AIMC Topic: Cardiovascular Diseases

Clear Filters Showing 561 to 570 of 736 articles

Integration of metabolomics and machine learning for precise management and prevention of cardiometabolic risk in Asians.

Clinical nutrition (Edinburgh, Scotland)
Rapid changes in dietary patterns have led to a rise in cardiometabolic diseases (CMDs) worldwide, highlighting the urgent need for effective dietary strategies to address the health issues. Compared to Caucasians, Asians are more susceptible to CMDs...

Development of a machine learning model for predicting cardiovascular risk factors and major adverse cardiovascular events in young adults with glucose metabolism disorders.

Medicina clinica
BACKGROUND: Glucose metabolism disorders (GMDs) are a serious global public health issue, characterized by a high incidence rate and youthfulness. GMDs contribute to the occurrence of major adverse cardiovascular events (MACEs), meanwhile with the re...

Hybrid time series and machine learning models for forecasting cardiovascular mortality in India: an age specific analysis.

BMC public health
Cardiovascular disease (CVD) is a primary cause of death in India, accounting for a significant portion of the global CVD burden. This study looks at statistics on heart disease mortality from the Institute for Health Metrics and Evaluation (IHME) fr...

Artificial Intelligence in the Prevention and Detection of Cardiovascular Disease.

Cardiology in review
For more than 60 years, artificial intelligence (AI) has served as a mainstay in augmenting and assisting the lives of individuals across a wide array of interests and professional fields. Functioning to create deep computer simulations, analyze data...

A flexible machine learning Mendelian randomization estimator applied to predict the safety and efficacy of sclerostin inhibition.

American journal of human genetics
Mendelian randomization (MR) enables the estimation of causal effects while controlling for unmeasured confounding factors. However, traditional MR's reliance on strong parametric assumptions can introduce bias if these are violated. We describe a ma...

Brain Aging in Patients With Cardiovascular Disease From the UK Biobank.

Human brain mapping
The brain undergoes complex but normal structural changes during the aging process in healthy adults, whereas deviations from the normal aging patterns of the brain can be indicative of various conditions as well as an increased risk for the developm...

Utilizing machine learning algorithms for cardiovascular disease prediction: "Detailed analysis based on medical parameters".

Medical engineering & physics
Among the most prevalent and dangerous ailments impacting human health are cardiovascular diseases (CVDs). Early diagnosis may help avoid or lessen CVDs, thereby lowering death rates. Several clinical methods have already been deployed for diagnosing...

[Applications of artificial intelligence in cardiovascular imaging: advantages, limitations, and future challenges].

Giornale italiano di cardiologia (2006)
Artificial intelligence (AI) is rapidly transforming cardiovascular imaging, offering innovative solutions to enhance diagnostic precision, prognostic accuracy, and therapeutic decision-making. This review explores the role of AI in cardiovascular im...

CRT 2025 late-breaking trials: Key takeaways.

Cardiovascular revascularization medicine : including molecular interventions
The Cardiovascular Research Technologies (CRT) 2025 conference, a prominent gathering in the field of cardiology, convened more than three thousand attendees from around the world. CRT provides a forum for exemplary education for interventional cardi...

AI driven cardiovascular risk prediction using NLP and Large Language Models for personalized medicine in athletes.

SLAS technology
The performance and long-term health of athletes are significantly influenced by their cardiovascular resilience and associated risk factors. This study explores the innovative applications of Natural Language Processing (NLP) and Large Language Mode...