Artificial Intelligence in Hypertension

Abstract

Hypertension remains the largest modifiable cause of mortality worldwide despite the availability of effective medications and sustained research efforts over the past 100 years. Hypertension requires transformative solutions that can help reduce the global burden of the disease. Artificial intelligence and machine learning, which have made a substantial impact on our everyday lives over the last decade may be the route to this transformation. However, artificial intelligence in health care is still in its nascent stages and realizing its potential requires numerous challenges to be overcome. In this review, we provide a clinician-centric perspective on artificial intelligence and machine learning as applied to medicine and hypertension. We focus on the main roadblocks impeding implementation of this technology in clinical care and describe efforts driving potential solutions. At the juncture, there is a critical requirement for clinical and scientific expertise to work in tandem with algorithmic innovation followed by rigorous validation and scrutiny to realize the promise of artificial intelligence-enabled health care for hypertension and other chronic diseases.

Therapeutic strategies targeting inflammation and immunity in atherosclerosis: how to proceed?

“…Further improvements in the imaging of atherosclerosis will facilitate the development of valid surrogate end points of cardiovascular outcomes. Although cardiovascular surrogate end points are at present not sufficiently specific and, therefore, have not reached the benchmark of a clinical trial, developments in the field of machine learning could be used to combine multiple surrogate end points for a more accurate prediction of clinical outcomes 145 , 146 .…”

Microcirculation and Macrocirculation in Hypertension: A Dangerous Cross-Link?

“…Machine learning approaches provide efficient strategies to build phenotypic scores, whereas clustering techniques applied to well-phenotyped populations may identify different patterns of micro-macrovascular damage. 76 This could be performed not only in cross-sectional studies but also in trajectory studies with long follow-up and repeated measurements. Not only are these multiparameter, deep vascular phenotyping approaches significant for understanding pathophysiology of organ damage in hypertension, but they may also provide indications about which tests exploring micro-macrocirculation are most relevant and should be used in randomized clinical trials and then included in routine workup of the hypertensive patient.…”

Precision Medicine for Hypertension Patients with Type 2 Diabetes via Reinforcement Learning

“…Recent papers and treatment guidelines on precision medicine for hypertension have highlighted difficulties in the disease's architecture, management issues, and the need for transformation [15][16][17][18][19]. Over the last half-century, the treatment technique has remained virtually unchanged, and personalization of treatment has not gone beyond taking African ancestry and serum renin levels into account [20].…”

“…Furthermore, substantial genetic, molecular, and physiological research discoveries are not being integrated into screening, diagnostic, and management regimens. More than half of patients require numerous clinic visits at varied intervals to try dose titration, switching, or adding medicines until a satisfactory outcome is obtained, intolerable side effects develop, or no further progress appears likely [20]. Despite the high prevalence of hypertension, good health management must be devolved to the patients or machine learning-based intelligent systems [21].…”