Applications of artificial intelligence for hypertension management.

Affiliation

Tsoi K(1)(2), Yiu K(1), Lee H(2), Cheng HM(3)(4)(5)(6), Wang TD(7)(8), Tay JC(9), Teo BW(10), Turana Y(11), Soenarta AA(12), Sogunuru GP(13), Siddique S(14), Chia YC(15)(16), Shin J(17), Chen CH(3), Wang JG(18), Kario K(19); HOPE Asia Network.
Author information:
(1)SH Big Data Decision and Analytics Research Centre, Shatin, Hong Kong.
(2)JC School of Public Health and Primary Care, The Chinese University of Hong Kong, Shatin, Hong Kong.
(3)Division of Cardiology, Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan.
(4)Faculty of Medicine, National Yang-Ming University School of Medicine, Taipei, Taiwan.
(5)Institute of Public Health and Community Medicine Research Center, National Yang-Ming University School of Medicine, Taipei, Taiwan.
(6)Center for Evidence-based Medicine, Department of Medical Education, Taipei Veterans General Hospital, Taipei, Taiwan.
(7)Cardiovascular Center and Division of Cardiology, Department of Internal Medicine, National Taiwan University Hospital, Taipei City, Taiwan.
(8)Division of Hospital Medicine, Department of Internal Medicine, National Taiwan University Hospital, Taipei City, Taiwan.
(9)Department of General Medicine, Tan Tock Seng Hospital, Singapore, Singapore.
(10)Division of Nephrology Department of Medicine, Yong Loo Lin School of Medicine, Singapore, Singapore.
(11)Department of Neurology, School of Medicine and health Sciences, Atma Jaya Catholic University of Indonesia, Indonesia.
(12)Department of Cardiology and Vascular Medicine, Faculty of Medicine, University of Indonesia, Jakarta, Indonesia.
(13)Department of Cardiology, MIOT international hospital, Chennai, India.
(14)Punjab Medical Center, Lahore, Pakistan.
(15)Department of Medical Sciences, School of Healthcare and Medical Sciences, Sunway University, Bandar Sunway, Malaysia.
(16)Faculty of Medicine, Department of Primary Care Medicine, University of Malaya, Kuala Lumpur, Malaysia.
(17)Faculty of Cardiology Service, Hanyang University Medical Center, Seoul, Korea.
(18)Department of Hypertension, Centre for Epidemiological Studies and Clinical Trials, The Shanghai Institute of Hypertension, Shanghai Key Laboratory of Hypertension, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China.
(19)Division of Cardiovascular Medicine, Department of Medicine, Jichi Medical University School of Medicine, Tochigi, Japan.

Abstract

The prevalence of hypertension is increasing along with an aging population, causing millions of premature deaths annually worldwide. Low awareness of blood pressure (BP) elevation and suboptimal hypertension diagnosis serve as the major hurdles in effective hypertension management. The advent of artificial intelligence (AI), however, sheds the light of new strategies for hypertension management, such as remote supports from telemedicine and big data-derived prediction. There is considerable evidence demonstrating the feasibility of AI applications in hypertension management. A foreseeable trend was observed in integrating BP measurements with various wearable sensors and smartphones, so as to permit continuous and convenient monitoring. In the meantime, further investigations are advised to validate the novel prediction and prognostic tools. These revolutionary developments have made a stride toward the future model for digital management of chronic diseases.