AIMC Topic: Hypertension

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The acceptability and effectiveness of artificial intelligence-based chatbot for hypertensive patients in community: protocol for a mixed-methods study.

BMC public health
BACKGROUND: Chatbots can provide immediate assistance tailored to patients' needs, making them suitable for sustained accompanying interventions. Nevertheless, there is currently no evidence regarding their acceptability by hypertensive patients and ...

Assessment of EMR ML Mining Methods for Measuring Association between Metal Mixture and Mortality for Hypertension.

High blood pressure & cardiovascular prevention : the official journal of the Italian Society of Hypertension
INTRODUCTION: There are limited data available regarding the connection between heavy metal exposure and mortality among hypertension patients.

Interactive molecular causal networks of hypertension using a fast machine learning algorithm MRdualPC.

BMC medical research methodology
BACKGROUND: Understanding the complex interactions between genes and their causal effects on diseases is crucial for developing targeted treatments and gaining insight into biological mechanisms. However, the analysis of molecular networks, especiall...

Transforming Hypertension Diagnosis and Management in The Era of Artificial Intelligence: A 2023 National Heart, Lung, and Blood Institute (NHLBI) Workshop Report.

Hypertension (Dallas, Tex. : 1979)
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...

Brief Review and Primer of Key Terminology for Artificial Intelligence and Machine Learning in Hypertension.

Hypertension (Dallas, Tex. : 1979)
Recent breakthroughs in artificial intelligence (AI) have caught the attention of many fields, including health care. The vision for AI is that a computer model can process information and provide output that is indistinguishable from that of a human...

Does clinical practice supported by artificial intelligence improve hypertension care management? A pilot systematic review.

Hypertension research : official journal of the Japanese Society of Hypertension
Although artificial intelligence (AI) is considered to be a promising tool, evidence for the effectiveness of AI-supported clinical practice for lowering blood pressure (BP) in the real world is scarce. We conducted a systematic review to elucidate w...

Plasma infrared fingerprinting with machine learning enables single-measurement multi-phenotype health screening.

Cell reports. Medicine
Infrared spectroscopy is a powerful technique for probing the molecular profiles of complex biofluids, offering a promising avenue for high-throughput in vitro diagnostics. While several studies showcased its potential in detecting health conditions,...

Identification of immune-related genes and small-molecule drugs in hypertension-induced left ventricular hypertrophy based on machine learning algorithms and molecular docking.

Frontiers in immunology
BACKGROUND: Left ventricular hypertrophy (LVH) is a common consequence of hypertension and can lead to heart failure. The immune response plays an important role in hypertensive LVH; however, there is no comprehensive method to investigate the mechan...

Smart solutions in hypertension diagnosis and management: a deep dive into artificial intelligence and modern wearables for blood pressure monitoring.

Blood pressure monitoring
Hypertension, a widespread cardiovascular issue, presents a major global health challenge. Traditional diagnosis and treatment methods involve periodic blood pressure monitoring and prescribing antihypertensive drugs. Smart technology integration in ...

Precise risk-prediction model including arterial stiffness for new-onset atrial fibrillation using machine learning techniques.

Journal of clinical hypertension (Greenwich, Conn.)
Atrial fibrillation (AF) is the most common clinically significant cardiac arrhythmia and is an important risk factor for ischemic cerebrovascular events. This study used machine learning techniques to develop and validate a new risk prediction model...