Eugene Tseytlin

985 total citations
27 papers, 692 citations indexed

About

Eugene Tseytlin is a scholar working on Artificial Intelligence, Molecular Biology and Family Practice. According to data from OpenAlex, Eugene Tseytlin has authored 27 papers receiving a total of 692 indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Artificial Intelligence, 10 papers in Molecular Biology and 10 papers in Family Practice. Recurrent topics in Eugene Tseytlin's work include Biomedical Text Mining and Ontologies (10 papers), Clinical Reasoning and Diagnostic Skills (10 papers) and AI in cancer detection (6 papers). Eugene Tseytlin is often cited by papers focused on Biomedical Text Mining and Ontologies (10 papers), Clinical Reasoning and Diagnostic Skills (10 papers) and AI in cancer detection (6 papers). Eugene Tseytlin collaborates with scholars based in United States, Canada and India. Eugene Tseytlin's co-authors include Elizabeth Legowski, Rebecca S. Crowley, Olga Medvedeva, D.M. Jukic, Melissa Castine, Rebecca S. Jacobson, Girish Chavan, Tanja Bekhuis, Kevin J. Mitchell and Roger Azevedo and has published in prestigious journals such as PLoS ONE, Cancer Research and BMC Bioinformatics.

In The Last Decade

Eugene Tseytlin

27 papers receiving 659 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Eugene Tseytlin United States 15 341 171 133 107 101 27 692
Olga Medvedeva United States 11 258 0.8× 103 0.6× 79 0.6× 58 0.5× 78 0.8× 18 425
Elizabeth Legowski United States 11 202 0.6× 110 0.6× 56 0.4× 77 0.7× 86 0.9× 14 408
Steven Bedrick United States 17 406 1.2× 250 1.5× 56 0.4× 46 0.4× 7 0.1× 64 978
Rafat Damseh United Arab Emirates 13 165 0.5× 42 0.2× 311 2.3× 42 0.4× 38 0.4× 36 816
Eugenio Alberdi United Kingdom 10 171 0.5× 23 0.1× 69 0.5× 19 0.2× 17 0.2× 20 456
Giovanni Briganti Belgium 13 152 0.4× 47 0.3× 173 1.3× 53 0.5× 20 0.2× 45 942
Nikolay S. Markov United States 8 156 0.5× 156 0.9× 112 0.8× 35 0.3× 26 0.3× 15 842
Christopher Parisien Canada 7 330 1.0× 69 0.4× 90 0.7× 27 0.3× 5 0.0× 13 626
Moritz Lehne Germany 14 161 0.5× 113 0.7× 87 0.7× 128 1.2× 6 0.1× 22 1.1k
Amy Franklin United States 14 87 0.3× 53 0.3× 26 0.2× 156 1.5× 36 0.4× 42 694

Countries citing papers authored by Eugene Tseytlin

Since Specialization
Citations

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

Fields of papers citing papers by Eugene Tseytlin

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Eugene Tseytlin

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

All Works

20 of 20 papers shown
1.
Savova, Guergana, Eugene Tseytlin, Sean Finan, et al.. (2017). DeepPhe: A Natural Language Processing System for Extracting Cancer Phenotypes from Clinical Records. Cancer Research. 77(21). e115–e118. 65 indexed citations
2.
Savova, Guergana, Eugene Tseytlin, Sean Finan, et al.. (2017). DeepPhe - A Natural Language Processing System for Extracting Cancer Phenotypes from Clinical Records.. AMIA. 1 indexed citations
3.
Castro, Sergio, Eugene Tseytlin, Olga Medvedeva, et al.. (2017). Automated annotation and classification of BI-RADS assessment from radiology reports. Journal of Biomedical Informatics. 69. 177–187. 52 indexed citations
4.
Banerjee, Arna, John R. Boulet, Tanja Bekhuis, et al.. (2016). A Taxonomy of Delivery and Documentation Deviations During Delivery of High-Fidelity Simulations. Simulation in Healthcare The Journal of the Society for Simulation in Healthcare. 12(1). 1–8. 8 indexed citations
5.
Mowery, Danielle L., et al.. (2016). Knowledge Author: facilitating user-driven, domain content development to support clinical information extraction. Journal of Biomedical Semantics. 7(1). 42–42. 10 indexed citations
6.
Tseytlin, Eugene, et al.. (2016). NOBLE – Flexible concept recognition for large-scale biomedical natural language processing. BMC Bioinformatics. 17(1). 32–32. 72 indexed citations
7.
Tseytlin, Eugene, et al.. (2015). Building a gold standard to construct search filters: a case study with biomarkers for oral cancer. Journal of the Medical Library Association JMLA. 103(1). 22–30. 8 indexed citations
8.
Jacobson, Rebecca S., Michael J. Becich, Roni J. Bollag, et al.. (2015). A Federated Network for Translational Cancer Research Using Clinical Data and Biospecimens. Cancer Research. 75(24). 5194–5201. 27 indexed citations
9.
Bekhuis, Tanja, et al.. (2015). A prototype for a hybrid system to support systematic review teams: A case study of organ transplantation. PubMed. 160. 940–947. 1 indexed citations
10.
Linkov, Faina, Lora E. Burke, Robert P. Edwards, et al.. (2014). An exploratory investigation of links between changes in adipokines and quality of life in individuals undergoing weight loss interventions: Possible implications for cancer research. Gynecologic Oncology. 133(1). 67–72. 10 indexed citations
11.
Bekhuis, Tanja, et al.. (2014). Feature Engineering and a Proposed Decision-Support System for Systematic Reviewers of Medical Evidence. PLoS ONE. 9(1). e86277–e86277. 37 indexed citations
12.
Feyzi-Behnagh, Reza, et al.. (2013). Fostering Diagnostic Accuracy in a Medical Intelligent Tutoring System.. 1 indexed citations
13.
Cable, William, et al.. (2013). Digital Pathology Consultations—a New Era in Digital Imaging, Challenges and Practical Applications. Journal of Digital Imaging. 26(4). 668–677. 62 indexed citations
14.
Feyzi-Behnagh, Reza, et al.. (2013). Metacognitive scaffolds improve self-judgments of accuracy in a medical intelligent tutoring system. Instructional Science. 42(2). 159–181. 42 indexed citations
15.
Crowley, Rebecca S., Elizabeth Legowski, Olga Medvedeva, et al.. (2012). Automated detection of heuristics and biases among pathologists in a computer-based system. Advances in Health Sciences Education. 18(3). 343–363. 41 indexed citations
16.
Azevedo, Roger, Melissa Castine, Velma L. Payne, et al.. (2009). Factors affecting feeling-of-knowing in a medical intelligent tutoring system: the role of immediate feedback as a metacognitive scaffold. Advances in Health Sciences Education. 15(1). 9–30. 39 indexed citations
17.
Payne, Velma L., Olga Medvedeva, Elizabeth Legowski, et al.. (2009). Effect of a limited-enforcement intelligent tutoring system in dermatopathology on student errors, goals and solution paths. Artificial Intelligence in Medicine. 47(3). 175–197. 25 indexed citations
18.
Crowley, Rebecca S., et al.. (2007). Evaluation of an Intelligent Tutoring System in Pathology: Effects of External Representation on Performance Gains, Metacognition, and Acceptance. Journal of the American Medical Informatics Association. 14(2). 182–190. 34 indexed citations
19.
Tseytlin, Eugene, Elizabeth Legowski, D.M. Jukic, et al.. (2007). A natural language intelligent tutoring system for training pathologists: implementation and evaluation. Advances in Health Sciences Education. 13(5). 709–722. 36 indexed citations
20.
Fissell, Kate, et al.. (2003). Fiswidgets: A Graphical Computing Environment for Neuroimaging Analysis. Neuroinformatics. 1(1). 111–126. 62 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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