AIMC Topic: Cardiovascular Diseases
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Hybrid time series and machine learning models for forecasting cardiovascular mortality in India: an age specific analysis.
BMC public health
Jun 10, 2025
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
Jun 8, 2025
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
Jun 5, 2025
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
Jun 1, 2025
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
Jun 1, 2025
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)
Jun 1, 2025
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
Jun 1, 2025
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
Jun 1, 2025
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...
Fusion of multi-scale feature extraction and adaptive multi-channel graph neural network for 12-lead ECG classification.
Computer methods and programs in biomedicine
Jun 1, 2025
BACKGROUND AND OBJECTIVE: The 12-lead electrocardiography (ECG) is a widely used diagnostic method in clinical practice for cardiovascular diseases. The potential correlation between interlead signals is an important reference for clinical diagnosis ...
OptiStack classifier: optimized stacking framework with ensemble feature engineering for enhanced cardiovascular risk prediction.
Inflammation research : official journal of the European Histamine Research Society ... [et al.]
May 31, 2025
BACKGROUND: Cardiovascular diseases (CVD) are a leading cause of morbidity and mortality globally, highlighting the urgent need for accurate risk prediction to improve early intervention and management. Traditional models have difficulty capturing th...
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