Applications of artificial intelligence for hypertension management

Kelvin Tsoi^{1

2}, Karen Yiu¹, Helen Lee², Hao-Min Cheng^{3

4

5

6}, Tzung-Dau Wang^{7

8}, Jam-Chin Tay⁹, Boon Wee Teo¹⁰, Yuda Turana¹¹, Arieska Ann Soenarta¹², Guru Prasad Sogunuru¹³, Saulat Siddique¹⁴, Yook-Chin Chia^{15

16}, Jinho Shin¹⁷, Chen-Huan Chen³, Ji-Guang Wang¹⁸, Kazuomi Kario¹⁹; HOPE Asia Network

Affiliations

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.

PMID: 33533536
PMCID: PMC8029548
DOI: 10.1111/jch.14180

Review

Applications of artificial intelligence for hypertension management

Kelvin Tsoi et al. J Clin Hypertens (Greenwich). 2021 Mar.

. 2021 Mar;23(3):568-574.

doi: 10.1111/jch.14180. Epub 2021 Feb 3.

Authors

Affiliations

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.

PMID: 33533536
PMCID: PMC8029548
DOI: 10.1111/jch.14180

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.

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Conflict of interest statement

HM Cheng received speakers honorarium and sponsorship to attend conferences and CME seminars from Eli Lilly and AstraZeneca; Pfizer Inc; Bayer AG; Boehringer Ingelheim Pharmaceuticals, Inc; Daiichi Sankyo, Novartis Pharmaceuticals, Inc; SERVIER; Co., Pharmaceuticals Corporation; Sanofi; TAKEDA Pharmaceuticals International and served as an advisor or consultant for ApoDx Technology, Inc YC Chia has received honorarium and sponsorship at attend conferences and seminars from Boeringher‐Ingelheim, Pfizer, Omron, Servier and Xepa‐Sol and an investigator‐initiated research grant from Pfizer. J Shin has received lecture honoraria from Pfizer Inc, Hanmi Pharm. Co. Ltd., Yuhan Co. Ltd., Boryung Pharmaceutical Co. Ltd.; consulting fees from Hanmi Pharm. Co. Ltd. And Handok Kalos Medical Inc; and research grants from Sanofi Pharm. and Hanmi Pharm. Co. Ltd. CH Chen reports personal fees from Novartis, Sanofi, Daiichi Sankyo, SERVIER, Bayer, and Boehringer Ingelheim Pharmaceuticals, Inc JG Wang reports having received research grants from Chendu Di‐Ao and Omron, and lecture and consulting fees from AstraZeneca, Novartis, Omron, Servier and Takeda. K Kario reports research grants from CureApp Co. and Omron Healthcare, and lecture honorarium from Omron. All other authors report no potential conflicts of interest in relation to this article.

References

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1. Chia Y‐C, Buranakitjaroen P, Chen C‐H, et al. Current status of home blood pressure monitoring in Asia: statement from the HOPE Asia Network. J Clin Hypertens (Greenwich). 2017;19(11):1192‐1201. - PMC - PubMed
1. United Nations . World Population Ageing 2015, Statistical Papers ‐ United Nations (Ser. A): Population and Vital Statistics Report. United Nations, New York; 2017. 10.18356/88fa44e7-en. Accessed November 7, 2019. - DOI

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Applications of artificial intelligence for hypertension management

Affiliations

Applications of artificial intelligence for hypertension management

Authors

Affiliations

Abstract

Conflict of interest statement

References

Publication types

MeSH terms

LinkOut - more resources

Full Text Sources

Other Literature Sources

Medical