Articles with public access mandates - Nathaniel DiamantLearn more
Available somewhere: 14
ECG-based deep learning and clinical risk factors to predict atrial fibrillation
S Khurshid, S Friedman, C Reeder, P Di Achille, N Diamant, P Singh, ...
Circulation 145 (2), 122-133, 2022
Mandates: US National Institutes of Health, American Heart Association, British Heart …
Inherited basis of visceral, abdominal subcutaneous and gluteofemoral fat depots
S Agrawal, M Wang, MDR Klarqvist, K Smith, J Shin, H Dashti, N Diamant, ...
Nature communications 13 (1), 3771, 2022
Mandates: US National Institutes of Health, UK Medical Research Council, Dutch Heart …
Cohort design and natural language processing to reduce bias in electronic health records research
S Khurshid, C Reeder, LX Harrington, P Singh, G Sarma, SF Friedman, ...
Npj Digital Medicine 5 (1), 47, 2022
Mandates: US National Institutes of Health, American Heart Association, Leducq …
BMI-adjusted adipose tissue volumes exhibit depot-specific and divergent associations with cardiometabolic diseases
S Agrawal, MDR Klarqvist, N Diamant, TL Stanley, PT Ellinor, NN Mehta, ...
Nature Communications 14 (1), 266, 2023
Mandates: US National Institutes of Health
Deep learning to predict cardiac magnetic resonance–derived left ventricular mass and hypertrophy from 12-lead ECGs
S Khurshid, S Friedman, JP Pirruccello, P Di Achille, N Diamant, ...
Circulation: Cardiovascular Imaging 14 (6), e012281, 2021
Mandates: US National Institutes of Health, American Heart Association, UK Medical …
Association of machine learning-derived measures of body fat distribution with cardiometabolic diseases in> 40,000 individuals
S Agrawal, MDR Klarqvist, N Diamant, TL Stanley, PT Ellinor, NN Mehta, ...
MedRxiv, 2021.05. 07.21256854, 2021
Mandates: US National Institutes of Health
Silhouette images enable estimation of body fat distribution and associated cardiometabolic risk
MDR Klarqvist, S Agrawal, N Diamant, PT Ellinor, A Philippakis, K Ng, ...
NPJ digital medicine 5 (1), 105, 2022
Mandates: US National Institutes of Health, UK Medical Research Council
Genetic susceptibility to atrial fibrillation identified via deep learning of 12-lead electrocardiograms
X Wang, S Khurshid, SH Choi, S Friedman, LC Weng, C Reeder, ...
Circulation: Genomic and Precision Medicine 16 (4), 340-349, 2023
Mandates: US National Institutes of Health, American Heart Association
Artificial intelligence–enabled classification of hypertrophic heart diseases using electrocardiograms
JS Haimovich, N Diamant, S Khurshid, P Di Achille, C Reeder, ...
Cardiovascular Digital Health Journal 4 (2), 48-59, 2023
Mandates: US National Institutes of Health, American Heart Association
Deep learning to estimate cardiac magnetic resonance–derived left ventricular mass
S Khurshid, SF Friedman, JP Pirruccello, P Di Achille, N Diamant, ...
Cardiovascular Digital Health Journal 2 (2), 109-117, 2021
Mandates: US National Institutes of Health, American Heart Association, Leducq …
Deep learning on resting electrocardiogram to identify impaired heart rate recovery
N Diamant, P Di Achille, LC Weng, ES Lau, S Khurshid, S Friedman, ...
Cardiovascular Digital Health Journal 3 (4), 161-170, 2022
Mandates: US National Institutes of Health, American Heart Association, UK Medical …
Deep Learning of Electrocardiograms Enables Scalable Human Disease Profiling
RA Venn, X Wang, SF Friedman, N Diamant, S Khurshid, PD Achille, ...
medRxiv, 2022.12. 21.22283757, 2022
Mandates: US National Institutes of Health, American Heart Association
Estimating body fat distribution–a driver of cardiometabolic health–from silhouette images
MDR Klarqvist, S Agrawal, N Diamant, PT Ellinor, A Philippakis, K Ng, ...
medRxiv, 2022.01. 14.22269328, 2022
Mandates: US National Institutes of Health
Identifying high risk clinical phenogroups of pulmonary hypertension through a clustering analysis
P Rambarat, EK Zern, D Wang, A Roshandelpoor, S Zarbafian, EE Liu, ...
Plos one 18 (8), e0290553, 2023
Mandates: US National Institutes of Health
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