AIMC Topic: Models, Cardiovascular

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Transfer learning for classification of cardiovascular tissues in histological images.

Computer methods and programs in biomedicine Aug 16, 2018
BACKGROUND AND OBJECTIVE: Automatic classification of healthy tissues and organs based on histology images is an open problem, mainly due to the lack of automated tools. Solutions in this regard have potential in educational medicine and medical prac...
Humans Neural Networks, Computer Machine Learning Algorithms Cardiovascular System Models, Cardiovascular Histological Techniques Models, Anatomic Deep Learning Image Processing, Computer-Assisted Support Vector Machine Reference Values
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Machine learning-aided exploration of relationship between strength and elastic properties in ascending thoracic aneurysm.

International journal for numerical methods in biomedical engineering Apr 10, 2018
Machine learning was applied to classify tension-strain curves harvested from inflation tests on ascending thoracic aneurysm samples. The curves were classified into rupture and nonrupture groups using prerupture response features. Two groups of feat...
Elasticity Humans Aortic Aneurysm, Thoracic Machine Learning Models, Cardiovascular Aorta, Thoracic Stress, Mechanical
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Model-Based Feature Augmentation for Cardiac Ablation Target Learning From Images.

IEEE transactions on bio-medical engineering Mar 22, 2018
GOAL: We present a model-based feature augmentation scheme to improve the performance of a learning algorithm for the detection of cardiac radio-frequency ablation (RFA) targets with respect to learning from images alone.
Heart Image Interpretation, Computer-Assisted Catheter Ablation Models, Cardiovascular Machine Learning Algorithms Sensitivity and Specificity Magnetic Resonance Imaging Humans
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Viscosity Prediction in a Physiologically Controlled Ventricular Assist Device.

IEEE transactions on bio-medical engineering Jan 24, 2018
OBJECTIVE: We present a novel machine learning model to accurately predict the blood-analog viscosity during support of a pathological circulation with a rotary ventricular assist device (VAD). The aim is the continuous monitoring of the hematocrit (...
Models, Cardiovascular Hematocrit Heart-Assist Devices Viscosity Humans Algorithms Databases, Factual Hemodynamics Monitoring, Physiologic Signal Processing, Computer-Assisted Supervised Machine Learning
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Artificial Intelligence Estimation of Carotid-Femoral Pulse Wave Velocity using Carotid Waveform.

Scientific reports Jan 17, 2018
In this article, we offer an artificial intelligence method to estimate the carotid-femoral Pulse Wave Velocity (PWV) non-invasively from one uncalibrated carotid waveform measured by tonometry and few routine clinical variables. Since the signal pro...
Humans Machine Learning Female Longitudinal Studies Pulse Wave Analysis Carotid Arteries Femoral Artery Aged Adult Male Middle Aged Manometry Models, Cardiovascular
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Development of models for predicting Torsade de Pointes cardiac arrhythmias using perceptron neural networks.

BMC bioinformatics Dec 28, 2017
BACKGROUND: Blockage of some ion channels and in particular, the hERG (human Ether-a'-go-go-Related Gene) cardiac potassium channel delays cardiac repolarization and can induce arrhythmia. In some cases it leads to a potentially life-threatening arrh...
Torsades de Pointes Electrocardiography Action Potentials Heart Ventricles Long QT Syndrome Neural Networks, Computer Male Arrhythmias, Cardiac Humans Middle Aged Myocytes, Cardiac ROC Curve Models, Cardiovascular Ether-A-Go-Go Potassium Channels
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Modeling the control of the central nervous system over the cardiovascular system using support vector machines.

Computers in biology and medicine Dec 15, 2017
The control of the central nervous system (CNS) over the cardiovascular system (CS) has been modeled using different techniques, such as fuzzy inductive reasoning, genetic fuzzy systems, neural networks, and nonlinear autoregressive techniques; the r...
Models, Neurological Central Nervous System Cardiovascular System Models, Cardiovascular Humans Support Vector Machine
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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...
Middle Aged Risk Assessment Cohort Studies Logistic Models Electrocardiography Acute Coronary Syndrome Multivariate Analysis Machine Learning Female Aged Male Humans Kaplan-Meier Estimate Neural Networks, Computer Models, Cardiovascular
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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...
Myocardial Contraction Humans Neural Networks, Computer Databases, Factual Female Male Electrocardiography Signal Processing, Computer-Assisted Arrhythmias, Cardiac Heart Rate Models, Cardiovascular
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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...
Survival Analysis Risk Factors Models, Cardiovascular Aged Aged, 80 and over Cause of Death Prognosis Humans Male Registries Middle Aged Female Algorithms Myocardial Infarction Risk Assessment Reproducibility of Results Prospective Studies Support Vector Machine Sweden
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