| 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 | 129 | 2022 |
| 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 | 52 | 2022 |
| 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 | 37 | 2022 |
| Patient contrastive learning: A performant, expressive, and practical approach to electrocardiogram modeling N Diamant, E Reinertsen, S Song, AD Aguirre, CM Stultz, P Batra PLoS computational biology 18 (2), e1009862, 2022 | 35 | 2022 |
| 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 | 29 | 2023 |
| 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 | 29 | 2021 |
| A deep learning model for inferring elevated pulmonary capillary wedge pressures from the 12-lead electrocardiogram DE Schlesinger, N Diamant, A Raghu, E Reinertsen, K Young, P Batra, ... JACC: Advances 1 (1), 100003, 2022 | 15 | 2022 |
| 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 | 15 | 2021 |
| 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 | 12 | 2022 |
| Physiology as a Lingua Franca for clinical machine learning GP Sarma, E Reinertsen, A Aguirre, C Anderson, P Batra, SH Choi, ... Patterns 1 (2), 2020 | 11 | 2020 |
| Scalable querying of human cell atlases via a foundational model reveals commonalities across fibrosis-associated macrophages G Heimberg, T Kuo, D DePianto, T Heigl, N Diamant, O Salem, G Scalia, ... bioRxiv, 2023.07. 18.549537, 2023 | 10 | 2023 |
| Stab: Self-supervised learning for tabular data E Hajiramezanali, NL Diamant, G Scalia, MW Shen NeurIPS 2022 First Table Representation Workshop, 2022 | 10 | 2022 |
| 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 | 7 | 2023 |
| Cohort design and natural language processing to reduce bias in electronic health records research: The Community Care Cohort Project S Khurshid, C Reeder, LX Harrington, P Singh, G Sarma, SF Friedman, ... medRxiv, 2021.05. 26.21257872, 2021 | 7 | 2021 |
| 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 | 5 | 2023 |
| 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 | 5 | 2022 |
| 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 | 5 | 2021 |
| Fine-Tuning of Continuous-Time Diffusion Models as Entropy-Regularized Control M Uehara, Y Zhao, K Black, E Hajiramezanali, G Scalia, NL Diamant, ... arXiv preprint arXiv:2402.15194, 2024 | 4 | 2024 |
| 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 | 3 | 2022 |
| Deep learned representations of the resting 12-lead electrocardiogram to predict at peak exercise S Khurshid, TW Churchill, N Diamant, P Di Achille, C Reeder, P Singh, ... European Journal of Preventive Cardiology 31 (2), 252-262, 2024 | 2 | 2024 |
Puneet Batra, PhDDirector of Machine Learning, The Broad Institute of Harvard and MITVerified email at post.harvard.edu
