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

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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Bibliographic Details
Main Authors: Lacson, Ronilda, Baker, Bowen, Suresh, Harini, Andriole, Katherine, Szolovits, Peter, Lacson, Eduardo
Other Authors: Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Format: Article
Language:English
Published: Oxford University Press 2019
Online Access:https://hdl.handle.net/1721.1/122821

Internet

https://hdl.handle.net/1721.1/122821

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