, Volume 85, Issue 2, pp 541–551 | Cite as

Intellectual structure of biomedical informatics reflected in scholarly events

  • Senator Jeong
  • Hong-Gee KimEmail author


The purpose of this paper was to analyze the intellectual structure of biomedical informatics reflected in scholarly events such as conferences, workshops, symposia, and seminars. As analysis variables, ‘call for paper topics’, ‘session titles’ and author keywords from biomedical informatics-related scholarly events, and the MeSH descriptors were combined. As analysis cases, the titles and abstracts of 12,536 papers presented at five medical informatics (MI) and six bioinformatics (BI) global scale scholarly event series during the years 1999–2008 were collected. Then, n-gram terms (MI = 6,958; BI = 5,436) from the paper corpus were extracted and the term co-occurrence network was analyzed. One hundred important topics for each medical informatics and bioinformatics were identified through the hub-authority metric, and their usage contexts were compared with the k-nearest neighbor measure. To research trends, newly popular topics by 2-year period units were observed. In the past 10 years the most important topic in MI has been “decision support”, while in BI “gene expression”. Though the two communities share several methodologies, according to our analysis, they do not use them in the same context. This evidence suggests that MI uses technologies for the improvement of productivity in clinical settings, while BI uses algorithms as its tools for scientific biological discovery. Though MI and BI are arguably separate research fields, their topics are increasingly intertwined, and the gap between the fields blurred, forming a broad informatics—namely biomedical informatics. Using scholarly events as data sources for domain analysis is the closest way to approximate the forefront of biomedical informatics.


Scholarly event Conference Biomedical informatics Intellectual structure Co-word analysis Social network analysis 



This study was supported by the Korean Research Foundation under the Grant KRF-2008-562-D00035 and KRF-2006-511-H00001.

Supplementary material

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Supplementary material 1 (PDF 2665 kb)


  1. Andrews, J. E. (2003). An author co-citation analysis of medical informatics. Journal of the Medical Library Association, 91(1), 47–56.Google Scholar
  2. Bansard, J. Y., Rebholz-Schuhmann, D., Cameron, G., Clark, D., van Mulligen, E., Beltrame, E., et al. (2007). Medical informatics and bioinformatics: A bibliometric study. IEEE Transaction on Information Technology in Biomedicine, 11(3), 237–243.CrossRefGoogle Scholar
  3. Callon, M., Courtial, J., & Laville, F. (1991). Co-word analysis as a tool for describing the network of interactions between basic and technological research: The case of polymer chemistry. Scientometrics, 22(1), 155–205.CrossRefGoogle Scholar
  4. Callon, M., Courtial, J.-P., Turner, W. A., & Bauin, S. (1983). From translations to problematic networks: An introduction to co-word analysis. Social Science Information, 22(2), 191–235.CrossRefGoogle Scholar
  5. David, F. (2005). Introduction to bioinformatics. Journal of the American Society for Information Science and Technology, 56(5), 440–446.CrossRefGoogle Scholar
  6. Fuller, S., Revere, D., Bugni, P., & Martin, G. (2004). A knowledgebase system to enhance scientific discovery: Telemakus. Biomedical Digital Libraries, 1(1), 2.CrossRefGoogle Scholar
  7. Garfield, E. (1986). Mapping the world of biomedical engineering: Alza lecture (1985). Annals of Biomedical Engineering, 14(2), 97–108.CrossRefGoogle Scholar
  8. Glänzel, W., Schlemmer, B., Schubert, A., & Thijs, B. (2006). Proceedings literature as additional data source for bibliometric analysis. Scientometrics, 68(3), 457–473.CrossRefGoogle Scholar
  9. Godin, B. (1998). Measuring knowledge flows between countries: The use of scientific meeting data. Scientometrics, 42(3), 313–323.CrossRefGoogle Scholar
  10. Greenes, R. A., & Shortliffe, E. H. (1990). Medical informatics. An emerging academic discipline and institutional priority. JAMA, 263(8), 1114–1120.CrossRefGoogle Scholar
  11. Hasman, A., & Haux, R. (2006). Modeling in biomedical informatics—an exploratory analysis (part 1). Methods of Information in Medicine, 45(6), 638–642.Google Scholar
  12. Hasman, A., & Haux, R. (2007). Modeling in biomedical informatics: An exploratory analysis part 2. International Journal of Medical Informatics, 76(2–3), 96–102.CrossRefGoogle Scholar
  13. Hasman, A., Haux, R., & Albert, A. (1996). A systematic view on medical informatics. Computer Methods and Programs in Biomedicine, 51(3), 131–139.CrossRefGoogle Scholar
  14. He, Q. (1999). Knowledge discovery through co-word analysis. Library Trends, 48(1), 133–159.Google Scholar
  15. Jeong, S., Lee, S., & Kim, H.-G. (2009). Are you an invited speaker? A bibliometric analysis of elite groups for scholarly events in bioinformatics. Journal of the American Society for Information Science and Technology, 60(6), 1118–1131.CrossRefGoogle Scholar
  16. Kleinberg, J. (1999). Authoritative sources in a hyperlinked environment. Journal of the ACM, 46(5), 604–632.zbMATHCrossRefMathSciNetGoogle Scholar
  17. Kranakis, E., & Leydesdorff, L. (1989). Teletraffic conferences: Studying a field of engineering science. Scientometrics, 15(5), 563–591.CrossRefGoogle Scholar
  18. Lisacek, F., Chichester, C., Kaplan, A., & Agnes, S. (2005). Discovering paradigm shift patterns in biomedical abstracts: Application to neurodegenerative diseases. Paper presented at the first international symposium on semantic mining in biomedicine.Google Scholar
  19. Lisée, C., & Larivière, V. (2008). Conference proceedings as a source of scientific information: A bibliometric analysis. Journal of the American Society for Information Science and Technology, 59(11), 1776–1784.CrossRefGoogle Scholar
  20. Luscombe, N. M., Greenbaum, D., & Gerstein, M. (2001). What is bioinformatics? A proposed definition and overview of the field. Methods of Information in Medicine, 40(4), 346–358.Google Scholar
  21. Malin, B., & Carley, K. (2007). A longitudinal social network analysis of the editorial boards of medical informatics and bioinformatics journals. Journal of American Medical Informatics Association, 14(3), 340–348.CrossRefGoogle Scholar
  22. Mane, K. K., & Börner, K. (2004). Mapping topics and topic bursts in PNAS. Proceedings of the National Academy of Sciences of the United States of America, 101, 5287–5290.CrossRefGoogle Scholar
  23. Mann, G. S., Mimno, D., & McCallum, A. (2006). Bibliometric impact measures leveraging topic analysis. Paper presented at the 6th ACM/IEEE-CS joint conference on digital libraries.Google Scholar
  24. Maojo, V., & Kulikowski, C. A. (2003). Bioinformatics and medical informatics: Collaboration on the road to genomic medicine? Journal of American Medical Informatics Association, 10(6), 515–522.CrossRefGoogle Scholar
  25. Martens, B., & Saretzki, T. (1993). Conferences and courses on biotechnology. Describing scientific communication by exploratory methods. Scientometrics, 27(3), 237–260.CrossRefGoogle Scholar
  26. Martens, B., & Saretzki, T. (1994). Quantitative analysis of thematic structures in the field of biotechnology: A study on the basis of conference data. Scientometrics, 30(1), 117–128.CrossRefGoogle Scholar
  27. Matsuo, Y., Tomobe, H., Hasida, K., & Ishizuka, M. (2003). Mining social network of conference participants from the web. In Proceedings of the international conference on web intelligence (pp. 190–194).Google Scholar
  28. McCain, K. W. (1991). Core journal networks and cocitation maps: New bibliometric tools for serials research and management. Library Quarterly, 61(3), 311–336.CrossRefGoogle Scholar
  29. McCain, K. W. (1995). Biotechnology in context: A database-filtering approach to identifying core and productive non-core journals supporting multidisciplinary R & D. Journal of the American Society for Information Science, 46(4), 306–317.CrossRefGoogle Scholar
  30. McCain, K. W., & Silverstein, S. M. (2007). Tracing persistent highly visible research themes in medical informatics. Paper presented at the 2006 AMIA spring congress. Retrieved from
  31. Morris, T. A. (2000). Structural relationships within medical informatics. Paper presented at the AMIA 2000 annual symposium.Google Scholar
  32. Morris, T. A. (2001). Structural relationships within medical informatics: A classification/indexing co-occurrence analysis. Philadelphia: Drexel University.Google Scholar
  33. Morris, T. A., & McCain, K. W. (1998). The structure of medical informatics journal literature. Journal of the American Medical Informatics Association, 5(5), 448–466.Google Scholar
  34. Noyons, E. (2001). Bibliometric mapping of science in a policy context. Scientometrics, 50(1), 83–98.CrossRefGoogle Scholar
  35. Pickens, J., & MacFarlane, A. (2006). Term context models for information retrieval. Paper presented at the 15th ACM international conference on information and knowledge management.Google Scholar
  36. Rebholz-Schuhman, D., Cameron, G., Clark, D., van Mulligen, E., Coatrieux, J.-L., Del Hoyo Barbolla, E., et al. (2007). SYMBIOmatics: Synergies in medical informatics and bioinformatics—exploring current scientific literature for emerging topics. BMC Bioinformatics, 8(Suppl 1), S18.CrossRefGoogle Scholar
  37. Rip, A., & Courtial, J. (1984). Co-word maps of biotechnology: An example of cognitive scientometrics. Scientometrics, 6(6), 381–400.CrossRefGoogle Scholar
  38. Söderqvist, T., & Silverstein, A. M. (1994). Participation in scientific meetings: A new prosopographical approach to the disciplinary history of science—the case of immunology 1951–72. Social Studies of Science, 24, 513–548.CrossRefGoogle Scholar
  39. Stegmann, J., & Grohmann, G. (2003). Hypothesis generation guided by co-word clustering. Scientometrics, 56(1), 111–135.CrossRefGoogle Scholar
  40. Stegmann, J., & Grohmann, G. (2005). Transitive text mining for information extraction and hypothesis generation. Retrieved July 10, 2008, from
  41. Swanson, D. (1986a). Fish oil, Raynaud’s syndrome, and undiscovered public knowledge. Perspectives in Biology and Medicine, 30(1), 7–18.Google Scholar
  42. Swanson, D. (1986b). Undiscovered public knowledge. Library Quarterly, 56(2), 103–118.CrossRefMathSciNetGoogle Scholar
  43. Swanson, D. (1988). Migraine and magnesium: Eleven neglected connections. Perspectives in Biology and Medicine, 31(4), 526–557.Google Scholar
  44. Synnestvedt, M. B., & Chen, C. (2003). Visualizing AMIA: A medical informatics knowledge domain analysis. Paper presented at the AMIA annual symposium. Retrieved from
  45. Synnestvedt, M. B., Chen, C., & Holmes, J. H. (2005). Visual exploration of landmarks and trends in the medical informatics literature. Paper presented at the 2005 AMIA annual symposium. Retrieved from

Copyright information

© Akadémiai Kiadó, Budapest, Hungary 2010

Authors and Affiliations

  1. 1.Biomedical Knowledge Engineering LaboratorySeoul National UniversitySeoulKorea

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