Future Direction for Using Artificial Intelligence to Predict and Manage Hypertension

被引:55
作者
Krittanawong, Chayakrit [1 ]
Bomback, Andrew S. [2 ]
Baber, Usman [3 ]
Bangalore, Sripal [4 ]
Messerli, Franz H. [5 ,6 ,7 ]
Tang, W. H. Wilson [8 ,9 ,10 ]
机构
[1] Icahn Sch Med Mt Sinai, Dept Internal Med, Tenth Ave,Suite 3A-09, New York, NY 10019 USA
[2] Columbia Univ, Coll Phys & Surg, Div Nephrol, New York, NY USA
[3] Icahn Sch Med Mt Sinai, Cardiovasc Inst, New York, NY 10029 USA
[4] NYU, Sch Med, Dept Cardiovasc Med, NYU Langone Hlth, New York, NY USA
[5] Mt Sinai Icahn Sch Med, New York, NY USA
[6] Univ Bern, Bern, Switzerland
[7] Jagiellonian Univ Krakow, Krakow, Poland
[8] Heart & Vasc Inst, Dept Cardiovasc Med, Cleveland, OH USA
[9] Lerner Res Inst, Dept Cellular & Mol Med, Cleveland, OH USA
[10] Cleveland Clin, Ctr Clin Genom, Cleveland, OH 44106 USA
关键词
Artificial intelligence; Machine learning; Deep learning; Big data; Wearable technology; Hypertension; CORONARY-ARTERY-DISEASE; BLOOD-PRESSURE TARGETS; NEURAL-NETWORKS; RISK-FACTORS; BIG DATA; CLASSIFICATION; IDENTIFICATION; GUIDELINES; DIAGNOSIS; MORTALITY;
D O I
10.1007/s11906-018-0875-x
中图分类号
R6 [外科学];
学科分类号
1002 ; 100210 ;
摘要
Evidence that artificial intelligence (AI) is useful for predicting risk factors for hypertension and its management is emerging. However, we are far from harnessing the innovative AI tools to predict these risk factors for hypertension and applying them to personalized management. This review summarizes recent advances in the computer science and medical field, illustrating the innovative AI approach for potential prediction of early stages of hypertension. Additionally, we review ongoing research and future implications of AI in hypertension management and clinical trials, with an eye towards personalized medicine. Although recent studies demonstrate that AI in hypertension research is feasible and possibly useful, AI-informed care has yet to transform blood pressure (BP) control. This is due, in part, to lack of data on AI's consistency, accuracy, and reliability in the BP sphere. However, many factors contribute to poorly controlled BP, including biological, environmental, and lifestyle issues. AI allows insight into extrapolating data analytics to inform prescribers and patients about specific factors that may impact their BP control. To date, AI has been mainly used to investigate risk factors for hypertension, but has not yet been utilized for hypertension management due to the limitations of study design and of physician's engagement in computer science literature. The future of AI with more robust architecture using multi-omics approaches and wearable technology will likely be an important tool allowing to incorporate biological, lifestyle, and environmental factors into decision-making of appropriate drug use for BP control.
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页数:16
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