Nathaniel Diamant

985 total citations · 1 hit paper
16 papers, 405 citations indexed

About

Nathaniel Diamant is a scholar working on Cardiology and Cardiovascular Medicine, Pulmonary and Respiratory Medicine and Molecular Biology. According to data from OpenAlex, Nathaniel Diamant has authored 16 papers receiving a total of 405 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Cardiology and Cardiovascular Medicine, 3 papers in Pulmonary and Respiratory Medicine and 2 papers in Molecular Biology. Recurrent topics in Nathaniel Diamant's work include Cardiovascular Disease and Adiposity (5 papers), Cardiovascular Function and Risk Factors (4 papers) and ECG Monitoring and Analysis (3 papers). Nathaniel Diamant is often cited by papers focused on Cardiovascular Disease and Adiposity (5 papers), Cardiovascular Function and Risk Factors (4 papers) and ECG Monitoring and Analysis (3 papers). Nathaniel Diamant collaborates with scholars based in United States, Switzerland and Canada. Nathaniel Diamant's co-authors include Puneet Batra, Patrick T. Ellinor, Anthony Philippakis, Jennifer E. Ho, Paolo Di Achille, Sam Friedman, Steven A. Lubitz, Christopher D. Anderson, Shaan Khurshid and Mostafa A. Al‐Alusi and has published in prestigious journals such as Nature, Circulation and Nature Communications.

In The Last Decade

Nathaniel Diamant

14 papers receiving 401 citations

Hit Papers

ECG-Based Deep Learning and Clinical Risk Factors to Pred... 2021 2026 2022 2024 2021 50 100 150
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. ECG-Based Deep Learning and Clinical Risk Factors to Predict Atrial Fibrillation
    2021 167 citations Shaan Khurshid, Sam Friedman et al. Circulation profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Nathaniel Diamant United States 7 240 63 62 52 44 16 405
Jonas Ghouse Denmark 15 376 1.6× 16 0.3× 44 0.7× 103 2.0× 53 1.2× 36 592
Carl Hayward United Kingdom 14 351 1.5× 23 0.4× 55 0.9× 59 1.1× 13 0.3× 41 479
N. Lan Australia 11 217 0.9× 19 0.3× 37 0.6× 44 0.8× 17 0.4× 74 438
Stefan van Duijvenboden United Kingdom 14 351 1.5× 22 0.3× 19 0.3× 94 1.8× 74 1.7× 49 469
Aruni Seneviratna Singapore 11 120 0.5× 37 0.6× 37 0.6× 88 1.7× 6 0.1× 26 320
Richard Carrick United States 12 331 1.4× 77 1.2× 53 0.9× 135 2.6× 5 0.1× 36 535
Monika Turk Slovenia 12 196 0.8× 33 0.5× 106 1.7× 29 0.6× 13 0.3× 15 537
Barış İkitimur Türkiye 14 220 0.9× 46 0.7× 27 0.4× 128 2.5× 5 0.1× 64 541
Kaustubha D. Patil United States 8 215 0.9× 41 0.7× 24 0.4× 148 2.8× 11 0.3× 19 483
Daniele Della Latta Italy 13 196 0.8× 38 0.6× 32 0.5× 48 0.9× 6 0.1× 34 451

Countries citing papers authored by Nathaniel Diamant

Since Specialization
Citations

This map shows the geographic impact of Nathaniel Diamant's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Nathaniel Diamant with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Nathaniel Diamant more than expected).

Fields of papers citing papers by Nathaniel Diamant

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Nathaniel Diamant. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Nathaniel Diamant. The network helps show where Nathaniel Diamant may publish in the future.

Co-authorship network of co-authors of Nathaniel Diamant

This figure shows the co-authorship network connecting the top 25 collaborators of Nathaniel Diamant. A scholar is included among the top collaborators of Nathaniel Diamant based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Nathaniel Diamant. Nathaniel Diamant is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

16 of 16 papers shown
1.
Scalia, Gabriele, Steven T. Rutherford, Kerry R. Buchholz, et al.. (2025). Deep-learning-based virtual screening of antibacterial compounds. Nature Biotechnology.
2.
Heimberg, Graham, Tony Kuo, Daryle J. DePianto, et al.. (2024). A cell atlas foundation model for scalable search of similar human cells. Nature. 638(8052). 1085–1094. 30 indexed citations
3.
Khurshid, Shaan, Timothy W. Churchill, Nathaniel Diamant, et al.. (2023). Deep learned representations of the resting 12-lead electrocardiogram to predict at peak exercise. European Journal of Preventive Cardiology. 31(2). 252–262. 6 indexed citations
4.
Agrawal, Saaket, Marcus D. R. Klarqvist, Nathaniel Diamant, et al.. (2023). BMI-adjusted adipose tissue volumes exhibit depot-specific and divergent associations with cardiometabolic diseases. Nature Communications. 14(1). 266–266. 43 indexed citations
5.
Rambarat, Paula, Emily K. Zern, Dongyu Wang, et al.. (2023). Identifying high risk clinical phenogroups of pulmonary hypertension through a clustering analysis. PLoS ONE. 18(8). e0290553–e0290553.
6.
Klarqvist, Marcus D. R., Saaket Agrawal, Nathaniel Diamant, et al.. (2022). Silhouette images enable estimation of body fat distribution and associated cardiometabolic risk. npj Digital Medicine. 5(1). 105–105. 8 indexed citations
7.
Agrawal, Saaket, Minxian Wang, Marcus D. R. Klarqvist, et al.. (2022). Inherited basis of visceral, abdominal subcutaneous and gluteofemoral fat depots. Nature Communications. 13(1). 3771–3771. 75 indexed citations
8.
Diamant, Nathaniel, Erik Reinertsen, Steven Song, et al.. (2022). Patient contrastive learning: A performant, expressive, and practical approach to electrocardiogram modeling. PLoS Computational Biology. 18(2). e1009862–e1009862. 24 indexed citations
9.
Diamant, Nathaniel, Aniruddh Raghu, Erik Reinertsen, et al.. (2022). A DEEP LEARNING MODEL FOR INFERRING ELEVATED PULMONARY CAPILLARY WEDGE PRESSURES FROM THE 12-LEAD ELECTROCARDIOGRAM. Journal of the American College of Cardiology. 79(9). 2016–2016. 2 indexed citations
10.
Diamant, Nathaniel, Aniruddh Raghu, Erik Reinertsen, et al.. (2022). A Deep Learning Model for Inferring Elevated Pulmonary Capillary Wedge Pressures From the 12-Lead Electrocardiogram. JACC Advances. 1(1). 100003–100003. 6 indexed citations
11.
Agrawal, Saaket, Marcus D. R. Klarqvist, Nathaniel Diamant, et al.. (2021). Abstract 12760: Association of Machine Learning-Derived Measures of Body Fat Distribution in >40,000 Individuals With Cardiometabolic Diseases. Circulation. 1 indexed citations
12.
Agrawal, Saaket, Marcus D. R. Klarqvist, Nathaniel Diamant, et al.. (2021). Abstract 12760: Association of Machine Learning-Derived Measures of Body Fat Distribution in >40,000 Individuals With Cardiometabolic Diseases. Circulation. 144(Suppl_1). 1 indexed citations
13.
Khurshid, Shaan, Sam Friedman, James P. Pirruccello, et al.. (2021). Deep learning to estimate cardiac magnetic resonance–derived left ventricular mass. SHILAP Revista de lepidopterología. 2(2). 109–117. 5 indexed citations
14.
Khurshid, Shaan, Sam Friedman, James P. Pirruccello, et al.. (2021). Deep Learning to Predict Cardiac Magnetic Resonance–Derived Left Ventricular Mass and Hypertrophy From 12-Lead ECGs. Circulation Cardiovascular Imaging. 14(6). e012281–e012281. 35 indexed citations
15.
Khurshid, Shaan, Sam Friedman, Christopher Reeder, et al.. (2021). Abstract 12922: Electrocardiogram-Based Deep Learning and Clinical Risk Factors to Predict Incident Atrial Fibrillation. Circulation. 144(Suppl_1). 2 indexed citations
16.
Khurshid, Shaan, Sam Friedman, Christopher Reeder, et al.. (2021). ECG-Based Deep Learning and Clinical Risk Factors to Predict Atrial Fibrillation. Circulation. 145(2). 122–133. 167 indexed citations breakdown →

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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