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Applications of artificial intelligence for hypertension management
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 ar...
Autores principales: | , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8029548/ https://www.ncbi.nlm.nih.gov/pubmed/33533536 http://dx.doi.org/10.1111/jch.14180 |
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author | Tsoi, Kelvin Yiu, Karen Lee, Helen Cheng, Hao‐Min Wang, Tzung‐Dau Tay, Jam‐Chin Teo, Boon Wee Turana, Yuda Soenarta, Arieska Ann Sogunuru, Guru Prasad Siddique, Saulat Chia, Yook‐Chin Shin, Jinho Chen, Chen‐Huan Wang, Ji‐Guang Kario, Kazuomi |
author_facet | Tsoi, Kelvin Yiu, Karen Lee, Helen Cheng, Hao‐Min Wang, Tzung‐Dau Tay, Jam‐Chin Teo, Boon Wee Turana, Yuda Soenarta, Arieska Ann Sogunuru, Guru Prasad Siddique, Saulat Chia, Yook‐Chin Shin, Jinho Chen, Chen‐Huan Wang, Ji‐Guang Kario, Kazuomi |
author_sort | Tsoi, Kelvin |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-8029548 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-80295482021-12-16 Applications of artificial intelligence for hypertension management Tsoi, Kelvin Yiu, Karen Lee, Helen Cheng, Hao‐Min Wang, Tzung‐Dau Tay, Jam‐Chin Teo, Boon Wee Turana, Yuda Soenarta, Arieska Ann Sogunuru, Guru Prasad Siddique, Saulat Chia, Yook‐Chin Shin, Jinho Chen, Chen‐Huan Wang, Ji‐Guang Kario, Kazuomi J Clin Hypertens (Greenwich) Treatment and Management of Hypertension 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. John Wiley and Sons Inc. 2021-02-03 /pmc/articles/PMC8029548/ /pubmed/33533536 http://dx.doi.org/10.1111/jch.14180 Text en © 2021 The Authors. The Journal of Clinical Hypertension published by Wiley Periodicals LLC. https://creativecommons.org/licenses/by-nc/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes. |
spellingShingle | Treatment and Management of Hypertension Tsoi, Kelvin Yiu, Karen Lee, Helen Cheng, Hao‐Min Wang, Tzung‐Dau Tay, Jam‐Chin Teo, Boon Wee Turana, Yuda Soenarta, Arieska Ann Sogunuru, Guru Prasad Siddique, Saulat Chia, Yook‐Chin Shin, Jinho Chen, Chen‐Huan Wang, Ji‐Guang Kario, Kazuomi Applications of artificial intelligence for hypertension management |
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 |
topic | Treatment and Management of Hypertension |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8029548/ https://www.ncbi.nlm.nih.gov/pubmed/33533536 http://dx.doi.org/10.1111/jch.14180 |
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