Application of artificial intelligence in hypertension

Abstract

Hypertension is an important modifiable risk factor for morbidity and mortality associated with cardiovascular disease. The incidence of hypertension is increasing not only in Korea but also in many Western countries due to the aging of the population and the increase in unhealthy lifestyles. However, hypertension control rates remain low due to poor adherence to antihypertensive medications, low awareness of hypertension, and numerous factors that contribute to hypertension, including diet, environment, lifestyle, obesity, and genetics. Because artificial intelligence (AI) involves data-driven algorithms, AI is an asset to understanding chronic diseases that are influenced by multiple factors, such as hypertension. Although several hypertension studies using AI have been published recently, most are exploratory descriptive studies that are often difficult for clinicians to understand and have little clinical relevance. This review aims to provide a clinician-centered perspective on AI by showing recent studies on the relevance of AI for patients with hypertension. The review is organized into sections on blood pressure measurement and hypertension diagnosis, prognosis, and management.

Keywords: Artificial intelligence; Disease management; Hypertension; Machine learning.

Fig. 1

Big data commonly used in each category of hypertension research, and the algorithms that use it to implement individualized approaches for hypertensive patients. BP, blood pressure; CVD, cardiovascular disease; DNN, deep neural network; ECG, electrocardiography; EHRs, electronic health records; LSTM, long short-term memory; PPG, photoplethysmogram; RCT, randomized controlled trial; SVM, support vector machine; XGboost, extreme gradient boost