Machine learning and blood pressure

Research output: Contribution to journalArticle

Abstract

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.

Original languageEnglish (US)
JournalJournal of Clinical Hypertension
DOIs
StateAccepted/In press - Jan 1 2019

Fingerprint

Blood Pressure
Artificial Intelligence
Waist-Hip Ratio
Waist Circumference
Uric Acid
LDL Cholesterol
Fibrinogen
HDL Cholesterol
Antihypertensive Agents
Chronic Disease
Machine Learning
Clinical Trials
Learning
Kidney
Wounds and Injuries
Research
Pharmaceutical Preparations

Keywords

  • blood pressure
  • machine learning

ASJC Scopus subject areas

  • Internal Medicine
  • Endocrinology, Diabetes and Metabolism
  • Cardiology and Cardiovascular Medicine

Cite this

Machine learning and blood pressure. / Santhanam, Prasanna; Ahima, Rexford S.

In: Journal of Clinical Hypertension, 01.01.2019.

Research output: Contribution to journalArticle

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