{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T06:49:15Z","timestamp":1768286955755,"version":"3.49.0"},"reference-count":15,"publisher":"Wiley","issue":"11","license":[{"start":{"date-parts":[[2019,9,19]],"date-time":"2019-09-19T00:00:00Z","timestamp":1568851200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J of Clinical Hypertension"],"published-print":{"date-parts":[[2019,11]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>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\u2010to\u2010hip 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\u2010hypertensive regimens. Data from large clinical trials like the SPRINT are being re\u2010analyzed 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.<\/jats:p>","DOI":"10.1111\/jch.13700","type":"journal-article","created":{"date-parts":[[2019,9,19]],"date-time":"2019-09-19T14:37:32Z","timestamp":1568903852000},"page":"1735-1737","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Machine learning and blood pressure"],"prefix":"10.1111","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4822-2705","authenticated-orcid":false,"given":"Prasanna","family":"Santhanam","sequence":"first","affiliation":[{"name":"Division of Endocrinology Diabetes, &amp; Metabolism Department of Medicine Johns Hopkins University School of Medicine Baltimore MD USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rexford S.","family":"Ahima","sequence":"additional","affiliation":[{"name":"Division of Endocrinology Diabetes, &amp; Metabolism Department of Medicine Johns Hopkins University School of Medicine Baltimore MD USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2019,9,19]]},"reference":[{"key":"e_1_2_4_2_1","first-page":"151","volume-title":"Automated Design of Both the Topology and Sizing of Analog Electrical Circuits Using Genetic Programming. 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