Use of machine-learning algorithms to determine features of systolic blood pressure variability that predict poor outcomes in hypertensive patients

BACKGROUND: We re-analyzed data from the Systolic Blood Pressure Intervention Trial (SPRINT) trial to identify features of systolic blood pressure (SBP) variability that portend poor cardiovascular outcomes using a nonlinear machine-learning algorithm. METHODS: We included all patients who completed...

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Detalles Bibliográficos
Autores principales: Lacson, Ronilda C, Baker, Bowen, Suresh, Harini, Andriole, Katherine, Szolovits, Peter, Lacson, Eduardo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2018
Materias:
Hypertension
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6452173/
https://www.ncbi.nlm.nih.gov/pubmed/30976397
http://dx.doi.org/10.1093/ckj/sfy049

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6452173/
https://www.ncbi.nlm.nih.gov/pubmed/30976397
http://dx.doi.org/10.1093/ckj/sfy049

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