AI Medical Compendium Topic:
Cardiovascular Diseases

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Cardiovascular disease risk prediction using automated machine learning: A prospective study of 423,604 UK Biobank participants.

PloS one
BACKGROUND: Identifying people at risk of cardiovascular diseases (CVD) is a cornerstone of preventative cardiology. Risk prediction models currently recommended by clinical guidelines are typically based on a limited number of predictors with sub-op...

A machine learning-based approach for predicting the outbreak of cardiovascular diseases in patients on dialysis.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Patients with End- Stage Kidney Disease (ESKD) have a unique cardiovascular risk. This study aims at predicting, with a certain precision, death and cardiovascular diseases in dialysis patients.

Putting machine learning into motion: applications in cardiovascular imaging.

Clinical radiology
Heart and circulatory diseases cause a quarter of all deaths in the UK and cardiac imaging offers an effective tool for early diagnosis and risk-stratification to improve premature death and disability. This domain of radiology is unique in that asse...

Importance of coding co-morbidities for APR-DRG assignment: Focus on cardiovascular and respiratory diseases.

Health information management : journal of the Health Information Management Association of Australia
BACKGROUND: The All Patient-Refined Diagnosis-Related Groups (APR-DRGs) system has adjusted the basic DRG structure by incorporating four severity of illness (SOI) levels, which are used for determining hospital payment. A comprehensive report of all...

ECG Multilead Interval Estimation Using Support Vector Machines.

Journal of healthcare engineering
This work reports a multilead interval measurement algorithm for a high-resolution digital electrocardiograph. The software enables off-line ECG processing including detection as well as an accurate multilead interval detection algorithm using sup...

Application of Machine Learning to Identify Clustering of Cardiometabolic Risk Factors in U.S. Adults.

Diabetes technology & therapeutics
The aim of this study is to compare some machine learning methods with traditional statistical parametric analyses using logistic regression to investigate the relationship of risk factors for diabetes and cardiovascular (cardiometabolic risk) for U...

Patient-Level Prediction of Cardio-Cerebrovascular Events in Hypertension Using Nationwide Claims Data.

Journal of medical Internet research
BACKGROUND: Prevention and management of chronic diseases are the main goals of national health maintenance programs. Previously widely used screening tools, such as Health Risk Appraisal, are restricted in their achievement this goal due to their li...

Learning from Longitudinal Data in Electronic Health Record and Genetic Data to Improve Cardiovascular Event Prediction.

Scientific reports
Current approaches to predicting a cardiovascular disease (CVD) event rely on conventional risk factors and cross-sectional data. In this study, we applied machine learning and deep learning models to 10-year CVD event prediction by using longitudina...

Automated analysis of cardiovascular magnetic resonance myocardial native T mapping images using fully convolutional neural networks.

Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance
BACKGROUND: Cardiovascular magnetic resonance (CMR) myocardial native T mapping allows assessment of interstitial diffuse fibrosis. In this technique, the global and regional T are measured manually by drawing region of interest in motion-corrected T...