Kipp W. Johnson

7.4k total citations · 4 hit papers
46 papers, 2.9k citations indexed

About

Kipp W. Johnson is a scholar working on Cardiology and Cardiovascular Medicine, Artificial Intelligence and Surgery. According to data from OpenAlex, Kipp W. Johnson has authored 46 papers receiving a total of 2.9k indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Cardiology and Cardiovascular Medicine, 12 papers in Artificial Intelligence and 10 papers in Surgery. Recurrent topics in Kipp W. Johnson's work include Machine Learning in Healthcare (12 papers), Artificial Intelligence in Healthcare (7 papers) and Coronary Interventions and Diagnostics (6 papers). Kipp W. Johnson is often cited by papers focused on Machine Learning in Healthcare (12 papers), Artificial Intelligence in Healthcare (7 papers) and Coronary Interventions and Diagnostics (6 papers). Kipp W. Johnson collaborates with scholars based in United States, Italy and Canada. Kipp W. Johnson's co-authors include Benjamin S. Glicksberg, Joel T. Dudley, Khader Shameer, Riccardo Miotto, Chayakrit Krittanawong, Mohsin Ali, Jessica Torres Soto, Euan A. Ashley, Partho P. Sengupta and Jonathan L. Halperin and has published in prestigious journals such as Journal of the American College of Cardiology, PLoS ONE and Applied and Environmental Microbiology.

In The Last Decade

Kipp W. Johnson

45 papers receiving 2.8k 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.

Artificial Intelligence in Cardiology

2018 680 citations Kipp W. Johnson, Jessica Torres Soto et al. Journal of the American College of Cardiology profile →
Machine learning in cardiovascular medicine: are we there yet?

2018 320 citations Khader Shameer, Kipp W. Johnson et al. profile →
Machine learning prediction in cardiovascular diseases: a meta-analysis

2020 264 citations Chayakrit Krittanawong, Hafeez Ul Hassan Virk et al. Scientific Reports profile →
Deep learning for cardiovascular medicine: a practical primer

2019 235 citations Chayakrit Krittanawong, Kipp W. Johnson et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Kipp W. Johnson United States	23	975	582	560	545	494	46	2.9k
Chayakrit Krittanawong United States	29	1.2k 1.2×	383 0.7×	510 0.9×	387 0.7×	444 0.9×	191	3.7k
Khader Shameer United States	29	811 0.8×	495 0.9×	550 1.0×	473 0.9×	402 0.8×	83	3.7k
Peter R. Rijnbeek Netherlands	31	1.0k 1.1×	818 1.4×	213 0.4×	426 0.8×	313 0.6×	120	4.3k
Wei‐Qi Wei United States	25	373 0.4×	680 1.2×	301 0.5×	379 0.7×	393 0.8×	100	4.1k
Yilong Wang China	19	309 0.3×	744 1.3×	760 1.4×	472 0.9×	1.1k 2.2×	90	3.9k
Alvin Rajkomar United States	14	250 0.3×	880 1.5×	727 1.3×	456 0.8×	959 1.9×	23	3.1k
Girish Dwivedi Australia	33	1.8k 1.9×	318 0.5×	1.0k 1.8×	139 0.3×	211 0.4×	249	4.2k
Zachi I. Attia United States	32	4.3k 4.4×	590 1.0×	994 1.8×	453 0.8×	903 1.8×	172	6.0k
Jenna Wiens United States	26	219 0.2×	775 1.3×	396 0.7×	298 0.5×	552 1.1×	81	2.4k
Jie Xu China	28	308 0.3×	520 0.9×	1.6k 2.9×	227 0.4×	692 1.4×	146	4.2k

Countries citing papers authored by Kipp W. Johnson

Since Specialization

Citations

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

Fields of papers citing papers by Kipp W. Johnson

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Kipp W. Johnson. 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 Kipp W. Johnson. The network helps show where Kipp W. Johnson may publish in the future.

Co-authorship network of co-authors of Kipp W. Johnson

This figure shows the co-authorship network connecting the top 25 collaborators of Kipp W. Johnson. A scholar is included among the top collaborators of Kipp W. Johnson 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 Kipp W. Johnson. Kipp W. Johnson 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

Krittanawong, Chayakrit, Kipp W. Johnson, Edward Choi, et al.. (2022). Artificial Intelligence and Cardiovascular Genetics. Life. 12(2). 279–279. 32 indexed citations

Johnson, Kipp W., et al.. (2022). Association of Reduced Hospitalizations and Mortality Rates Among COVID-19-Vaccinated Patients With Heart Failure. Journal of Cardiac Failure. 28(9). 1475–1479. 11 indexed citations

Paranjpe, Ishan, Kumardeep Chaudhary, Kipp W. Johnson, et al.. (2021). Association of SARS-CoV-2 viral load at admission with in-hospital acute kidney injury: A retrospective cohort study. PLoS ONE. 16(2). e0247366–e0247366. 6 indexed citations

Kröner, Paul T., Megan Engels, Benjamin S. Glicksberg, et al.. (2021). Artificial intelligence in gastroenterology: A state-of-the-art review. World Journal of Gastroenterology. 27(40). 6794–6824. 91 indexed citations

Chaudhary, Kumardeep, Akhil Vaid, Áine Duffy, et al.. (2020). Utilization of Deep Learning for Subphenotype Identification in Sepsis-Associated Acute Kidney Injury. Clinical Journal of the American Society of Nephrology. 15(11). 1557–1565. 66 indexed citations

Russak, Adam, Farhan Chaudhry, Jessica K. De Freitas, et al.. (2020). Machine Learning in Cardiology—Ensuring Clinical Impact Lives Up to the Hype. Journal of Cardiovascular Pharmacology and Therapeutics. 25(5). 379–390. 12 indexed citations

Krittanawong, Chayakrit, Albert J. Rogers, Kipp W. Johnson, et al.. (2020). Integration of novel monitoring devices with machine learning technology for scalable cardiovascular management. Nature Reviews Cardiology. 18(2). 75–91. 145 indexed citations

Krittanawong, Chayakrit, Anirudh Kumar, Zhen Wang, et al.. (2020). Clinical features and prognosis of patients with spontaneous coronary artery dissection. International Journal of Cardiology. 312. 33–36. 16 indexed citations

Krittanawong, Chayakrit, Anirudh Kumar, Kipp W. Johnson, et al.. (2019). Prevalence, Presentation, and Associated Conditions of Patients With Fibromuscular Dysplasia. The American Journal of Cardiology. 123(7). 1169–1172. 3 indexed citations

10.

Krittanawong, Chayakrit, Anirudh Kumar, Zhen Wang, et al.. (2019). Predictors of In-Hospital Mortality after Transcatheter Aortic Valve Implantation. The American Journal of Cardiology. 125(2). 251–257. 11 indexed citations

11.

Krittanawong, Chayakrit, Albert J. Rogers, Mehmet Aydar, et al.. (2019). Integrating blockchain technology with artificial intelligence for cardiovascular medicine. Nature Reviews Cardiology. 17(1). 1–3. 81 indexed citations

12.

Krittanawong, Chayakrit, Anirudh Kumar, Hafeez Ul Hassan Virk, et al.. (2019). Recurrent spontaneous coronary artery dissection in the United States. International Journal of Cardiology. 301. 34–37. 16 indexed citations

13.

Krittanawong, Chayakrit, Anirudh Kumar, Kipp W. Johnson, et al.. (2018). Conditions and Factors Associated With Spontaneous Coronary Artery Dissection (from a National Population-Based Cohort Study). The American Journal of Cardiology. 123(2). 249–253. 52 indexed citations

14.

Johnson, Kipp W., Khader Shameer, Benjamin S. Glicksberg, et al.. (2018). A MACHINE LEARNING MODEL PREDICTS INDIVIDUALS WHO IMPROVE CORONARY ARTERY PLAQUE FIBROUS CAP THICKNESS FOLLOWING HIGH-INTENSITY STATIN THERAPY. Journal of the American College of Cardiology. 71(11). A1348–A1348. 1 indexed citations

15.

Shameer, Khader, M. Mercedes Pérez-Rodríguez, Li Li, et al.. (2018). Pharmacological risk factors associated with hospital readmission rates in a psychiatric cohort identified using prescriptome data mining. BMC Medical Informatics and Decision Making. 18(S3). 79–79. 11 indexed citations

16.

Narula, Navneet, Andrew J. Dannenberg, Jeffrey W. Olin, et al.. (2018). Pathology of Peripheral Artery Disease in Patients With Critical Limb Ischemia. Journal of the American College of Cardiology. 72(18). 2152–2163. 199 indexed citations

17.

Johnson, Kipp W., Joel T. Dudley, & Jason Bobe. (2018). A 72-Year-Old Patient with Longstanding, Untreated Familial Hypercholesterolemia but no Coronary Artery Calcification: A Case Report. Cureus. 10(4). e2452–e2452. 3 indexed citations

18.

Johnson, Kipp W., Jessica Torres Soto, Benjamin S. Glicksberg, et al.. (2018). Artificial Intelligence in Cardiology. Journal of the American College of Cardiology. 71(23). 2668–2679. 680 indexed citations breakdown →

19.

Johnson, Kipp W., Yuliya Vengrenyuk, Usman Baber, et al.. (2017). Intracoronary Imaging, Cholesterol Efflux, and Transcriptomics after Intensive Statin Treatment in Diabetes. Scientific Reports. 7(1). 7001–7001. 9 indexed citations

20.

Shameer, Khader, Kipp W. Johnson, Alexandre Yahi, et al.. (2016). PREDICTIVE MODELING OF HOSPITAL READMISSION RATES USING ELECTRONIC MEDICAL RECORD-WIDE MACHINE LEARNING: A CASE-STUDY USING MOUNT SINAI HEART FAILURE COHORT. PubMed. 22. 276–287. 106 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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