György Surján

640 total citations
33 papers, 417 citations indexed

About

György Surján is a scholar working on Molecular Biology, Artificial Intelligence and Health Information Management. According to data from OpenAlex, György Surján has authored 33 papers receiving a total of 417 indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Molecular Biology, 12 papers in Artificial Intelligence and 7 papers in Health Information Management. Recurrent topics in György Surján's work include Biomedical Text Mining and Ontologies (17 papers), Semantic Web and Ontologies (7 papers) and SARS-CoV-2 and COVID-19 Research (5 papers). György Surján is often cited by papers focused on Biomedical Text Mining and Ontologies (17 papers), Semantic Web and Ontologies (7 papers) and SARS-CoV-2 and COVID-19 Research (5 papers). György Surján collaborates with scholars based in Hungary, France and Switzerland. György Surján's co-authors include Zoltán Kiss, G Molnár, Zoltán Vokó, Tamás Vicsek, Bernadett Pályi, Róbert Herczeg, Attila Miseta, Anna Horváth, Péter Pollner and Attila Gyenesei and has published in prestigious journals such as Frontiers in Immunology, Clinical Microbiology and Infection and Computers in Biology and Medicine.

In The Last Decade

György Surján

30 papers receiving 402 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
György Surján Hungary	11	141	110	97	82	76	33	417
Vikram Anand United States	12	72 0.5×	166 1.5×	122 1.3×	62 0.8×	28 0.4×	24	734
Brett Trusko United States	9	29 0.2×	53 0.5×	57 0.6×	48 0.6×	34 0.4×	27	361
Venky Soundararajan United States	17	600 4.3×	123 1.1×	189 1.9×	22 0.3×	12 0.2×	42	834
Sumit K. Shah United States	18	112 0.8×	62 0.6×	213 2.2×	86 1.0×	3 0.0×	42	876
Anya Okhmatovskaia Canada	9	24 0.2×	33 0.3×	25 0.3×	52 0.6×	26 0.3×	27	305
Scott K. Winiecki United States	12	47 0.3×	39 0.4×	53 0.5×	22 0.3×	12 0.2×	21	343
Jiansheng Zhu China	14	156 1.1×	136 1.2×	156 1.6×	32 0.4×	2 0.0×	49	513
Jesutofunmi A. Omiye United States	10	36 0.3×	36 0.3×	15 0.2×	185 2.3×	57 0.8×	17	598
Lindsay M. Wong United States	5	34 0.2×	47 0.4×	8 0.1×	67 0.8×	53 0.7×	8	320
Shannan N. Rich United States	9	60 0.4×	48 0.4×	6 0.1×	98 1.2×	17 0.2×	28	388

Countries citing papers authored by György Surján

Since Specialization

Citations

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

Fields of papers citing papers by György Surján

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of György Surján

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

Müller, Veronika, István Vályi‐Nagy, Alexandra Nagy, et al.. (2022). Booster Vaccination Decreases 28-Day All-Cause Mortality of the Elderly Hospitalized Due to SARS-CoV-2 Delta Variant. Vaccines. 10(7). 986–986. 10 indexed citations

Kiss, Zoltán, György Surján, G Molnár, et al.. (2022). Nationwide Effectiveness of First and Second SARS-CoV2 Booster Vaccines During the Delta and Omicron Pandemic Waves in Hungary (HUN-VE 2 Study). Frontiers in Immunology. 13. 905585–905585. 30 indexed citations

Vokó, Zoltán, Zoltán Kiss, György Surján, et al.. (2022). Effectiveness and Waning of Protection With Different SARS-CoV-2 Primary and Booster Vaccines During the Delta Pandemic Wave in 2021 in Hungary (HUN-VE 3 Study). Frontiers in Immunology. 13. 919408–919408. 31 indexed citations

Vokó, Zoltán, Zoltán Kiss, György Surján, et al.. (2021). Nationwide effectiveness of five SARS-CoV-2 vaccines in Hungary—the HUN-VE study. Clinical Microbiology and Infection. 28(3). 398–404. 85 indexed citations

Horváth, Anna, et al.. (2016). A nationwide study of the epidemiology of relapsing polychondritis. Clinical Epidemiology. Volume 8. 211–230. 34 indexed citations

Ammenwerth, Elske, Petra Knaup, Christian Lovis, et al.. (2011). Biomedical Informatics – A Confluence of Disciplines?. Methods of Information in Medicine. 50(6). 508–524. 16 indexed citations

Surján, György, et al.. (2008). Design principles of DOLCE-based formal representation of ICD10.. PubMed. 136. 821–6. 5 indexed citations

Surján, György, et al.. (2008). Ontological analysis of SNOMED CT. BMC Medical Informatics and Decision Making. 8(S1). S8–S8. 31 indexed citations

Surján, György, et al.. (2005). A pilot ontological model of public health indicators. Computers in Biology and Medicine. 36(7-8). 802–816. 5 indexed citations

10.

Surján, György, et al.. (2004). Conceptual Framework of Health Indicators: The IDA Model. Studies in health technology and informatics. 107(Pt 2). 1230–4. 3 indexed citations

11.

Surján, György, et al.. (2003). Using n-gram method in the decomposition of compound medical diagnoses. International Journal of Medical Informatics. 70(2-3). 229–236. 5 indexed citations

12.

Surján, György, et al.. (2003). Semi-automatic classification of clinical diagnoses with hybrid approach. 347–352. 1 indexed citations

13.

Surján, György, et al.. (2003). About the language of Hungarian discharge reports.. PubMed. 95. 869–73. 4 indexed citations

14.

Surján, György, Rolf Engelbrecht, & Peter McNair. (2002). Health Data in the Information Society : Proceedings of MIE2002. Site cant be reached. 4 indexed citations

15.

Surján, György, et al.. (2001). Indexing of medical diagnoses by word affinity method.. PubMed. 84(Pt 1). 276–9. 1 indexed citations

16.

Surján, György. (1999). Questions on validity of International Classification of Diseases-coded diagnoses. International Journal of Medical Informatics. 54(2). 77–95. 77 indexed citations

17.

Ceusters, Werner, et al.. (1997). TSMI: a CEN/TC251 standard for time specific problems in healthcare informatics and telematics. International Journal of Medical Informatics. 46(2). 87–101. 11 indexed citations

18.

Surján, György, et al.. (1996). Theoretical considerations on medical concept representation. Medical Informatics. 21(1). 61–68. 1 indexed citations

19.

Surján, György, et al.. (1993). Towards a quantitative approach of medical information. Part 2. Comparative measurement of medical information objects. Medical Informatics. 18(4). 347–354.

20.

Surján, György, et al.. (1993). Towards a quantitative approach of medical information. Part 1. Measures of a multidimensional medical information space. Medical Informatics. 18(4). 339–346. 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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