AIMC Topic: Cardiovascular Diseases
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Artificial intelligence and digital tools for design and execution of cardiovascular clinical trials.
European heart journal
Mar 3, 2025
Recent advances have given rise to a spectrum of digital health technologies that have the potential to revolutionize the design and conduct of cardiovascular clinical trials. Advances in domain tasks such as automated diagnosis and classification, s...
Exploring the full potential of the electronic health record: the application of natural language processing for clinical practice.
European journal of cardiovascular nursing
Mar 3, 2025
The electronic health record (EHR) contains valuable patient data and offers opportunities to administer and analyse patients' individual needs longitudinally. However, most information in the EHR is currently stored in unstructured text notations. N...
Research on Prediction model of Carotid-Femoral Pulse Wave Velocity: Based on Machine Learning Algorithm.
Journal of clinical hypertension (Greenwich, Conn.)
Mar 1, 2025
Carotid-femoral pulse wave velocity (cf-PWV) is an important but difficult to obtain measure of arterial stiffness and an independent predictor of cardiovascular events and all-cause mortality. The objective of this study was to develop a predictive ...
Artificial intelligence-enhanced electrocardiography for the identification of a sex-related cardiovascular risk continuum: a retrospective cohort study.
The Lancet. Digital health
Mar 1, 2025
BACKGROUND: Females are typically underserved in cardiovascular medicine. The use of sex as a dichotomous variable for risk stratification fails to capture the heterogeneity of risk within each sex. We aimed to develop an artificial intelligence-enha...
[The joint analysis of heart health and mental health based on continual learning].
Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi
Feb 25, 2025
Cardiovascular diseases and psychological disorders represent two major threats to human physical and mental health. Research on electrocardiogram (ECG) signals offers valuable opportunities to address these issues. However, existing methods are cons...
[Application Status of Machine Learning in Assisted Diagnosis Techniques of Cardiovascular Diseases].
Zhongguo yi liao qi xie za zhi = Chinese journal of medical instrumentation
Jan 30, 2025
In recent years, cardiovascular disease has become a common disease. With the development of machine learning and big data technologies, the processing ability of electrocardiogram (ECG) signals has been greatly enhanced through new computer technolo...
A novel machine learning-based cancer-specific cardiovascular disease risk score among patients with breast, colorectal, or lung cancer.
JNCI cancer spectrum
Jan 3, 2025
BACKGROUND: Cancer patients have up to a 3-fold higher risk for cardiovascular disease (CVD) than the general population. Traditional CVD risk scores may be less accurate for them. We aimed to develop cancer-specific CVD risk scores and compare them ...
Enhancing ECG disease detection accuracy through deep learning models and P-QRS-T waveform features.
PloS one
Jan 1, 2025
Cardiovascular diseases (CVDs) have surpassed cancer and become the major cause of death worldwide. An electrocardiogram (ECG) is a non-invasive and quicker method for diagnosing abnormal heart conditions. While research has extensively focused on EC...
Sarcopenia prediction model based on machine learning and SHAP values for community-based older adults with cardiovascular disease in China.
Frontiers in public health
Jan 1, 2025
BACKGROUND: Sarcopenia (SP), is recognized as a complication of cardiovascular disease (CVD), but few relevant diagnostic models have been developed. This study aims to establish an interpretable diagnostic model for the occurrence of SP in older adu...
Developing machine learning models for predicting cardiovascular disease survival based on heavy metal serum and urine levels.
Frontiers in public health
Jan 1, 2025
BACKGROUND: Environmental exposure to heavy metals, such as arsenic, cadmium, and lead, is a known risk factor for cardiovascular diseases.
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