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논문KCI 등재

Application of Social Network Analysis to Health Care Sectors

이영성|안지영|장해란
대한의료정보학회 (The Korean Society Of Medical Informatics)|2012.03|Healthcare Informatics Research|제18권 제1호|pp. 44-56

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참고문헌

키워드

BibliometricsMedical Subject HeadingsKnowledge BasesPeriodicals as TopicNational Institutes of Health

초록

Objectives: This study aimed to examine the feasibility of social network analysis as a valuable research tool for indicating a change in research topics in health care and medicine. Methods: Papers used in the analysis were collected from the PubMed database at the National Library of Medicine. After limiting the search to papers affiliated with the National Institutes of Health, 27,125 papers were selected for the analysis. From these papers, the top 100 non-duplicate and most studied Medical Subject Heading terms were extracted. NetMiner V.3 was used for analysis. Weighted degree centrality was applied to the analysis to compare the trends in the change of research topics. Changes in the core keywords were observed for the entire group and in three-year intervals. Results: The core keyword with the highest centrality value was “Risk Factor,” followed by “Molecular Sequence Data,” “Neoplasms,” “Signal Transduction,” “Brain,” and “Amino Acid Sequence.” Core keywords varied between time intervals, changing from “Molecular Sequence Data” to “Risk Factors” over time. “Risk Factors” was added as a new keyword and its social network was expanded. The slope of the keywords also varied over time: “Molecular Sequence Data,” with a high centrality value, had a decreasing slope at certain intervals, whereas “SNP,” with a low centrality value, had an increasing slope at certain intervals. Conclusions: The social network analysis method is useful for tracking changes in research topics over time. Further research should be conducted to confirm the usefulness of this method in health care and medicine.

Abstract

Objectives: This study aimed to examine the feasibility of social network analysis as a valuable research tool for indicating a change in research topics in health care and medicine. Methods: Papers used in the analysis were collected from the PubMed database at the National Library of Medicine. After limiting the search to papers affiliated with the National Institutes of Health, 27,125 papers were selected for the analysis. From these papers, the top 100 non-duplicate and most studied Medical Subject Heading terms were extracted. NetMiner V.3 was used for analysis. Weighted degree centrality was applied to the analysis to compare the trends in the change of research topics. Changes in the core keywords were observed for the entire group and in three-year intervals. Results: The core keyword with the highest centrality value was “Risk Factor,” followed by “Molecular Sequence Data,” “Neoplasms,” “Signal Transduction,” “Brain,” and “Amino Acid Sequence.” Core keywords varied between time intervals, changing from “Molecular Sequence Data” to “Risk Factors” over time. “Risk Factors” was added as a new keyword and its social network was expanded. The slope of the keywords also varied over time: “Molecular Sequence Data,” with a high centrality value, had a decreasing slope at certain intervals, whereas “SNP,” with a low centrality value, had an increasing slope at certain intervals. Conclusions: The social network analysis method is useful for tracking changes in research topics over time. Further research should be conducted to confirm the usefulness of this method in health care and medicine.

참고문헌 (52)

  1. Korean Medical Research Report 2006, "National Academy of Medicine of Korea.", National Academy of Medicine of Korea, 15~ (2007)
  2. Synnestvedt MB., "CiteSpace II : visualization and knowledge discovery in bibliographic databases", AMIA Annu Symp Proc, 2005, pp.724~728 (2005)
  3. Bernstam EV., "Using citation data to improve retrieval from MEDLINE", J Am Med Inform Assoc, 13, pp.96~105 (2006)
  4. 장혜란, "국내의학자가 국내외에 발표한 의학논문 현황 분석 - 1960년부터 2008년까지 -", 한국문헌정보학회지, 45(3), pp.259~277 (2011), 한국문헌정보학회
  5. He Q., "Knowledge discovery through co-word analysis", Libr Trends, 48, pp.133~159 (1999)
  6. Mane KK., "Mapping topics and topic bursts in PNAS", Proc Natl Acad Sci U S A, 101(Suppl 1), pp.5287~5290 (2004)
  7. 정민수, "국내 예방의학 분야의 공저자 · 핵심어 네트워크와 군집 양상-대한예방의학회지(1991~2006) 게재논문의 분석-", 예방의학회지, 41(1), pp.1~9 (2008), 대한예방의학회
  8. Jung M., "Academic research activities and their coauthor and keyword network in epidemiology fields : analysis of papers in the Korean Journal of Epidemiology, 1991-2006", Korean J Epidemiol, 30, pp.60~72 (2008)
  9. 강진오, "Analysis of Scientific Publication Networks among Medical Schools in Korea", Healthcare Informatics Research, 16(2), pp.100~119 (2010), 대한의료정보학회
  10. Senator Jeong, "Knowledge Structure of Korean Medical Informatics: A Social Network Analysis of Articles in Journal and Proceedings", Healthcare Informatics Research, 16(1), pp.52~59 (2010), 대한의료정보학회
  11. 이수경, "한국 간호학 연구주제의 사회 연결망 분석", Journal of Korean Academy of Nursing, 41(5), pp.623~632 (2011), 한국간호과학회
  12. Lee YS., "Research network analysis for the national science knowledge map. Seoul" (2010), Korea Research Council of Fundamental Science and Technology
  13. Sohn DK., "Generation and analysis of the research network for colorectal neoplasms dissertation", pp.2011
  14. Price DD., "The pattern of bibliographic references indicates the nature of the scientific research front", Science, 149, pp.510~515 (1965)
  15. Small H., "Co-citation in the scientific literature : a new measure of the relationship between two documents", J Am Soc Inf Sci, 24, pp.265~269 (1973)
  16. Chen C., "Searching for intellectual turning points: progressive knowledge domain visualization", Proc Natl Acad Sci U S A, 101(Suppl 1), pp.5303~5310 (2004)
  17. Callon M., "Co-word analysis as a tool for describing the network of interactions between basic and technological research : the case of polymer chemistry", Scientometrics, 22, pp.155~205 (1991)
  18. 정영미, "Mapping Knowledge Structure of Science and Technology Based on University Research Domain Analysis", 정보관리학회지, 26(2), pp.195~210 (2009), 한국정보관리학회
  19. Mane KK., "Mapping topics and topic bursts in PNAS", Proc Natl Acad Sci U S A, 101(Suppl 1), pp.5287~5290 (2004)
  20. Garfield E., "The history and meaning of the journal impact factor", JAMA, 295, pp.90~93 (2006)
  21. Ding Y., "Bibliometric cartography of information retrieval research by using co-word analysis", Inf Process Manag, 37, pp.817~842 (2001)
  22. Wang X., "The "Small-World" characteristic of author co-words network", Proceedings of the International Conference on WICOM, pp.3717~3720 (2007)
  23. Van Raan AF., "The neural net of neural network research", Scientometrics, 26, pp.169~192 (1993)
  24. Zavaglia M., "A dynamical pattern recognition model of gamma activity in auditory cortex", Neural Netw, 28, pp.1~14 (2012)
  25. Coulter N., "Software engineering as seen through its research literature : a study in co-word analysis", J Am Soc Inf Sci, 49, pp.1206~1223 (1998)
  26. An evolutionary perspective of software engineering research through co-word analysis, "Coulter N.", Carnegie- Mellon University Software Engineering Institute, ~, pp.Technical report no.: CMU (1996), SEI-95-TR-019, ESCTR- 95-019
  27. Rip A., "Co-word maps of biotechnology : an example of cognitive", Scientometrics, 6, pp.381~400 (1984)
  28. Albert A., "Scientific and technological performance evaluation of the Spanish Council for Scientific Research(CSIC)in the field of Biotechnology", Scientometrics, 70, pp.41~51 (2007)
  29. Zheng HC., "Mapping the history and current situation of research on John Cunningham virus-a bibliometric analysis", BMC Infect Dis, 9, pp.28~ (2009)
  30. Kim SY., "From MeSH indexed to retrieval" (2008), Korean Medical Library Association
  31. 권애경, "MeSH를 확장한 한국보건의료정보분야 주제어 연구 : 대한의료정보학회지를 중심으로", Healthcare Informatics Research, 8(4), pp.1~11 (2002), 대한의료정보학회
  32. Courtial JP., "A coword analysis of scientometrics", Scientometrics, 31, pp.251~260 (1994)
  33. 이우형, "Co-word Analysis을 통한 신기술 분야 도식화 방법에 관한 연구", 경영과학, 23(3), pp.77~94 (2006), 한국경영과학회
  34. 김판준, "연구 영역 분석을 위한 디스크립터 프로파일링에 관한 연구", 정보관리학회지, 24(4), pp.285~303 (2007), 한국정보관리학회
  35. Sohn DW., "Social network analysis. 4th ed", pp.1~21 (2010), Kyungmun Publisher
  36. Mika P., "Social networks and the semantic web", pp.33~52 (2007), Springer
  37. Jung BS., "A study on the knowledge map of the computer engineering field by using a research paper database", Annu Proc Korea Inst Process Soc, 18, pp.1460~1462 (2011)
  38. Polanco X., "Co-word analysis revisited: modelling coword clusters in terms of graph theory", Proceedings of the 10th International Conference on Scientometrics and Informetrics, pp.662~663 (2005)
  39. Kim YH., "Social network analysis. 2nd ed" (2007), Pakyoungsa
  41. Faust K., "Comparing social networks : size, density, and local structure", Metodoloski zvezki, 3, pp.185~216 (2006)
  42. Borgatti SP., "Centrality and network flow", Soc Networks, 27, pp.55~71 (2005)
  43. Freeman LC., "Centrality in social networks conceptual clarification", Soc Networks, 1, pp.215~239 (1979)
  44. 이재윤, "계량서지적 네트워크 분석을 위한 중심성 척도에 관한 연구", 한국문헌정보학회지, 40(3), pp.191~214 (2006), 한국문헌정보학회
  45. Opsahla T., "Node centrality in weighted networks : generalizing degree and shortest paths", Soc Networks, 32, pp.245~251 (2010)
  46. Adamic LA., "Search in power-law networks", Phys Rev E, 64, pp.046135~ (2001)
  47. Yeo WD., "Identification of emerging research at the national level : scientometric approach using Scopus", J Inf Manag, 39, pp.95~113 (2008)
  48. Boyack KW., "Co-citation analysis., bibliographic coupling., and direct citation : which citation approach represents the research front most accurately?", J Am Soc Inf Sci Tech, 61, pp.2389~2404 (2010)
  49. 허선, "국내외 의학논문 데이터베이스 현황", 대한의사협회지, 53(8), pp.659~667 (2010), 대한의사협회
  50. Stegmann J., "Hypothesis generation guided by co-word clustering", Scientometrics, 56, pp.111~135 (2003)
  51. An XY., "Co-word analysis of the trends in stem cells field based on subject heading weighting", Scientometrics, 88, pp.133~144 (2011)
  52. Barrat A., "The architecture of complex weighted networks", Proc Natl Acad Sci U S A, 101, pp.3747~3752 (2004)

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