Paul A. Friedman
Mayo Clinic
Internal medicineSurgeryArtificial intelligenceCardiologyRetrospective cohort studyCardiac resynchronization therapyCatheter ablationVentricular tachycardiaImplantable cardioverter-defibrillatorDefibrillationHeart failureAblationAtrial fibrillationElectrocardiographyHeart diseasePercutaneousPopulationIn patientAnesthesiaStrokeMedicineEjection fraction
644Publications
69H-index
12.9kCitations
Publications 664
Newest
#1Alejandro A. Rabinstein (Mayo Clinic)H-Index: 85
#2Micah D. Yost (Mayo Clinic)H-Index: 3
Last. Paul A. Friedman (Mayo Clinic)H-Index: 69
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Abstract null null Objectives null Embolic strokes of unknown source (ESUS) are common and often suspected to be caused by unrecognized paroxysmal atrial fibrillation (AF). An AI-enabled ECG (AI-ECG) during sinus rhythm has been shown to identify patients with unrecognized AF. We pursued this study to determine if the AI-ECG model differentiates between patients with ESUS and those with known causes of stroke, and to evaluate whether the AF prediction by AI-ECG among patients with ESUS was assoc...
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#1David M. Harmon (Mayo Clinic)H-Index: 2
#2Daniel R. Witt (Mayo Clinic)H-Index: 2
Last. Zachi I. Attia (Mayo Clinic)H-Index: 12
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#1Konstantinos C. Siontis (Mayo Clinic)H-Index: 23
#2Kan Liu (Mayo Clinic)
Last. Michael J. Ackerman (Mayo Clinic)H-Index: 144
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Abstract null null Background null There is no established screening approach for hypertrophic cardiomyopathy (HCM). We recently developed an artificial intelligence (AI) model for the detection of HCM based on the 12‑lead electrocardiogram (AI-ECG) in adults. Here, we aimed to validate this approach of ECG-based HCM detection in pediatric patients (age ≤ 18 years). null null null Methods null We identified a cohort of 300 children and adolescents with HCM (mean age 12.5 ± 4.6 years, male 68%) w...
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#1Konstantinos C. Siontis (Mayo Clinic)H-Index: 23
#2Peter A. Noseworthy (Mayo Clinic)H-Index: 45
Last. Bernard J. Gersh (Mayo Clinic)H-Index: 163
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#1Michal Cohen-Shelly (Mayo Clinic)H-Index: 1
#2Zachi I. Attia (Mayo Clinic)H-Index: 12
Last. Jae K. Oh (Mayo Clinic)H-Index: 119
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Aims Early detection of aortic stenosis (AS) is becoming increasingly important with a better outcome after aortic valve replacement in asymptomatic severe AS patients and a poor outcome in moderate AS. We aimed to develop artificial intelligence-enabled electrocardiogram (AI-ECG) using a convolutional neural network to identify patients with moderate to severe AS. Methods and results Between 1989 and 2019, 258 607 adults [mean age 63 ± 16.3 years; women 122 790 (48%)] with an echocardiography a...
5 CitationsSource
#1Vivek Y. ReddyH-Index: 92
#2Reinoud E. KnopsH-Index: 32
Last. Daniel J. CantillonH-Index: 2
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#2Zachi I. AttiaH-Index: 12
Last. Paul A. FriedmanH-Index: 69
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#1Ali Ahmad (Mayo Clinic)H-Index: 5
#2Michel T. Corban (Mayo Clinic)H-Index: 12
Last. Lilach O. Lerman (Mayo Clinic)H-Index: 92
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#1Zachi I. Attia (Mayo Clinic)H-Index: 12
#2Suraj Kapa (Mayo Clinic)H-Index: 30
Last. John Signorino (Mayo Clinic)
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Abstract null null Objective null To rapidly exclude severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection using artificial intelligence applied to the electrocardiogram (ECG). null null null Methods null A global, volunteer consortium from 4 continents identified patients with ECGs obtained around the time of polymerase chain reaction–confirmed COVID-19 diagnosis and age- and sex-matched controls from the same sites. Clinical characteristics, polymerase chain reaction results, ...
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