Puneet Batra

2.0k total citations · 1 hit paper
37 papers, 751 citations indexed

About

Puneet Batra is a scholar working on Cardiology and Cardiovascular Medicine, Genetics and Epidemiology. According to data from OpenAlex, Puneet Batra has authored 37 papers receiving a total of 751 indexed citations (citations by other indexed papers that have themselves been cited), including 23 papers in Cardiology and Cardiovascular Medicine, 7 papers in Genetics and 5 papers in Epidemiology. Recurrent topics in Puneet Batra's work include Cardiovascular Function and Risk Factors (11 papers), Genetic Associations and Epidemiology (7 papers) and Cardiovascular Disease and Adiposity (5 papers). Puneet Batra is often cited by papers focused on Cardiovascular Function and Risk Factors (11 papers), Genetic Associations and Epidemiology (7 papers) and Cardiovascular Disease and Adiposity (5 papers). Puneet Batra collaborates with scholars based in United States, Netherlands and Germany. Puneet Batra's co-authors include Anthony Philippakis, Patrick T. Ellinor, Steven A. Lubitz, Sam Friedman, Shaan Khurshid, Nathaniel Diamant, Paolo Di Achille, Jennifer E. Ho, Kenney Ng and Amit V. Khera and has published in prestigious journals such as New England Journal of Medicine, The Lancet and Circulation.

In The Last Decade

Puneet Batra

35 papers receiving 741 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
Puneet Batra United States	15	375	138	96	93	88	37	751
Gunnar Brobert Germany	16	276 0.7×	99 0.7×	75 0.8×	36 0.4×	37 0.4×	47	777
Steve Garratt United Kingdom	6	300 0.8×	129 0.9×	49 0.5×	238 2.6×	52 0.6×	6	632
Biqi Wang China	11	454 1.2×	53 0.4×	135 1.4×	39 0.4×	72 0.8×	37	730
Hiroaki Semba Japan	10	234 0.6×	52 0.4×	126 1.3×	39 0.4×	46 0.5×	45	531
Michael Lichtenauer Austria	15	351 0.9×	133 1.0×	159 1.7×	47 0.5×	27 0.3×	92	721
Daniela Baumgartner Austria	16	281 0.7×	191 1.4×	221 2.3×	55 0.6×	190 2.2×	35	829
Wenyao Wang China	14	323 0.9×	174 1.3×	67 0.7×	94 1.0×	44 0.5×	69	726
Deepa M. Gopal United States	17	477 1.3×	96 0.7×	287 3.0×	78 0.8×	16 0.2×	49	983
Hala M. Raslan Egypt	14	120 0.3×	92 0.7×	120 1.3×	55 0.6×	59 0.7×	47	620
Saif Ahmad United Kingdom	13	327 0.9×	114 0.8×	120 1.3×	73 0.8×	29 0.3×	27	882

Countries citing papers authored by Puneet Batra

Since Specialization

Citations

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

Fields of papers citing papers by Puneet Batra

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Puneet Batra

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

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

1.

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

2.

Reeder, Christopher, Brian Claggett, Pulkit Singh, et al.. (2024). Natural Language Processing to Adjudicate Heart Failure Hospitalizations in Global Clinical Trials. Circulation Heart Failure. 18(1). e012514–e012514. 1 indexed citations

3.

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

4.

Nauffal, Victor, Marcus D. R. Klarqvist, Matthew C. Hill, et al.. (2024). Noninvasive assessment of organ-specific and shared pathways in multi-organ fibrosis using T1 mapping. Nature Medicine. 30(6). 1749–1760. 16 indexed citations

5.

Lau, Emily S., Paolo Di Achille, Pulkit Singh, et al.. (2023). Deep Learning–Enabled Assessment of Left Heart Structure and Function Predicts Cardiovascular Outcomes. Journal of the American College of Cardiology. 82(20). 1936–1948. 27 indexed citations

6.

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

7.

Kartoun, Uri, Akl C. Fahed, Shinwan Kany, et al.. (2023). Exploring the link between Gilbert’s syndrome and atherosclerotic cardiovascular disease: insights from a subpopulation-based analysis of over one million individuals. European Heart Journal Open. 3(3). oead059–oead059. 2 indexed citations

8.

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

9.

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.

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

11.

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

12.

Cunningham, Jonathan W., Paolo Di Achille, Valerie N. Morrill, et al.. (2022). Machine Learning to Understand Genetic and Clinical Factors Associated With the Pulse Waveform Dicrotic Notch. Circulation Genomic and Precision Medicine. 16(1). e003676–e003676. 10 indexed citations

13.

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

14.

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

15.

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

16.

Agrawal, Saaket, Marcus D. R. Klarqvist, Connor A. Emdin, et al.. (2021). Selection of 51 predictors from 13,782 candidate multimodal features using machine learning improves coronary artery disease prediction. Patterns. 2(12). 100364–100364. 29 indexed citations

17.

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

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.

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

20.

Dhar, Vasant, et al.. (2019). Transforming Finance Into Vision: Concurrent Financial Time Series as Convolutional Nets. Big Data. 7(4). 276–285. 5 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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