Tsoi, Kelvin and Yiu, Karen and Lee, Helen and Cheng, Hao-Min and Wang, Tzung-Dau and Tay, Jam-Chin and Teo, Boon Wee and Turana, Yuda and Soenarta, Arieska Ann and Sogunuru, Guru Prasad and Siddique, Saulat and Chia, Yook-Chin and Shin, Jinho and Chen, Chen-Huan and Wang, Ji-Guang and Kario, Kazuomi and Network, HOPE Asia (2021) Applications of artificial intelligence for hypertension management. Journal of Clinical Hypertension, 23 (3, SI). pp. 568-574. ISSN 1524-6175, DOI https://doi.org/10.1111/jch.14180.
Full text not available from this repository.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.
| Item Type: | Article |
|---|---|
| Funders: | UNSPECIFIED |
| Uncontrolled Keywords: | Hypertension diagnosis; Artificial intelligence; Aging population; Digital management of chronic diseases |
| Subjects: | Q Science > QM Human anatomy Q Science > QP Physiology R Medicine > RC Internal medicine |
| Divisions: | Faculty of Medicine > Medicine Department |
| Depositing User: | Ms Zaharah Ramly |
| Date Deposited: | 16 Aug 2022 01:33 |
| Last Modified: | 16 Aug 2022 01:33 |
| URI: | http://eprints.um.edu.my/id/eprint/34670 |
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