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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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| Publicado no: | PLoS One |
|---|---|
| Main Authors: | , , , , |
| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
Public Library of Science
2019
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| Assuntos: | |
| Acesso em linha: | https://ncbi.nlm.nih.gov/pmc/articles/PMC6519796/ https://ncbi.nlm.nih.gov/pubmed/31091238 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1371/journal.pone.0213653 |
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