AIMC Topic: Models, Cardiovascular

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Machine Learning Improves Risk Stratification After Acute Coronary Syndrome.

Scientific reports Oct 4, 2017
The accurate assessment of a patient's risk of adverse events remains a mainstay of clinical care. Commonly used risk metrics have been based on logistic regression models that incorporate aspects of the medical history, presenting signs and symptoms...
Female Aged Male Middle Aged Humans Neural Networks, Computer Machine Learning Acute Coronary Syndrome Multivariate Analysis Kaplan-Meier Estimate Models, Cardiovascular Risk Assessment Cohort Studies Logistic Models Electrocardiography
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A deep convolutional neural network model to classify heartbeats.

Computers in biology and medicine Aug 24, 2017
The electrocardiogram (ECG) is a standard test used to monitor the activity of the heart. Many cardiac abnormalities will be manifested in the ECG including arrhythmia which is a general term that refers to an abnormal heart rhythm. The basis of arrh...
Female Male Electrocardiography Signal Processing, Computer-Assisted Arrhythmias, Cardiac Humans Heart Rate Neural Networks, Computer Models, Cardiovascular Myocardial Contraction Databases, Factual
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Predicting two-year survival versus non-survival after first myocardial infarction using machine learning and Swedish national register data.

BMC medical informatics and decision making Jul 5, 2017
BACKGROUND: Machine learning algorithms hold potential for improved prediction of all-cause mortality in cardiovascular patients, yet have not previously been developed with high-quality population data. This study compared four popular machine learn...
Prognosis Female Reproducibility of Results Sweden Humans Algorithms Prospective Studies Middle Aged Risk Assessment Support Vector Machine Cause of Death Risk Factors Aged Aged, 80 and over Male Models, Cardiovascular Registries Myocardial Infarction Survival Analysis
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Machine Learning Approach for Predicting Wall Shear Distribution for Abdominal Aortic Aneurysm and Carotid Bifurcation Models.

IEEE journal of biomedical and health informatics Dec 14, 2016
Computer simulations based on the finite element method represent powerful tools for modeling blood flow through arteries. However, due to its computational complexity, this approach may be inappropriate when results are needed quickly. In order to r...
Models, Statistical Finite Element Analysis Humans Aortic Aneurysm, Abdominal Models, Cardiovascular Machine Learning Stress, Mechanical Hemodynamics Carotid Arteries
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Noninvasive Personalization of a Cardiac Electrophysiology Model From Body Surface Potential Mapping.

IEEE transactions on bio-medical engineering Nov 16, 2016
GOAL: We use noninvasive data (body surface potential mapping, BSPM) to personalize the main parameters of a cardiac electrophysiological (EP) model for predicting the response to different pacing conditions.
Humans Body Surface Potential Mapping Algorithms Patient-Centered Care Heart Conduction System Models, Cardiovascular Computer Simulation Reproducibility of Results Sensitivity and Specificity Diagnosis, Computer-Assisted
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Predicting the physiological response of Tivela stultorum hearts with digoxin from cardiac parameters using artificial neural networks.

Bio Systems Nov 15, 2016
Multi-layer perceptron artificial neural networks (MLP-ANNs) were used to predict the concentration of digoxin needed to obtain a cardio-activity of specific biophysical parameters in Tivela stultorum hearts. The inputs of the neural networks were th...
Animals Signal Processing, Computer-Assisted Heart Digoxin Bivalvia Cardiotonic Agents Models, Cardiovascular Myocardial Contraction Neural Networks, Computer Algorithms
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Prediction of blood-brain barrier permeability of organic compounds.

Doklady. Biochemistry and biophysics Nov 6, 2016
Using fragmental descriptors and artificial neural networks, a predictive model of the relationship between the structure of organic compounds and their blood-brain barrier permeability was constructed and the structural factors affecting the readine...
Models, Neurological Molecular Structure Models, Chemical Capillary Permeability Blood-Brain Barrier Models, Cardiovascular Neural Networks, Computer Animals Drug Discovery Organic Chemicals
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Developing new VOmax prediction models from maximal, submaximal and questionnaire variables using support vector machines combined with feature selection.

Computers in biology and medicine Oct 20, 2016
Maximal oxygen uptake (VOmax) is an essential part of health and physical fitness, and refers to the highest rate of oxygen consumption an individual can attain during exhaustive exercise. In this study, for the first time in the literature, we combi...
Oxygen Consumption Models, Cardiovascular Support Vector Machine Models, Statistical Regression Analysis Humans Surveys and Questionnaires Computational Biology
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A Discriminatively Trained Fully Connected Conditional Random Field Model for Blood Vessel Segmentation in Fundus Images.

IEEE transactions on bio-medical engineering Feb 26, 2016
GOAL: In this work, we present an extensive description and evaluation of our method for blood vessel segmentation in fundus images based on a discriminatively trained fully connected conditional random field model.
Humans Machine Learning Computer Simulation Pattern Recognition, Automated Retinal Diseases Fluorescein Angiography Ophthalmoscopy Reproducibility of Results Photography Sensitivity and Specificity Models, Cardiovascular Models, Statistical Retinal Artery Image Interpretation, Computer-Assisted
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A combined deep-learning and deformable-model approach to fully automatic segmentation of the left ventricle in cardiac MRI.

Medical image analysis Feb 6, 2016
Segmentation of the left ventricle (LV) from cardiac magnetic resonance imaging (MRI) datasets is an essential step for calculation of clinical indices such as ventricular volume and ejection fraction. In this work, we employ deep learning algorithms...
Reproducibility of Results Sensitivity and Specificity Heart Ventricles Ventricular Dysfunction, Left Image Interpretation, Computer-Assisted Pattern Recognition, Automated Humans Subtraction Technique Image Enhancement Machine Learning Magnetic Resonance Imaging, Cine Algorithms Computer Simulation Models, Cardiovascular
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