AIMC Topic: Cardiovascular Diseases

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Machine learning and blood pressure.

Journal of clinical hypertension (Greenwich, Conn.) Sep 19, 2019
Machine learning (ML) is a type of artificial intelligence (AI) based on pattern recognition. There are different forms of supervised and unsupervised learning algorithms that are being used to identify and predict blood pressure (BP) and other measu...
Artificial Intelligence Machine Learning Prognosis Cardiovascular Diseases Humans Risk Assessment Hypertension Disease Management
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Development and verification of prediction models for preventing cardiovascular diseases.

PloS one Sep 19, 2019
OBJECTIVES: Cardiovascular disease (CVD) is one of the major causes of death worldwide. For improved accuracy of CVD prediction, risk classification was performed using national time-series health examination data. The data offers an opportunity to a...
Cardiovascular Diseases Female Risk Factors Middle Aged Humans Adult Risk Assessment Algorithms Male Blood Pressure Deep Learning Prognosis Survival Analysis Proportional Hazards Models Models, Cardiovascular
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Comparison of risk models for mortality and cardiovascular events between machine learning and conventional logistic regression analysis.

PloS one Sep 9, 2019
AIMS: Non-linear models by machine learning may identify different risk factors with different weighting in comparison to conventional linear models.
Logistic Models Humans Cardiovascular Diseases Models, Statistical Machine Learning Female Middle Aged Male Risk Assessment Cohort Studies
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Automated and Interpretable Patient ECG Profiles for Disease Detection, Tracking, and Discovery.

Circulation. Cardiovascular quality and outcomes Sep 5, 2019
BACKGROUND: The ECG remains the most widely used diagnostic test for characterization of cardiac structure and electrical activity. We hypothesized that parallel advances in computing power, machine learning algorithms, and availability of large-scal...
Workflow Heart Rate Humans Reproducibility of Results Markov Chains Electrocardiography Cardiovascular Diseases Pattern Recognition, Automated Neural Networks, Computer Action Potentials Diagnosis, Computer-Assisted Predictive Value of Tests Machine Learning Time Factors Signal Processing, Computer-Assisted Databases, Factual Prognosis
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Artificial Intelligence: Practical Primer for Clinical Research in Cardiovascular Disease.

Journal of the American Heart Association Aug 27, 2019
Cardiovascular Diseases Diagnosis, Computer-Assisted Telemedicine Diffusion of Innovation Humans Artificial Intelligence Precision Medicine Biomedical Research Therapy, Computer-Assisted
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Scoring colorectal cancer risk with an artificial neural network based on self-reportable personal health data.

PloS one Aug 22, 2019
Colorectal cancer (CRC) is third in prevalence and mortality among all cancers in the US. Currently, the United States Preventative Services Task Force (USPSTF) recommends anyone ages 50-75 and/or with a family history to be screened for CRC. To impr...
Health Status Early Detection of Cancer Models, Statistical Self Report Practice Guidelines as Topic Medical History Taking Middle Aged Health Surveys United States Diabetes Mellitus Colorectal Neoplasms Male Cardiovascular Diseases Prognosis Humans Female Risk Factors Neural Networks, Computer Aged
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Machine learning to predict cardiovascular risk.

International journal of clinical practice Aug 4, 2019
AIMS: To analyse the predictive capacity of 15 machine learning methods for estimating cardiovascular risk in a cohort and to compare them with other risk scales.
Female Risk Factors Male Cardiovascular Diseases Humans Machine Learning Algorithms Area Under Curve Bayes Theorem Spain Middle Aged Cohort Studies Follow-Up Studies Predictive Value of Tests
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A Novel Approach for Multi-Lead ECG Classification Using DL-CCANet and TL-CCANet.

Sensors (Basel, Switzerland) Jul 21, 2019
Cardiovascular disease (CVD) has become one of the most serious diseases that threaten human health. Over the past decades, over 150 million humans have died of CVDs. Hence, timely prediction of CVDs is especially important. Currently, deep learning ...
Cardiovascular Diseases Deep Learning Electrocardiography Support Vector Machine Signal Processing, Computer-Assisted Humans Arrhythmias, Cardiac Algorithms Heart Rate Databases, Factual
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Disentangled representation learning in cardiac image analysis.

Medical image analysis Jul 18, 2019
Typically, a medical image offers spatial information on the anatomy (and pathology) modulated by imaging specific characteristics. Many imaging modalities including Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) can be interpreted in ...
Humans Tomography, X-Ray Computed Datasets as Topic Cardiovascular Diseases Magnetic Resonance Imaging Supervised Machine Learning Image Interpretation, Computer-Assisted
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Deep Learning to Assess Long-term Mortality From Chest Radiographs.

JAMA network open Jul 3, 2019
IMPORTANCE: Chest radiography is the most common diagnostic imaging test in medicine and may also provide information about longevity and prognosis.
Middle Aged Risk Assessment Deep Learning Mass Screening Lung Diseases Clinical Trials as Topic Radiography, Thoracic Thorax Risk Factors Aged Neural Networks, Computer Male Prognosis Cardiovascular Diseases Female Humans
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