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Myocardial Infarction

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Evaluation of Machine Learning Algorithms for Predicting Readmission After Acute Myocardial Infarction Using Routinely Collected Clinical Data.

The Canadian journal of cardiology
BACKGROUND: The ability to predict readmission accurately after hospitalization for acute myocardial infarction (AMI) is limited in current statistical models. Machine-learning (ML) methods have shown improved predictive ability in various clinical c...

ML-ResNet: A novel network to detect and locate myocardial infarction using 12 leads ECG.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Myocardial infarction (MI) is one of the most threatening cardiovascular diseases for human beings, which can be diagnosed by electrocardiogram (ECG). Automated detection methods based on ECG focus on extracting handcrafted ...

Segmentation and quantification of infarction without contrast agents via spatiotemporal generative adversarial learning.

Medical image analysis
Accurate and simultaneous segmentation and full quantification (all indices are required in a clinical assessment) of the myocardial infarction (MI) area are crucial for early diagnosis and surgical planning. Current clinical methods remain subject t...

A deep survival analysis method based on ranking.

Artificial intelligence in medicine
Survival analyses of populations and the establishment of prognoses for individual patients are important activities in the practice of medicine. Standard survival models, such as the Cox proportional hazards model, require extensive feature engineer...

A Deep Learning Approach for Assessment of Regional Wall Motion Abnormality From Echocardiographic Images.

JACC. Cardiovascular imaging
OBJECTIVES: This study investigated whether a deep convolutional neural network (DCNN) could provide improved detection of regional wall motion abnormalities (RWMAs) and differentiate among groups of coronary infarction territories from conventional ...

Deep Learning for Diagnosis of Chronic Myocardial Infarction on Nonenhanced Cardiac Cine MRI.

Radiology
Background Renal impairment is common in patients with coronary artery disease and, if severe, late gadolinium enhancement (LGE) imaging for myocardial infarction (MI) evaluation cannot be performed. Purpose To develop a fully automatic framework for...

Fuzzy DEA-based classifier and its applications in healthcare management.

Health care management science
Nonlinear fuzzy classification models have better classification performance than linear fuzzy classifiers. In many nonlinear fuzzy classification problems, piecewise-linear fuzzy discriminant functions can approximate nonlinear fuzzy discriminant fu...

An attention based deep learning model of clinical events in the intensive care unit.

PloS one
This study trained long short-term memory (LSTM) recurrent neural networks (RNNs) incorporating an attention mechanism to predict daily sepsis, myocardial infarction (MI), and vancomycin antibiotic administration over two week patient ICU courses in ...

Water-fat separation and parameter mapping in cardiac MRI via deep learning with a convolutional neural network.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: Water-fat separation is a postprocessing technique most commonly applied to multiple-gradient-echo magnetic resonance (MR) images to identify fat, provide images with fat suppression, and to measure fat tissue concentration. Recently, Num...

Detecting and interpreting myocardial infarction using fully convolutional neural networks.

Physiological measurement
OBJECTIVE: We aim to provide an algorithm for the detection of myocardial infarction that operates directly on ECG data without any preprocessing and to investigate its decision criteria.