubmed/37265868
http://dx.doi.org/10.1093/ehjdh/ztad014

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10232230/
https://www.ncbi.nlm.nih.gov/pubmed/37265868
http://dx.doi.org/10.1093/ehjdh/ztad014

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