AI Medical Compendium Journal:
Journal of the American Heart Association

Showing 41 to 50 of 64 articles

Deep Learning Algorithm for Automated Cardiac Murmur Detection via a Digital Stethoscope Platform.

Journal of the American Heart Association
Background Clinicians vary markedly in their ability to detect murmurs during cardiac auscultation and identify the underlying pathological features. Deep learning approaches have shown promise in medicine by transforming collected data into clinical...

Deep Neural Network Approach for Continuous ECG-Based Automated External Defibrillator Shock Advisory System During Cardiopulmonary Resuscitation.

Journal of the American Heart Association
Background Because chest compressions induce artifacts in the ECG, current automated external defibrillators instruct the user to stop cardiopulmonary resuscitation (CPR) while an automated rhythm analysis is performed. It has been shown that minimiz...

In Search of an Optimal Subset of ECG Features to Augment the Diagnosis of Acute Coronary Syndrome at the Emergency Department.

Journal of the American Heart Association
Background Classical ST-T waveform changes on standard 12-lead ECG have limited sensitivity in detecting acute coronary syndrome (ACS) in the emergency department. Numerous novel ECG features have been previously proposed to augment clinicians' decis...

Vascular Aging Detected by Peripheral Endothelial Dysfunction Is Associated With ECG-Derived Physiological Aging.

Journal of the American Heart Association
Background An artificial intelligence algorithm that detects age using the 12-lead ECG has been suggested to signal "physiologic age." This study aimed to investigate the association of peripheral microvascular endothelial function (PMEF) as an index...

Machine Learning-Based Risk Assessment for Cancer Therapy-Related Cardiac Dysfunction in 4300 Longitudinal Oncology Patients.

Journal of the American Heart Association
Background The growing awareness of cardiovascular toxicity from cancer therapies has led to the emerging field of cardio-oncology, which centers on preventing, detecting, and treating patients with cardiac dysfunction before, during, or after cancer...

Tree-Based Machine Learning to Identify and Understand Major Determinants for Stroke at the Neighborhood Level.

Journal of the American Heart Association
Background Stroke is a major cardiovascular disease that causes significant health and economic burden in the United States. Neighborhood community-based interventions have been shown to be both effective and cost-effective in preventing cardiovascul...

Claims-Based Algorithms for Identifying Patients With Pulmonary Hypertension: A Comparison of Decision Rules and Machine-Learning Approaches.

Journal of the American Heart Association
Background Real-world healthcare data are an important resource for epidemiologic research. However, accurate identification of patient cohorts-a crucial first step underpinning the validity of research results-remains a challenge. We developed and e...

Predicting the Risk of Adverse Events in Pregnant Women With Congenital Heart Disease.

Journal of the American Heart Association
Background Women with congenital heart disease are considered at high risk for adverse events. Therefore, we aim to establish 2 prediction models for mothers and their offspring, which can predict the risk of adverse events occurred in pregnant women...

Automatic Triage of 12-Lead ECGs Using Deep Convolutional Neural Networks.

Journal of the American Heart Association
BACKGROUND The correct interpretation of the ECG is pivotal for the accurate diagnosis of many cardiac abnormalities, and conventional computerized interpretation has not been able to reach physician-level accuracy in detecting (acute) cardiac abnorm...