SH Big Data Decision and Analytics Research Centre, Shatin, Hong Kong.
JC School of Public Health and Primary Care, The Chinese University of Hong Kong, Shatin, Hong Kong.
J Clin Hypertens (Greenwich). 2021 Mar;23(3):568-574. doi: 10.1111/jch.14180. Epub 2021 Feb 3.
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.
随着人口老龄化，高血压的患病率不断上升，导致全球每年有数百万人过早死亡。血压升高的意识低下和高血压诊断不理想是有效管理高血压的主要障碍。然而，人工智能（AI）的出现为高血压管理提供了新的策略，例如远程医疗和大数据衍生预测的支持。有相当多的证据表明 AI 在高血压管理中的应用是可行的。可以预见的趋势是将血压测量与各种可穿戴传感器和智能手机相结合，以便进行连续和方便的监测。同时，建议进一步研究验证新的预测和预后工具。这些革命性的发展为慢性病的数字化管理未来模式迈出了一步。
