Cardiovascular disease risk prediction using automated machine learning: A prospective study of 423,604 UK Biobank participants

BACKGROUND: Identifying people at risk of cardiovascular diseases (CVD) is a cornerstone of preventative cardiology. Risk prediction models currently recommended by clinical guidelines are typically based on a limited number of predictors with sub-optimal performance across all patient groups. Data-...

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Detalles Bibliográficos
Autores principales: Alaa, Ahmed M., Bolton, Thomas, Di Angelantonio, Emanuele, Rudd, James H. F., van der Schaar, Mihaela
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2019
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6519796/
https://www.ncbi.nlm.nih.gov/pubmed/31091238
http://dx.doi.org/10.1371/journal.pone.0213653

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6519796/
https://www.ncbi.nlm.nih.gov/pubmed/31091238
http://dx.doi.org/10.1371/journal.pone.0213653

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