Area of research
Cardiology and Cardiovascular Medicine
Research interest
Research interests include Heart Failure Treatment and Management, Atrial Fibrillation Management and Outcomes, Cardiovascular Function and Risk Factors, and Cardiac Arrhythmias and Treatments.
Abstract 4370694: Longitudinal Evaluation of Anti-Arrhythmic Drug Use to Predict Hospitalization or Death in Patients with Ventricular Tachycardia
Abstract 4370248: Novel Foundation Models for Detecting and Generating Text Reports of Atrial Fibrillation from 12-lead ECGs in a Large Registry
Abstract 4370585: AI-based prediction of mortality in patients with ventricular tachycardia
Abstract 4369502: Identifying optimum ECG features to predict sudden cardiac arrest at varying time points before the event
Abstract 4366827: Large Language Models Detect Ventricular Tachycardia Recurrence in Clinical Notes and Enable Prediction of Clinical Outcomes at Scale
NOVEL INTERACTIONS BETWEEN STROKE RISK FACTORS IN THE YOUNG REVEALED BY INTERNET SEARCH ALGORITHMS APPLIED TO ELECTRONIC HEALTH RECORDS
Machine learning of electrophysiological signals for the prediction of ventricular arrhythmias: systematic review and examination of heterogeneity between studies
Atrial fibrillation ablation outcome prediction with a machine learning fusion framework incorporating cardiac computed tomography
OBSTRUCTIVE SLEEP APNEA PORTENDS STROKE IN YOUNG INDIVIDUALS WITHOUT ATRIAL FIBRILLATION: A LARGE REGISTRY STUDY
VENTRICULAR TACHYCARDIA PREDICTS ATRIAL FIBRILLATION RECURRENCE POST ABLATION: A PROPENSITY SCORE-MATCHED ANALYSIS OF A LARGE PROSPECTIVE STUDY
Predicting success of atrial fibrillation ablation: comparing machine learning approaches of intracardiac electrograms
Noise reduction in electrophysiological signals using transfer machine learning
UNSUPERVISED MACHINE LEARNING IDENTIFIES PHENOTYPES FOR ATRIAL FIBRILLATION THAT PREDICT ACUTE ABLATION SUCCESS
Automatic left atrial segmentation from cardiac CT using computer graphics imaging and deep learning
Defining refractoriness in single atrial beats using autoencoder neural networks
Artificial intelligence to reduce artifact in cardiac electrophysiological signals
Novel electrogram featurization reveals a spectrum of response to ablation from atrial tachycardia to types of atrial fibrillation
Spatiotemporal signatures of response to atrial fibrillation ablation
Reduction of artifacts and noise in small electrogram datasets without manual annotation using transfer machine learning
Extent of atrium with 1:1 electrogram activation predicts response to ablation of atrial fibrillation
Machine Learning to Classify Intracardiac Electrical Patterns During Atrial Fibrillation
Machine Learned Cellular Phenotypes in Cardiomyopathy Predict Sudden Death
Safety and Efficacy of Minimal- versus Zero-fluoroscopy Radiofrequency Catheter Ablation for Atrial Fibrillation: A Multicenter, Prospective Study
PREDICTING SUDDEN CARDIAC DEATH BY MACHINE LEARNING OF VENTRICULAR ACTION POTENTIALS
Abstract 16313: Islands of Organized 1:1 Conduction Within Atrial Fibrillation as Potential Targets for Ablation
Usefulness of Proneurotensin to Predict Cardiovascular and All-Cause Mortality in a United States Population (from the Reasons for Geographic and Racial Differences in Stroke Study)
Web Camera Based Eye Tracking to Assess Visual Memory on a Visual Paired Comparison Task
Effect of therapeutic interventions on oxidized phospholipids on apolipoprotein B100 and lipoprotein(a)
Neutrophil Gelatinase-Associated Lipocalin for Acute Kidney Injury During Acute Heart Failure Hospitalizations
Organized Sources Are Spatially Conserved in Recurrent Compared to Pre‐Ablation Atrial Fibrillation: Further Evidence for Non‐Random Electrical Substrates