ArticleJournal of clinical hypertension (Greenwich, Conn.)2019
Machine learning and blood pressure.
Article in Journal of clinical hypertension (Greenwich, Conn.), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed.
- Predicting incident cardio-metabolic disease among persons with and without depressive and anxiety disorders: a machine learning approach.Social psychiatry and psychiatric epidemiology · 2025Article
- Exploring the diagnostic performance of machine learning in prediction of metabolic phenotypes focusing on thyroid function.PloS one · 2024Article
- Artificial Intelligence in Hypertension Management: An Ace up Your Sleeve.Journal of cardiovascular development and disease · 2023Review
- Circulating sepsis-related metabolite sphinganine could protect against intestinal damage during sepsis.Frontiers in immunology · 2023Article
- Classification and Prediction on Hypertension with Blood Pressure Determinants in a Deep Learning Algorithm.International journal of environmental research and public health · 2022Article
- Future possibilities for artificial intelligence in the practical management of hypertension.Hypertension research : official journal of the Japanese Society of Hypertension · 2020Review
- Artificial intelligence may offer insight into factors determining individual TSH level.PloS one · 2020Article
- Machine learning and blood pressure.Journal of clinical hypertension (Greenwich, Conn.) · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.