AIMC Topic: Heart Diseases
Clear Filters Showing 131 to 140 of 198 articles
Weakly supervised classification of aortic valve malformations using unlabeled cardiac MRI sequences.
Nature communications
Jul 15, 2019
Biomedical repositories such as the UK Biobank provide increasing access to prospectively collected cardiac imaging, however these data are unlabeled, which creates barriers to their use in supervised machine learning. We develop a weakly supervised ...
A Machine Learning Approach to Classifying Self-Reported Health Status in a Cohort of Patients With Heart Disease Using Activity Tracker Data.
IEEE journal of biomedical and health informatics
Jun 11, 2019
Constructing statistical models using personal sensor data could allow for tracking health status over time, thereby enabling the possibility of early intervention. The goal of this study was to use machine learning algorithms to classify patient-rep...
Explainable cardiac pathology classification on cine MRI with motion characterization by semi-supervised learning of apparent flow.
Medical image analysis
Jun 6, 2019
We propose a method to classify cardiac pathology based on a novel approach to extract image derived features to characterize the shape and motion of the heart. An original semi-supervised learning procedure, which makes efficient use of a large amou...
A novel IRBF-RVM model for diagnosis of atrial fibrillation.
Computer methods and programs in biomedicine
May 29, 2019
BACKGROUND AND OBJECTIVE: Atrial fibrillation (AF) is one of the common cardiovascular diseases, and electrocardiography (ECG) is a key indicator for the detection and diagnosis of AF and other heart diseases. In this study, an improved machine learn...
Supervised methods to extract clinical events from cardiology reports in Italian.
Journal of biomedical informatics
May 28, 2019
Clinical narratives are a valuable source of information for both patient care and biomedical research. Given the unstructured nature of medical reports, specific automatic techniques are required to extract relevant entities from such texts. In the ...
Electrocardiogram generation with a bidirectional LSTM-CNN generative adversarial network.
Scientific reports
May 1, 2019
Heart disease is a malignant threat to human health. Electrocardiogram (ECG) tests are used to help diagnose heart disease by recording the heart's activity. However, automated medical-aided diagnosis with computers usually requires a large volume of...
Bayesian Networks for Risk Prediction Using Real-World Data: A Tool for Precision Medicine.
Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research
Mar 15, 2019
OBJECTIVE: The fields of medicine and public health are undergoing a data revolution. An increasing availability of data has brought about a growing interest in machine-learning algorithms. Our objective is to present the reader with an introduction ...
SALMANTICOR study. Rationale and design of a population-based study to identify structural heart disease abnormalities: a spatial and machine learning analysis.
BMJ open
Feb 13, 2019
INTRODUCTION: This study aims to obtain data on the prevalence and incidence of structural heart disease in a population setting and, to analyse and present those data on the application of spatial and machine learning methods that, although known to...
Serial electrocardiography to detect newly emerging or aggravating cardiac pathology: a deep-learning approach.
Biomedical engineering online
Feb 12, 2019
BACKGROUND: Serial electrocardiography aims to contribute to electrocardiogram (ECG) diagnosis by comparing the ECG under consideration with a previously made ECG in the same individual. Here, we present a novel algorithm to construct dedicated deep-...
Predictors of in-hospital length of stay among cardiac patients: A machine learning approach.
International journal of cardiology
Jan 19, 2019
OBJECTIVE: The In-hospital length of stay (LOS) is expected to increase as cardiovascular diseases complexity increases and the population ages. This will affect healthcare systems especially with the current situation of decreased bed capacity and i...
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