The Lancet

Forecasting left ventricular systolic dysfunction in heart failure with artificial intelligence

aDepartment of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA bResearch Laboratory for Electronics, Massachusetts Institute of Technology ...
Frontiers

Disease classification via interpretable machine learning based on multi-center routine coagulation test

This study aims to establish an interpretable disease classification model via machine learning and identify key features related to the disease to assist clinical disease diagnosis based on a ...
GitHub

ECG-DualNet: Atrial Fibrillation Classification in Electrocardiography using Deep Learning

This repository includes the code of the ECG-DualNet for ECG classification proposed in the paper Exploring Novel Algorithms for Atrial Fibrillation Detection by Driving Graduate Level Education in ...
GitHub

Stress classifier with AutoML

This repository presents an automated machine learning approach in Python to create a stress monitoring system with data from devices such as fitness trackers. With the rising popularity of trackers ...
ahajournals.org

Phenotypic Selectivity of Artificial Intelligence–Enhanced Electrocardiography in Cardiovascular Diagnosis and Risk Prediction

Artificial intelligence (AI)–enhanced ECG (AI-ECG) models are often designed to detect specific anatomical and functional cardiac abnormalities. Understanding the selectivity of their phenotypic ...
Times Higher Education

University of Technology, Iraq

Basic information and contact details for the University of Technology, Iraq The University of Technology, Iraq is one of Iraq’s largest universities and is located on the Eastern side of the city of ...
ahajournals.org

Prognostic Significance and Associations of Neural Network–Derived Electrocardiographic Features

Subtle, prognostically important ECG features may not be apparent to physicians. In the course of supervised machine learning, thousands of ECG features are identified. These are not limited to ...
Nature

Machine learning for ECG diagnosis and risk stratification of occlusion myocardial infarction

Patients with occlusion myocardial infarction (OMI) and no ST-elevation on presenting electrocardiogram (ECG) are increasing in numbers. These patients have a poor prognosis and would benefit from ...
Frontiers

WaSP-ECG: A Wave Segmentation Pretraining Toolkit for Electrocardiogram Analysis

Representation learning allows artificial intelligence (AI) models to learn useful features from large, unlabelled datasets. This can reduce the need for labelled data across a range of downstream ...
Nature

Classification of aortic stenosis using conventional machine learning and deep learning methods based on multi-dimensional cardio-mechanical signals

Heart diseases are one of the leading health hazards around the world, accounting for more than 17.9 million deaths per year in 2015, a number that is expected to grow to more than 23.6 million by ...