AI Medical Compendium Topic

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Cardiovascular Diseases

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AI-integrated ocular imaging for predicting cardiovascular disease: advancements and future outlook.

Eye (London, England)
Cardiovascular disease (CVD) remains the leading cause of death worldwide. Assessing of CVD risk plays an essential role in identifying individuals at higher risk and enables the implementation of targeted intervention strategies, leading to improved...

An Efficient and Private ECG Classification System Using Split and Semi-Supervised Learning.

IEEE journal of biomedical and health informatics
Electrocardiography (ECG) is a standard diagnostic tool for evaluating the overall heart's electrical activity and is vital for detecting many cardiovascular diseases. Classifying ECG recordings using deep neural networks has been investigated in lit...

Identifying Cardiovascular Disease Risk Factors in Adults with Explainable Artificial Intelligence.

Anatolian journal of cardiology
BACKGROUND: The aim of this study was to evaluate the relationship between risk factors causing cardiovascular diseases and their importance with explainable machine learning models.

Fuzzy entropy DEMATEL inference system for accurate and efficient cardiovascular disease diagnosis.

Computer methods in biomechanics and biomedical engineering
The global population is at risk from both communicable and non-communicable deadly diseases, including cardiovascular disease. Early detection and prevention of cardiovascular disease require an accurate self-detection model. Therefore, this study i...

Mud Ring Optimization Algorithm with Deep Learning Model for Disease Diagnosis on ECG Monitoring System.

Sensors (Basel, Switzerland)
Due to the tremendous growth of the Internet of Things (IoT), sensing technologies, and wearables, the quality of medical services has been enhanced, and it has shifted from standard medical-based health services to real time. Commonly, the sensors c...

Comparing the performance of machine learning and conventional models for predicting atherosclerotic cardiovascular disease in a general Chinese population.

BMC medical informatics and decision making
BACKGROUND: Accurately predicting the risk of atherosclerotic cardiovascular disease (ASCVD) is crucial for implementing individualized prevention strategies and improving patient outcomes. Our objective is to develop machine learning (ML)-based mode...

Stratification of diabetes in the context of comorbidities, using representation learning and topological data analysis.

Scientific reports
Diabetes is a heterogenous, multimorbid disorder with a large variation in manifestations, trajectories, and outcomes. The aim of this study is to validate a novel machine learning method for the phenotyping of diabetes in the context of comorbiditie...

Deep learning prediction models based on EHR trajectories: A systematic review.

Journal of biomedical informatics
BACKGROUND: Electronic health records (EHRs) are generated at an ever-increasing rate. EHR trajectories, the temporal aspect of health records, facilitate predicting patients' future health-related risks. It enables healthcare systems to increase the...

A New Era in Cardiometabolic Management: Unlocking the Potential of Artificial Intelligence for Improved Patient Outcomes.

Endocrine practice : official journal of the American College of Endocrinology and the American Association of Clinical Endocrinologists

Artificial intelligence in vascular surgical decision making.

Seminars in vascular surgery
Despite advances in prevention, detection, and treatment, cardiovascular disease is a leading cause of mortality and represents a major health problem worldwide. Artificial intelligence and machine learning have brought new insights to the management...