Condition: Atrial Fibrillation · Arrhythmias, Cardiac · Sponsor: Stanford University
Atrial fibrillation is a serious public health issue that affects over 5 million Americans (Miyazaka, Circulation 2006) in whom it may cause skipped beats, dizziness, stroke and even death. Therapy for AF is currently suboptimal, in part because AF represents several disease states of which few have been delineated or used to successfully guide management. This study seeks to clarify this delineation of AF types using machine learning (ML).
This description comes directly from the study's public registry record.
Sanjiv Narayan, MD · 650-724-1850 · sanjiv1@stanford.edu
Kathleen Mills, BA · kmills2@stanford.edu
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| Stanford University | Stanford, California, United States | Recruiting |
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Source record: clinicaltrials.gov/study/NCT05371405