Transforming Hypertension Diagnosis and Management in The Era of Artificial Intelligence: A 2023 National Heart, Lung, and Blood Institute (NHLBI) Workshop Report.
Daichi ShimboRashmee U ShahMarwah AbdallaRitu AgarwalFaraz S AhmadGabriel AnayaItzhak Zachi AttiaSheana BullAlexander R ChangYvonne Commodore-MensahKeith C FerdinandKensaku KawamotoRohan KheraJane A LeopoldJames LuoSonya MakhniBobak J MortazaviYoung S OhLucia Clara SavageErica S SpatzGeorge S StergiouMintu P TurakhiaPaul K WheltonClyde W YancyErin IturriagaPublished in: Hypertension (Dallas, Tex. : 1979) (2024)
Hypertension is among the most important risk factors for cardiovascular disease, chronic kidney disease, and dementia. The artificial intelligence (AI) field is advancing quickly, and there has been little discussion on how AI could be leveraged for improving the diagnosis and management of hypertension. AI technologies, including machine learning tools, could alter the way we diagnose and manage hypertension, with potential impacts for improving individual and population health. The development of successful AI tools in public health and health care systems requires diverse types of expertise with collaborative relationships between clinicians, engineers, and data scientists. Unbiased data sources, management, and analyses remain a foundational challenge. From a diagnostic standpoint, machine learning tools may improve the measurement of blood pressure and be useful in the prediction of incident hypertension. To advance the management of hypertension, machine learning tools may be useful to find personalized treatments for patients using analytics to predict response to antihypertension medications and the risk for hypertension-related complications. However, there are real-world implementation challenges to using AI tools in hypertension. Herein, we summarize key findings from a diverse group of stakeholders who participated in a workshop held by the National Heart, Lung, and Blood Institute in March 2023. Workshop participants presented information on communication gaps between clinical medicine, data science, and engineering in health care; novel approaches to estimating BP, hypertension risk, and BP control; and real-world implementation challenges and issues.
Keyphrases
- artificial intelligence
- blood pressure
- machine learning
- big data
- public health
- healthcare
- cardiovascular disease
- deep learning
- chronic kidney disease
- hypertensive patients
- end stage renal disease
- heart rate
- quality improvement
- primary care
- type diabetes
- newly diagnosed
- peritoneal dialysis
- electronic health record
- risk assessment
- drinking water
- ejection fraction
- skeletal muscle
- cognitive impairment
- risk factors
- cardiovascular events
- atrial fibrillation
- insulin resistance
- patient reported
- drug induced