AIMC Topic: Models, Cardiovascular
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Improving the clinical understanding of hypertrophic cardiomyopathy by combining patient data, machine learning and computer simulations: A case study.
Morphologie : bulletin de l'Association des anatomistes
Sep 27, 2019
Most patients with hypertrophic cardiomyopathy (HCM), the most common genetic cardiac disease, remain asymptomatic, but others may suffer from sudden cardiac death. A better identification of those patients at risk, together with a better understandi...
Automated diagnosis of heart valve degradation using novelty detection algorithms and machine learning.
PloS one
Sep 26, 2019
The blood flow through the major vessels holds great diagnostic potential for the identification of cardiovascular complications and is therefore routinely assessed with current diagnostic modalities. Heart valves are subject to high hydrodynamic loa...
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...
Accelerating cardiovascular model building with convolutional neural networks.
Medical & biological engineering & computing
Aug 24, 2019
The objective of this work is to reduce the user effort required for 2D segmentation when building patient-specific cardiovascular models using the SimVascular cardiovascular modeling software package. The proposed method uses a fully convolutional n...
Automated detection of diabetic subject using pre-trained 2D-CNN models with frequency spectrum images extracted from heart rate signals.
Computers in biology and medicine
Aug 9, 2019
In this study, a deep-transfer learning approach is proposed for the automated diagnosis of diabetes mellitus (DM), using heart rate (HR) signals obtained from electrocardiogram (ECG) data. Recent progress in deep learning has contributed significant...
A new machine learning technique for an accurate diagnosis of coronary artery disease.
Computer methods and programs in biomedicine
Jul 24, 2019
BACKGROUND AND OBJECTIVE: Coronary artery disease (CAD) is one of the commonest diseases around the world. An early and accurate diagnosis of CAD allows a timely administration of appropriate treatment and helps to reduce the mortality. Herein, we de...
Using machine learning to characterize heart failure across the scales.
Biomechanics and modeling in mechanobiology
Jun 25, 2019
Heart failure is a progressive chronic condition in which the heart undergoes detrimental changes in structure and function across multiple scales in time and space. Multiscale models of cardiac growth can provide a patient-specific window into the p...
Smeared multiscale finite element model for electrophysiology and ionic transport in biological tissue.
Computers in biology and medicine
Mar 26, 2019
Basic functions of living organisms are governed by the nervous system through bidirectional signals transmitted from the brain to neural networks. These signals are similar to electrical waves. In electrophysiology the goal is to study the electrica...
Machine learning methodologies versus cardiovascular risk scores, in predicting disease risk.
BMC medical research methodology
Dec 29, 2018
BACKGROUND: The use of Cardiovascular Disease (CVD) risk estimation scores in primary prevention has long been established. However, their performance still remains a matter of concern. The aim of this study was to explore the potential of using ML m...
A minimal set of physiomarkers in continuous high frequency data streams predict adult sepsis onset earlier.
International journal of medical informatics
Dec 10, 2018
PURPOSE: Sepsis is a life-threatening condition with high mortality rates and expensive treatment costs. To improve short- and long-term outcomes, it is critical to detect at-risk sepsis patients at an early stage.
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