Sam Friedman

2.0k total citations · 1 hit paper
41 papers, 783 citations indexed

About

Sam Friedman is a scholar working on Cardiology and Cardiovascular Medicine, Computational Theory and Mathematics and Molecular Biology. According to data from OpenAlex, Sam Friedman has authored 41 papers receiving a total of 783 indexed citations (citations by other indexed papers that have themselves been cited), including 19 papers in Cardiology and Cardiovascular Medicine, 7 papers in Computational Theory and Mathematics and 6 papers in Molecular Biology. Recurrent topics in Sam Friedman's work include Cardiovascular Function and Risk Factors (8 papers), ECG Monitoring and Analysis (8 papers) and Advanced Multi-Objective Optimization Algorithms (6 papers). Sam Friedman is often cited by papers focused on Cardiovascular Function and Risk Factors (8 papers), ECG Monitoring and Analysis (8 papers) and Advanced Multi-Objective Optimization Algorithms (6 papers). Sam Friedman collaborates with scholars based in United States, Germany and Finland. Sam Friedman's co-authors include Anthony Philippakis, Patrick T. Ellinor, Steven A. Lubitz, Ioannis Stamos, Puneet Batra, Shaan Khurshid, Douglas Allaire, James P. Pirruccello, Jennifer E. Ho and Paolo Di Achille and has published in prestigious journals such as Circulation, Nature Communications and Nature Genetics.

In The Last Decade

Sam Friedman

40 papers receiving 766 citations

Hit Papers

align trajectories

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.

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						Papers	Cites
Sam Friedman United States	15	373	124	93	82	68	41	783
Shilei Sun China	14	93 0.2×	91 0.7×	40 0.4×	162 2.0×	26 0.4×	53	761
Marek Śmieja Poland	11	201 0.5×	140 1.1×	15 0.2×	105 1.3×	20 0.3×	37	839
Shengjun Zhang China	16	148 0.4×	383 3.1×	16 0.2×	67 0.8×	19 0.3×	52	1.5k
M. Brown Australia	11	108 0.3×	55 0.4×	23 0.2×	71 0.9×	22 0.3×	41	845
J. M. Grau Spain	14	200 0.5×	110 0.9×	12 0.1×	181 2.2×	27 0.4×	71	1.0k
Wenliang Che China	17	385 1.0×	162 1.3×	15 0.2×	137 1.7×	270 4.0×	63	1000
Fuhua Chen United States	17	599 1.6×	610 4.9×	42 0.5×	56 0.7×	40 0.6×	54	1.1k
Junhan Zhao United States	13	127 0.3×	215 1.7×	23 0.2×	20 0.2×	42 0.6×	52	632
Tayaza Fadason New Zealand	11	22 0.1×	185 1.5×	114 1.2×	48 0.6×	36 0.5×	23	701

Countries citing papers authored by Sam Friedman

Since Specialization

Citations

This map shows the geographic impact of Sam Friedman'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 Sam Friedman with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sam Friedman more than expected).

Fields of papers citing papers by Sam Friedman

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Sam Friedman. 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 Sam Friedman. The network helps show where Sam Friedman may publish in the future.

Co-authorship network of co-authors of Sam Friedman

This figure shows the co-authorship network connecting the top 25 collaborators of Sam Friedman. A scholar is included among the top collaborators of Sam Friedman 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 Sam Friedman. Sam Friedman is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Friedman, Sam, Shaan Khurshid, Xin Wang, et al.. (2025). Unsupervised deep learning of electrocardiograms enables scalable human disease profiling. npj Digital Medicine. 8(1). 23–23. 4 indexed citations

Kany, Shinwan, Sam Friedman, Mostafa A. Al‐Alusi, et al.. (2025). Electrocardiogram-Based Artificial Intelligence to Identify Coronary Artery Disease. JACC Advances. 4(9). 102041–102041. 1 indexed citations

Urbut, Sarah, Ozan Ünlü, Sam Friedman, et al.. (2025). Genomic Drivers of Coronary Artery Disease and Risk of Future Outcomes After Coronary Angiography. JAMA Network Open. 8(1). e2455368–e2455368. 1 indexed citations

Khurshid, Shaan, Sam Friedman, Mostafa A. Al‐Alusi, et al.. (2025). Artificial intelligence-enabled analysis of handheld single-lead electrocardiograms to predict incident atrial fibrillation: an analysis of the VITAL-AF randomized trial. npj Digital Medicine. 8(1). 776–776.

Pirruccello, James P., Paolo Di Achille, Seung Hoan Choi, et al.. (2024). Deep learning of left atrial structure and function provides link to atrial fibrillation risk. Nature Communications. 15(1). 4304–4304. 10 indexed citations

Rämö, Joel, Sean J. Jurgens, Shinwan Kany, et al.. (2024). Rare Genetic Variants in LDLR , APOB , and PCSK9 Are Associated With Aortic Stenosis. Circulation. 150(22). 1767–1780. 8 indexed citations

Friedman, Sam, et al.. (2024). Genetic Architectures of Medical Images Revealed by Registration of Multiple Modalities. Bioinformatics and Biology Insights. 18. 769847737–769847737. 1 indexed citations

Wang, Xin, Shaan Khurshid, Seung Hoan Choi, et al.. (2023). Genetic Susceptibility to Atrial Fibrillation Identified via Deep Learning of 12-Lead Electrocardiograms. Circulation Genomic and Precision Medicine. 16(4). 340–349. 7 indexed citations

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

10.

Nauffal, Victor, Paolo Di Achille, Marcus D. R. Klarqvist, et al.. (2023). Genetics of myocardial interstitial fibrosis in the human heart and association with disease. Nature Genetics. 55(5). 777–786. 39 indexed citations

11.

Radhakrishnan, Adityanarayanan, Sam Friedman, Shaan Khurshid, et al.. (2023). Cross-modal autoencoder framework learns holistic representations of cardiovascular state. Nature Communications. 14(1). 2436–2436. 30 indexed citations

12.

Haimovich, Julian S., Shaan Khurshid, Paolo Di Achille, et al.. (2023). Artificial intelligence–enabled classification of hypertrophic heart diseases using electrocardiograms. PubMed. 4(2). 48–59. 23 indexed citations

13.

Khurshid, Shaan, Julieta Lazarte, James P. Pirruccello, et al.. (2023). Clinical and genetic associations of deep learning-derived cardiac magnetic resonance-based left ventricular mass. Nature Communications. 14(1). 1558–1558. 23 indexed citations

14.

Pirruccello, James P., Joel Rämö, Seung Hoan Choi, et al.. (2023). The Genetic Determinants of Aortic Distention. Journal of the American College of Cardiology. 81(14). 1320–1335. 14 indexed citations

15.

Friedman, Sam, et al.. (2023). Trajectories of sentiment in 11,816 psychoactive narratives. Human Psychopharmacology Clinical and Experimental. 39(1). e2889–e2889. 2 indexed citations

16.

Jakeman, John, et al.. (2022). Adaptive experimental design for multi‐fidelity surrogate modeling of multi‐disciplinary systems. International Journal for Numerical Methods in Engineering. 123(12). 2760–2790. 9 indexed citations

17.

Nekoui, Mahan, James P. Pirruccello, Paolo Di Achille, et al.. (2022). Spatially Distinct Genetic Determinants of Aortic Dimensions Influence Risks of Aneurysm and Stenosis. Journal of the American College of Cardiology. 80(5). 486–497. 15 indexed citations

18.

Haas, Mary E., James P. Pirruccello, Sam Friedman, et al.. (2021). Machine learning enables new insights into genetic contributions to liver fat accumulation. Cell Genomics. 1(3). 100066–100066. 55 indexed citations

19.

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

20.

Pirruccello, James P., Alexander G. Bick, Minxian Wang, et al.. (2020). Analysis of cardiac magnetic resonance imaging in 36,000 individuals yields genetic insights into dilated cardiomyopathy. Nature Communications. 11(1). 129 indexed citations

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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