AIMC Topic: Myocardial Infarction

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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.

Scalable Electronic Phenotyping For Studying Patient Comorbidities.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Over 75 million Americans have multiple concurrent chronic conditions and medical decision making for these patients is mostly based on retrospective cohort studies. Current methods to generate cohorts of patients with comorbidities are neither scala...

Complex robotic compared to manual coronary interventions: 6- and 12-month outcomes.

Catheterization and cardiovascular interventions : official journal of the Society for Cardiac Angiography & Interventions
OBJECTIVES: To assess the long-term safety and efficacy of robotic percutaneous coronary revascularization for use in complex coronary lesions.

Multi-sequence myocardium segmentation with cross-constrained shape and neural network-based initialization.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
For myocardial infarction (MI) patients, delayed enhancement (DE) and T2-weighted cardiovascular magnetic resonance imaging (CMR) can play significant roles in diagnosis, prognosis and therapeutic strategy evaluation. However, the non-rigid registrat...

Deep Deterministic Learning for Pattern Recognition of Different Cardiac Diseases through the Internet of Medical Things.

Journal of medical systems
Electrocardiography (ECG) sensors play a vital role in the Internet of Medical Things, and these sensors help in monitoring the electrical activity of the heart. ECG signal analysis can improve human life in many ways, from diagnosing diseases among ...

Predicting Adherence to Internet-Delivered Psychotherapy for Symptoms of Depression and Anxiety After Myocardial Infarction: Machine Learning Insights From the U-CARE Heart Randomized Controlled Trial.

Journal of medical Internet research
BACKGROUND: Low adherence to recommended treatments is a multifactorial problem for patients in rehabilitation after myocardial infarction (MI). In a nationwide trial of internet-delivered cognitive behavior therapy (iCBT) for the high-risk subgroup ...

Multi-dimensional proprio-proximus machine learning for assessment of myocardial infarction.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
This work presents a novel analysis methodology that utilises high-resolution, multi-dimensional information to better classify regions of the left ventricle after myocardial infarction. Specifically, the focus is to determine degree of infarction in...

Combining Desorption Electrospray Ionization Mass Spectrometry Imaging and Machine Learning for Molecular Recognition of Myocardial Infarction.

Analytical chemistry
Lipid profile changes in heart muscle have been previously linked to cardiac ischemia and myocardial infarction, but the spatial distribution of lipids and metabolites in ischemic heart remains to be fully investigated. We performed desorption electr...