Applications of artificial intelligence for hypertension management
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 adv...
| Main Authors: | , , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-03-01
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| Series: | The Journal of Clinical Hypertension |
| Online Access: | https://doi.org/10.1111/jch.14180 |
| _version_ | 1797645161291841536 |
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| author | Kelvin Tsoi Karen Yiu Helen Lee Hao‐Min Cheng Tzung‐Dau Wang Jam‐Chin Tay Boon Wee Teo Yuda Turana Arieska Ann Soenarta Guru Prasad Sogunuru Saulat Siddique Yook‐Chin Chia Jinho Shin Chen‐Huan Chen Ji‐Guang Wang Kazuomi Kario the HOPE Asia Network |
| author_facet | Kelvin Tsoi Karen Yiu Helen Lee Hao‐Min Cheng Tzung‐Dau Wang Jam‐Chin Tay Boon Wee Teo Yuda Turana Arieska Ann Soenarta Guru Prasad Sogunuru Saulat Siddique Yook‐Chin Chia Jinho Shin Chen‐Huan Chen Ji‐Guang Wang Kazuomi Kario the HOPE Asia Network |
| author_sort | Kelvin Tsoi |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T14:42:12Z |
| format | Article |
| id | doaj.art-65df154bcdd548a193de7d53a56a1fbb |
| institution | Directory Open Access Journal |
| issn | 1524-6175 1751-7176 |
| language | English |
| last_indexed | 2024-03-11T14:42:12Z |
| publishDate | 2021-03-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Journal of Clinical Hypertension |
| spelling | doaj.art-65df154bcdd548a193de7d53a56a1fbb2023-10-30T13:30:41ZengWileyThe Journal of Clinical Hypertension1524-61751751-71762021-03-0123356857410.1111/jch.14180Applications of artificial intelligence for hypertension managementKelvin Tsoi0Karen Yiu1Helen Lee2Hao‐Min Cheng3Tzung‐Dau Wang4Jam‐Chin Tay5Boon Wee Teo6Yuda Turana7Arieska Ann Soenarta8Guru Prasad Sogunuru9Saulat Siddique10Yook‐Chin Chia11Jinho Shin12Chen‐Huan Chen13Ji‐Guang Wang14Kazuomi Kario15the HOPE Asia NetworkSH Big Data Decision and Analytics Research Centre Shatin Hong KongSH Big Data Decision and Analytics Research Centre Shatin Hong KongJC School of Public Health and Primary Care The Chinese University of Hong Kong Shatin Hong KongDivision of Cardiology Department of Medicine Taipei Veterans General Hospital Taipei TaiwanCardiovascular Center and Division of Cardiology Department of Internal Medicine National Taiwan University Hospital Taipei City TaiwanDepartment of General Medicine Tan Tock Seng Hospital Singapore SingaporeDivision of Nephrology Department of Medicine Yong Loo Lin School of Medicine Singapore SingaporeDepartment of Neurology School of Medicine and health Sciences Atma Jaya Catholic University of Indonesia IndonesiaDepartment of Cardiology and Vascular Medicine Faculty of Medicine University of Indonesia Jakarta IndonesiaDepartment of Cardiology MIOT international hospital Chennai IndiaPunjab Medical Center Lahore PakistanDepartment of Medical Sciences School of Healthcare and Medical Sciences Sunway University Bandar Sunway MalaysiaFaculty of Cardiology Service Hanyang University Medical Center Seoul KoreaDivision of Cardiology Department of Medicine Taipei Veterans General Hospital Taipei TaiwanDepartment 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 ChinaDivision of Cardiovascular Medicine Department of Medicine Jichi Medical University School of Medicine Tochigi JapanAbstract 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.https://doi.org/10.1111/jch.14180 |
| spellingShingle | Kelvin Tsoi Karen Yiu Helen Lee Hao‐Min Cheng Tzung‐Dau Wang Jam‐Chin Tay Boon Wee Teo Yuda Turana Arieska Ann Soenarta Guru Prasad Sogunuru Saulat Siddique Yook‐Chin Chia Jinho Shin Chen‐Huan Chen Ji‐Guang Wang Kazuomi Kario the HOPE Asia Network Applications of artificial intelligence for hypertension management The Journal of Clinical Hypertension |
| title | Applications of artificial intelligence for hypertension management |
| title_full | Applications of artificial intelligence for hypertension management |
| title_fullStr | Applications of artificial intelligence for hypertension management |
| title_full_unstemmed | Applications of artificial intelligence for hypertension management |
| title_short | Applications of artificial intelligence for hypertension management |
| title_sort | applications of artificial intelligence for hypertension management |
| url | https://doi.org/10.1111/jch.14180 |
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