AIMC Topic: Cardiovascular Diseases
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Predicting cardiovascular outcomes in Chinese patients with type 2 diabetes by combining risk factor trajectories and machine learning algorithm: a cohort study.
Cardiovascular diabetology
Feb 7, 2025
BACKGROUND: Cardiovascular complications are major concerns for Chinese patients with type 2 diabetes. Accurately predicting these risks remains challenging due to limitations in traditional risk models. We aimed to develop a dynamic prediction model...
Characterisation of cardiovascular disease (CVD) incidence and machine learning risk prediction in middle-aged and elderly populations: data from the China health and retirement longitudinal study (CHARLS).
BMC public health
Feb 7, 2025
BACKGROUND: Due to the ageing population and evolving lifestyles occurring in China, middle-aged and elderly populations have become high-risk groups for cardiovascular disease (CVD). The aim of this study was to analyse the incidence characteristics...
Unlocking the link: predicting cardiovascular disease risk with a focus on airflow obstruction using machine learning.
BMC medical informatics and decision making
Feb 3, 2025
BACKGROUND: Respiratory diseases and Cardiovascular Diseases (CVD) often coexist, with airflow obstruction (AO) severity closely linked to CVD incidence and mortality. As both conditions rise, early identification and intervention in risk populations...
Adaptive wavelet base selection for deep learning-based ECG diagnosis: A reinforcement learning approach.
PloS one
Feb 3, 2025
Electrocardiogram (ECG) signals are crucial in diagnosing cardiovascular diseases (CVDs). While wavelet-based feature extraction has demonstrated effectiveness in deep learning (DL)-based ECG diagnosis, selecting the optimal wavelet base poses a sign...
Development and validation of an interpretable machine learning model to predict major adverse cardiovascular events after noncardiac surgery in geriatric patients: a prospective study.
International journal of surgery (London, England)
Feb 1, 2025
BACKGROUND: Major adverse cardiovascular events (MACEs) within 30 days following noncardiac surgery are prognostically relevant. Accurate prediction of risk and modifiable risk factors for postoperative MACEs is critical for surgical planning and pat...
Semi-supervised Strong-Teacher Consistency Learning for few-shot cardiac MRI image segmentation.
Computer methods and programs in biomedicine
Jan 27, 2025
BACKGROUND AND OBJECTIVE: Cardiovascular disease is a leading cause of mortality worldwide. Automated analysis of heart structures in MRI is crucial for effective diagnostics. While supervised learning has advanced the field of medical image segmenta...
Enhancing cardiovascular disease classification in ECG spectrograms by using multi-branch CNN.
Computers in biology and medicine
Jan 25, 2025
Cardiovascular disease (CVD) is caused by the abnormal functioning of the heart which results in a high mortality rate across the globe. The accurate and early prediction of various CVDs from the electrocardiogram (ECG) is vital for the prevention of...
The association of lifestyle with cardiovascular and all-cause mortality based on machine learning: a prospective study from the NHANES.
BMC public health
Jan 24, 2025
BACKGROUND: Lifestyle and cardiovascular mortality and all-cause mortality have been exhaustively explored by traditional methods, but the advantages of machine learning (ML) over traditional methods may lead to different or more precise conclusions....
Random Survival Forest Machine Learning for the Prediction of Cardiovascular Events Among Patients With a Measured Lipoprotein(a) Level: A Model Development Study.
Circulation. Genomic and precision medicine
Jan 23, 2025
BACKGROUND: Established risk models may not be applicable to patients at higher cardiovascular risk with a measured Lp(a) (lipoprotein[a]) level, a causal risk factor for atherosclerotic cardiovascular disease.
Multiple token rearrangement Transformer network with explicit superpixel constraint for segmentation of echocardiography.
Medical image analysis
Jan 22, 2025
Diagnostic cardiologists have considerable clinical demand for precise segmentation of echocardiography to diagnose cardiovascular disease. The paradox is that manual segmentation of echocardiography is a time-consuming and operator-dependent task. C...
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