Alexandra Urman

1.2k total citations
17 papers, 136 citations indexed

About

Alexandra Urman is a scholar working on Molecular Biology, Health Informatics and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Alexandra Urman has authored 17 papers receiving a total of 136 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Molecular Biology, 4 papers in Health Informatics and 4 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Alexandra Urman's work include Biomedical Text Mining and Ontologies (5 papers), Radiomics and Machine Learning in Medical Imaging (4 papers) and Artificial Intelligence in Healthcare and Education (4 papers). Alexandra Urman is often cited by papers focused on Biomedical Text Mining and Ontologies (5 papers), Radiomics and Machine Learning in Medical Imaging (4 papers) and Artificial Intelligence in Healthcare and Education (4 papers). Alexandra Urman collaborates with scholars based in United States, Thailand and Norway. Alexandra Urman's co-authors include Irene Dankwa‐Mullan, H. Dean Hosgood, Kyu Rhee, Andrew D. Norden, Tufia C. Haddad, J. Helgeson, Matthew P. Goetz, Melissa Rammage, Melissa L. Johnson and Vincent A. Miller and has published in prestigious journals such as Journal of Clinical Oncology, Frontiers of Medicine and JCO Clinical Cancer Informatics.

In The Last Decade

Alexandra Urman

17 papers receiving 133 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Alexandra Urman United States 8 43 43 41 33 31 17 136
Mehr Kashyap United States 7 74 1.7× 47 1.1× 90 2.2× 32 1.0× 21 0.7× 11 235
Aymen Elfiky United States 3 33 0.8× 36 0.8× 26 0.6× 27 0.8× 8 0.3× 4 117
Zhihang Chen China 5 27 0.6× 39 0.9× 41 1.0× 51 1.5× 42 1.4× 8 211
Melissa Estévez United States 6 39 0.9× 24 0.6× 15 0.4× 45 1.4× 15 0.5× 18 142
Marius Geantă Romania 6 29 0.7× 30 0.7× 49 1.2× 12 0.4× 16 0.5× 15 148
Satvik Tripathi United States 8 86 2.0× 74 1.7× 84 2.0× 18 0.5× 29 0.9× 24 216
Melissa Rammage United States 7 51 1.2× 42 1.0× 69 1.7× 23 0.7× 21 0.7× 9 275
Amudha Thangavelu United Kingdom 10 40 0.9× 53 1.2× 20 0.5× 54 1.6× 14 0.5× 20 230
Shang Xue China 5 53 1.2× 48 1.1× 58 1.4× 16 0.5× 48 1.5× 9 238
Martín-José Sepúlveda United States 5 97 2.3× 106 2.5× 109 2.7× 35 1.1× 22 0.7× 9 281

Countries citing papers authored by Alexandra Urman

Since Specialization
Citations

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

Fields of papers citing papers by Alexandra Urman

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Alexandra Urman

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

All Works

17 of 17 papers shown
1.
Haney, Nora M., Alexandra Urman, Tayab Waseem, Yvonne Cagle, & Jose Morey. (2020). AI’s role in deep space. 3. 11–11. 3 indexed citations
2.
Urman, Alexandra, et al.. (2018). ChemoQuant: A novel approach to address the current chemotherapy supply problems in low resource countries.. Journal of Clinical Oncology. 36(15_suppl). e18593–e18593. 1 indexed citations
3.
Helgeson, J., Melissa Rammage, Alexandra Urman, et al.. (2018). Clinical performance pilot using cognitive computing for clinical trial matching at Mayo Clinic.. Journal of Clinical Oncology. 36(15_suppl). e18598–e18598. 16 indexed citations
4.
Umsawasdi, Theera, et al.. (2018). Concordance assessment of a clinical decision support software in patients with solid tumors.. Journal of Clinical Oncology. 36(15_suppl). e18584–e18584. 4 indexed citations
5.
Haddad, Tufia C., et al.. (2018). Impact of a cognitive computing clinical trial matching system in an ambulatory oncology practice.. Journal of Clinical Oncology. 36(15_suppl). 6550–6550. 19 indexed citations
6.
Cantalapiedra, Diego, Ignacio Durán, Oriol Calvete, et al.. (2018). The application of cognitive computing technology in genomics in precision oncological medicine: The Sistemas Genomicos Experience.. Journal of Clinical Oncology. 36(15_suppl). e18544–e18544. 3 indexed citations
7.
Urman, Alexandra, et al.. (2018). Harnessing AI for health equity in oncology research and practice.. Journal of Clinical Oncology. 36(30_suppl). 67–67. 3 indexed citations
8.
Chen, Peng‐Ju, Tingting Sun, Tianle Li, et al.. (2018). Can AI technology augment tumor board treatment decisions for stage II colon cancer care?. Journal of Clinical Oncology. 36(15_suppl). e18582–e18582. 3 indexed citations
9.
Norden, Andrew D., Irene Dankwa‐Mullan, Alexandra Urman, Fernando Suárez, & Kyu Rhee. (2018). Realizing the Promise of Cognitive Computing in Cancer Care: Ushering in a New Era. JCO Clinical Cancer Informatics. 2(2). 1–6. 6 indexed citations
10.
Suárez, Fernando, et al.. (2018). IBM Watson Evidence Service (WES): A system for retrieval, summation and insight generation of relevant clinical evidence for personalized oncology.. Journal of Clinical Oncology. 36(15_suppl). e18588–e18588. 1 indexed citations
11.
Melendez, Fidel David Huitzil, et al.. (2017). Cognitive computing in oncology: A qualitative assessment of IBM Watson for Oncology in Mexico.. Journal of Clinical Oncology. 35(15_suppl). e18166–e18166. 5 indexed citations
12.
Beck, J. Thaddeus, Irene Dankwa‐Mullan, Alexandra Urman, et al.. (2017). Cognitive technology addressing optimal cancer clinical trial matching and protocol feasibility in a community cancer practice.. Journal of Clinical Oncology. 35(15_suppl). 6501–6501. 9 indexed citations
13.
Norden, Andrew D., et al.. (2017). Concordance assessment of a cognitive computing system in Thailand.. Journal of Clinical Oncology. 35(15_suppl). 6589–6589. 16 indexed citations
14.
Baek, Jeong‐Heum, Alexandra Urman, Young Saing Kim, et al.. (2017). Use of a cognitive computing system for treatment of colon and gastric cancer in South Korea.. Journal of Clinical Oncology. 35(15_suppl). e18204–e18204. 8 indexed citations
15.
Urman, Alexandra & H. Dean Hosgood. (2016). Curbing the burden of lung cancer. Frontiers of Medicine. 10(2). 228–232. 10 indexed citations
16.
Urman, Alexandra, et al.. (2016). Burden of Lung Cancer and Associated Risk Factors in Africa by Region. Journal of Pulmonary & Respiratory Medicine. 6(3). 12 indexed citations
17.
Johnson, Melissa L., Eric Hart, Alfred Rademaker, et al.. (2013). A phase II study of HSP90 inhibitor AUY922 and erlotinib (E) for patients (pts) with EGFR-mutant lung cancer and acquired resistance (AR) to EGFR tyrosine kinase inhibitors (EGFR TKIs).. Journal of Clinical Oncology. 31(15_suppl). 8036–8036. 17 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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