Kayvan Najarian

7.0k total citations · 1 hit paper
283 papers, 4.3k citations indexed

About

Kayvan Najarian is a scholar working on Biomedical Engineering, Computer Vision and Pattern Recognition and Cardiology and Cardiovascular Medicine. According to data from OpenAlex, Kayvan Najarian has authored 283 papers receiving a total of 4.3k indexed citations (citations by other indexed papers that have themselves been cited), including 73 papers in Biomedical Engineering, 71 papers in Computer Vision and Pattern Recognition and 52 papers in Cardiology and Cardiovascular Medicine. Recurrent topics in Kayvan Najarian's work include Non-Invasive Vital Sign Monitoring (34 papers), Medical Image Segmentation Techniques (34 papers) and Heart Rate Variability and Autonomic Control (28 papers). Kayvan Najarian is often cited by papers focused on Non-Invasive Vital Sign Monitoring (34 papers), Medical Image Segmentation Techniques (34 papers) and Heart Rate Variability and Autonomic Control (28 papers). Kayvan Najarian collaborates with scholars based in United States, Iran and Canada. Kayvan Najarian's co-authors include S. M. Reza Soroushmehr, Kevin R. Ward, Shadrokh Samavi, Nader Karimi, Ashwin Belle, Jonathan Gryak, Mohammad H. Jafari, Robert Splinter, Ebrahim Nasr-Esfahani and Elyas Sabeti and has published in prestigious journals such as Circulation, SHILAP Revista de lepidopterología and Gastroenterology.

In The Last Decade

Kayvan Najarian

272 papers receiving 4.1k 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.

Machine learning approaches and databases for prediction of drug–target interaction: a survey paper

2019 296 citations Kayvan Najarian et al. profile →

Peers

Kayvan Najarian

CVPR

RNMI

CCM

Constantinos S. Pattichis Cyprus

Konstantina S. Nikita Greece

Yun Liu United States

Petia Radeva Spain

Katherine Chou United States

Marc Coram United States

Nicos Maglaveras Greece

Riccardo Miotto United States

Kostas Marias Greece

Kelvin K. L. Wong Australia

Constantinos S. Pattichis Cyprus

Kayvan Najarian

1.0k ×1.0

1.6k ×1.8

CVPR

1.1k ×1.4

RNMI

1.3k ×1.7

1.4k ×2.5

CCM

Citations per year, relative to Kayvan Najarian Kayvan Najarian (= 1×) peers Constantinos S. Pattichis

Countries citing papers authored by Kayvan Najarian

Since Specialization

Citations

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

Fields of papers citing papers by Kayvan Najarian

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Kayvan Najarian

This figure shows the co-authorship network connecting the top 25 collaborators of Kayvan Najarian. A scholar is included among the top collaborators of Kayvan Najarian 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 Kayvan Najarian. Kayvan Najarian 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

Reavey‐Cantwell, John, et al.. (2025). AI post-intervention operational and functional outcomes prediction in ischemic stroke patients using MRIs. BMC Medical Imaging. 25(1). 329–329. 1 indexed citations

Gu, Feng, Andreas Meyer, Tonimarie Catalan, et al.. (2025). Identification of digital twins to guide interpretable AI for diagnosis and prognosis in heart failure. npj Digital Medicine. 8(1). 110–110. 5 indexed citations

Cheng, Shuyang, et al.. (2024). Learning using privileged information with logistic regression on acute respiratory distress syndrome detection. Artificial Intelligence in Medicine. 156. 102947–102947. 1 indexed citations

Mathis, Michael R., et al.. (2024). A Comparison of Interpretable Machine Learning Approaches to Identify Outpatient Clinical Phenotypes Predictive of First Acute Myocardial Infarction. Diagnostics. 14(16). 1741–1741.

Sun, Duxin, Christian Macedonia, Zhigang Chen, et al.. (2024). Can Machine Learning Overcome the 95% Failure Rate and Reality that Only 30% of Approved Cancer Drugs Meaningfully Extend Patient Survival?. Journal of Medicinal Chemistry. 67(18). 16035–16055. 3 indexed citations

Stidham, Ryan W., Shuyang Cheng, Michael Rice, et al.. (2023). Using Computer Vision to Improve Endoscopic Disease Quantification in Therapeutic Clinical Trials of Ulcerative Colitis. Gastroenterology. 166(1). 155–167.e2. 30 indexed citations

Yao, Heming, Jessica R. Golbus, Jonathan Gryak, et al.. (2022). Identifying potential candidates for advanced heart failure therapies using an interpretable machine learning algorithm. The Journal of Heart and Lung Transplantation. 41(12). 1781–1789. 6 indexed citations

Mathis, Michael R., Milo Engoren, Aaron M. Williams, et al.. (2022). Prediction of Postoperative Deterioration in Cardiac Surgery Patients Using Electronic Health Record and Physiologic Waveform Data. Anesthesiology. 137(5). 586–601. 14 indexed citations

Yao, Heming, Harm Derksen, Jessica R. Golbus, et al.. (2022). A Novel Tropical Geometry-Based Interpretable Machine Learning Method: Pilot Application to Delivery of Advanced Heart Failure Therapies. IEEE Journal of Biomedical and Health Informatics. 27(1). 239–250. 8 indexed citations

10.

Ansari, Sardar, Jessica R. Golbus, Mohamad Hakam Tiba, et al.. (2020). Detection of Low Cardiac Index Using a Polyvinylidene Fluoride-Based Wearable Ring and Convolutional Neural Networks. IEEE Sensors Journal. 21(13). 14281–14289. 3 indexed citations

11.

Soroushmehr, S. M. Reza, Craig A. Williamson, Cheng Jiang, et al.. (2017). Automated subdural hematoma segmentation for traumatic brain injured (TBI) patients. PubMed. 2017. 3069–3072. 18 indexed citations

12.

Ansari, Sardar, Marlena Duda, Hedvig Andersson, et al.. (2017). A Review of Automated Methods for Detection of Myocardial Ischemia and Infarction Using Electrocardiogram and Electronic Health Records. IEEE Reviews in Biomedical Engineering. 10. 264–298. 86 indexed citations

13.

Belle, Ashwin, et al.. (2013). Actual Brain Midline Detection using Level Set Segmentation and Window Selection. 122–126.

14.

Najarian, Kayvan, et al.. (2013). Finding an Optimal Model for Prediction of Shock Outcomes through Machine Learning. 214–218. 1 indexed citations

15.

Wu, Jie Ying, et al.. (2012). Fracture Detection in Traumatic Pelvic CT Images. International Journal of Biomedical Imaging. 2012. 1–10. 53 indexed citations

16.

Wu, Jie Ying, et al.. (2012). Hemorrhage Detection and Segmentation in Traumatic Pelvic Injuries. Computational and Mathematical Methods in Medicine. 2012. 1–12. 13 indexed citations

17.

Ji, Soo-Yeon, et al.. (2009). Abstract P115: Prediction of Severity of Blood Volume Loss Using ECG Features Based on P, QRS, and T Waves. Circulation. 120. 1 indexed citations

18.

Green, Tera Marie & Kayvan Najarian. (2007). Correlations between Emotion Regulation, Learning Performance, and Cortical Activity. eScholarship (California Digital Library). 29(29). 1 indexed citations

19.

Najarian, Kayvan, et al.. (2004). Computational analysis and classification of p53 mutants according to primary structure. 694–695. 4 indexed citations

20.

Najarian, Kayvan, et al.. (2003). Learning theory applied to Sigmoid network classification of protein biological function using primary protein structure. Discrete and Continuous Dynamical Systems. 2003. 898–904. 1 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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