Vasa Ćurčin

5.2k total citations
140 papers, 3.0k citations indexed

About

Vasa Ćurčin is a scholar working on Epidemiology, Artificial Intelligence and Health Information Management. According to data from OpenAlex, Vasa Ćurčin has authored 140 papers receiving a total of 3.0k indexed citations (citations by other indexed papers that have themselves been cited), including 28 papers in Epidemiology, 28 papers in Artificial Intelligence and 27 papers in Health Information Management. Recurrent topics in Vasa Ćurčin's work include Scientific Computing and Data Management (24 papers), Electronic Health Records Systems (21 papers) and Biomedical Text Mining and Ontologies (18 papers). Vasa Ćurčin is often cited by papers focused on Scientific Computing and Data Management (24 papers), Electronic Health Records Systems (21 papers) and Biomedical Text Mining and Ontologies (18 papers). Vasa Ćurčin collaborates with scholars based in United Kingdom, Sweden and Spain. Vasa Ćurčin's co-authors include Azeem Majeed, Brendan Delaney, Akkapon Wongkoblap, Miguel A. Vadillo, Mariam Molokhia, Christopher Millett, Moustafa Ghanem, Robert Verheij, Mark McGilchrist and Kamlesh Khunti and has published in prestigious journals such as The Lancet, SHILAP Revista de lepidopterología and Gastroenterology.

In The Last Decade

Vasa Ćurčin

130 papers receiving 2.9k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Vasa Ćurčin United Kingdom 29 591 496 441 438 403 140 3.0k
Abel Kho United States 29 480 0.8× 608 1.2× 641 1.5× 530 1.2× 215 0.5× 126 3.1k
Rae Woong Park South Korea 28 408 0.7× 763 1.5× 628 1.4× 220 0.5× 242 0.6× 191 3.7k
Hee Hwang South Korea 30 324 0.5× 183 0.4× 465 1.1× 512 1.2× 250 0.6× 202 3.8k
Peter R. Rijnbeek Netherlands 31 931 1.6× 818 1.6× 487 1.1× 158 0.4× 358 0.9× 120 4.3k
Justin Starren United States 37 572 1.0× 501 1.0× 614 1.4× 1.2k 2.8× 639 1.6× 137 4.7k
David A. Hanauer United States 34 319 0.5× 495 1.0× 560 1.3× 983 2.2× 169 0.4× 111 3.7k
Christian Reich United States 21 292 0.5× 617 1.2× 575 1.3× 160 0.4× 152 0.4× 57 3.0k
Henry C. Chueh United States 20 360 0.6× 314 0.6× 265 0.6× 343 0.8× 237 0.6× 49 1.7k
Michael G. Kahn United States 33 439 0.7× 678 1.4× 455 1.0× 443 1.0× 94 0.2× 134 3.4k
Johan van der Lei Netherlands 40 1.5k 2.5× 549 1.1× 862 2.0× 714 1.6× 503 1.2× 174 6.5k

Countries citing papers authored by Vasa Ćurčin

Since Specialization
Citations

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

Fields of papers citing papers by Vasa Ćurčin

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Vasa Ćurčin

This figure shows the co-authorship network connecting the top 25 collaborators of Vasa Ćurčin. A scholar is included among the top collaborators of Vasa Ćurčin 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 Vasa Ćurčin. Vasa Ćurčin 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.
Arina, Pietro, Nicholas Tetlow, Robert Stephens, et al.. (2025). Mortality prediction after major surgery in a mixed population through machine learning: a multi‐objective symbolic regression approach. Anaesthesia. 80(5). 551–560.
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Huo, Zhiqiang, Timothy Neate, David Wyatt, et al.. (2024). Co-Designing a User-Centred Digital Portal to Support Health-Related Self-Management for Stroke Survivors. 418–425.
4.
Krentz, Andrew J., et al.. (2024). Machine learning to optimise statin therapy using real-world primary care outcomes: can statin doses be reduced in some patients?. Atherosclerosis. 395. 117927–117927. 1 indexed citations
5.
Ćurčin, Vasa, et al.. (2024). Profiling Generalized Anxiety Disorder on Social Networks: Content and Behavior Analysis. Journal of Medical Internet Research. 27. e53399–e53399.
6.
Čyras, Kristijonas, Oana Cocarascu, Francis Ruiz, et al.. (2023). ROAD2H: Development and evaluation of an open‐source explainable artificial intelligence approach for managing co‐morbidity and clinical guidelines. Learning Health Systems. 8(2). e10391–e10391. 3 indexed citations
7.
Knoche, Hendrik, Alfie Abdul‐Rahman, Leigh Clark, et al.. (2023). Identifying Challenges and Opportunities for Intelligent Data-Driven Health Interfaces to Support Ongoing Care. ORCA Online Research @Cardiff (Cardiff University). 1–7. 1 indexed citations
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Chapman, Martin, Shahzad Mumtaz, Luke V. Rasmussen, et al.. (2021). Desiderata for the development of next-generation electronic health record phenotype libraries. GigaScience. 10(9). 16 indexed citations
12.
Wongkoblap, Akkapon, Miguel A. Vadillo, & Vasa Ćurčin. (2021). Deep Learning With Anaphora Resolution for the Detection of Tweeters With Depression: Algorithm Development and Validation Study. JMIR Mental Health. 8(8). e19824–e19824. 28 indexed citations
13.
Gulliford, Martin, Judith Charlton, Olga Boiko, et al.. (2021). Safety of reducing antibiotic prescribing in primary care: a mixed-methods study. SHILAP Revista de lepidopterología. 9(9). 1–126. 9 indexed citations
14.
Tapuria, Archana, Dipak Kalra, & Vasa Ćurčin. (2020). Feasibility of Using EN 13606 Clinical Archetypes for Defining Computable Phenotypes. Studies in health technology and informatics. 270. 228–232. 1 indexed citations
15.
Chapman, Martin, Mark Ashworth, Vasa Ćurčin, et al.. (2019). Computational Argumentation-based Clinical Decision Support. Research Portal (King's College London). 2345–2347. 3 indexed citations
16.
Ford, Elizabeth, Andy Boyd, Juliana Bowles, et al.. (2019). Our data, our society, our health: A vision for inclusive and transparent health data science in the United Kingdom and beyond. Learning Health Systems. 3(3). e10191–e10191. 39 indexed citations
17.
Porat, Talya, Iain Marshall, Euan Sadler, et al.. (2019). Collaborative design of a decision aid for stroke survivors with multimorbidity: a qualitative study in the UK engaging key stakeholders. BMJ Open. 9(8). e030385–e030385. 22 indexed citations
18.
Wongkoblap, Akkapon, Miguel A. Vadillo, & Vasa Ćurčin. (2017). Researching Mental Health Disorders in the Era of Social Media: Systematic Review. Journal of Medical Internet Research. 19(6). e228–e228. 168 indexed citations
19.
Sadler, Euan, Talya Porat, Iain Marshall, et al.. (2017). Shaping innovations in long-term care for stroke survivors with multimorbidity through stakeholder engagement. PLoS ONE. 12(5). e0177102–e0177102. 20 indexed citations
20.
Liu, Jian Guo, et al.. (2006). Achievements and Experiences from a Grid-Based Earthquake Analysis and Modelling Study. 35–35. 2 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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