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Machine learning predicts long-term mortality after acute myocardial infarction using systolic time intervals and routinely collected clinical data
Using medical and demographic records as well as two recently introduced biomarkers, brachial pre-ejection period (bPEP) and brachial ejection time (bET), collected from 139 patients with acute myocardial infarction, we investigated the performance of advanced ensemble tree-based ML algorithms (random forest, AdaBoost, and XGBoost) to predict all-cause mortality within 14 years.
Localizing the Origin of Idiopathic Ventricular Arrhythmia from ECG Using an Attention-Based Recurrent Convolutional Neural Network
The proposed method shows promise toward automatic and noninvasive evaluation of IVA patients before cardiac catheter ablation.
Oscillometric Blood Pressure Measurement Using a Hybrid Deep Morpho-Temporal Representation Learning Framework
Oscillometric monitors are the most common automated blood pressure (BP) measurement devices used in non-specialist settings.
CT-LungNet: A Deep Learning Framework for Precise Lung Tissue Segmentation in 3D Thoracic CT Scans
Segmentation of lung tissue in computed tomography (CT) images is a precursor to most pulmonary image analysis applications.
