Open AccessMachine learning and blood pressure
Author(s) -
Santhanam Prasanna,
Ahima Rexford S.
Publication year - 2019
Publication title -
the journal of clinical hypertension
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.909
H-Index - 67
eISSN - 1751-7176
pISSN - 1524-6175
DOI - 10.1111/jch.13700
Subject(s) - medicine , blood pressure , artificial intelligence , machine learning , artificial neural network , waist , disease , obesity , computer science
Machine learning (ML) is a type of artificial intelligence (AI) based on pattern recognition. There are different forms of supervised and unsupervised learning algorithms that are being used to identify and predict blood pressure (BP) and other measures of cardiovascular risk. Since 1999, starting with neural network methods, ML has been used to gauge the relationship between BP and pulse wave forms. Since then, the scope of the research has expanded to using different cardiometabolic risk factors like BMI, waist circumference, waist‐to‐hip ratio in concert with BP and its various pharmaceutical agents to estimate biochemical measures (like HDL cholesterol, LDL and total cholesterol, fibrinogen, and uric acid) as well as effectiveness of anti‐hypertensive regimens. Data from large clinical trials like the SPRINT are being re‐analyzed by ML methods to unearth new findings and identify unique relationships between predictors and outcomes. In summary, AI and ML methods are gaining immense attention in the management of chronic disease. Elevated BP is a very important early metric for the risk of development of cardiovascular and renal injury; therefore, advances in AI and ML will aid in early disease prediction and intervention.