Visualization of health-subject analysis based on query term co-occurrences

Journal of the American Society for Information Science and Technology

Volumn 59, Issue 12, 2008, Pages 1933-1947

  Zhang, Jin ^a   Wolfram, Dietmar ^a   Wang, Peiling ^b   Hong, Yi ^c   Gillis, Rick ^c  

^a UNIVERSITY OF WISCONSIN MILWAUKEE   (United States)

^b UNIVERSITY OF TENNESSEE   (United States)

^c MEDICAL COLLEGE OF WISCONSIN   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

FUTURE DIRECTIONS; HEALTH INFORMATION SYSTEMS; MEDICAL PROFESSIONALS; MEDICAL TERMINOLOGIES; QUERY TERMS; SCALING APPROACH; SIMILARITY MEASURING; STRESS VALUES; TERM CO-OCCURRENCE; TRANSACTION LOG; VISUAL CLUSTERING; VISUAL CONFIGURATION;

INDUSTRIAL ECONOMICS; SECURITY OF DATA; TERMINOLOGY;

HEALTH;

EID: 53849123358     PISSN: 15322882     EISSN: 15322890     Source Type: Journal    
DOI: 10.1002/asi.20911     Document Type: Article

References (58)

1. Buja, A., Swayne, D.F., Littman, M.L., Dean, N., & Hofmann, H. (2001). XGvis: Interactive data visualization with multidimensional scaling. Retrieved June 28, 2008, from http://public.research.att.com/~stat/xgobi/papers/ xgvis.pdf
2. Callon, M. (1986). Pinpointing industrial invention: An exploration of quantitative methods for the analysis of patents. In M. Callon, J. Law, & A. Rip (Eds.). Mapping the dynamics of science and technology: Sociology of science in the real world (pp. 163-188). London: Macmillan Press.
3. Callon, M., Courtial, J.P., & Laville, F. (1991). Coword analysis as a tool for describing the network of interactions between basic and technological research: The case of polymer chemistry. Scientometrics, 22, 155-205.
4. Carson, G.L. (1975). Identifying groups of computer users through factor analysis. ACM SIGCSIM Installation Management Review, 4(1), 19-32.
5. Centers for Disease Control and Prevention (2003). Chronic disease prevention. Retrieved April 1, 2008, from http://www.cdc.gov/nccdphp/ overview_text.htm
6. Centers for Disease Control and Prevention (2006). National nursing home survey (NNHS) Public-use data files. Retrieved April 1, 2008. from http://www.cdc.gov/nchs/products/elec_prods/subject/nnhs.htm
7. Chen, C. (1999). Visualising semantic spaces and author cocitation networks in digital libraries. Information Processing & Management. 35, 401-420.
8. Cooper, M.D. (2001). Usage patterns of a Web-based library catalog. Journal of the American Society for Information Science and Technology. 52, 137-148.
9. Coulter, N., Monarch, I., & Konda, S. ( 1998). Software engineering as seen through its research literature: A study in coword analysis. Journal of the American Society for Information Science, 49, 1206-1223.
10. Courtial, J.P., & Law, J. (1989). A coword study of artificial intelligence. Social Studies of Science (London), 19, 301-311.
11. Eick, S.G. (2001). Visualizing online activity. Communications of the ACM. 44(8), 45-50.
12. Feigin V.L. (2005). Stroke epidemiology in the developing world. The Lancet, 365(9478), 2160-2161.
13. Fowler, R.H. Fowler, W.A.L., & Wilson, B.A. (1991). Integrating query, thesaurus, and documents through a common visual representation. In Proceedings of the 14th Annual International ACM SIGIR Conference on Research and Development in Information (pp. 142-151). Chicago: ACM.
14. Frakes, W.B., and Baeza-Yates, R. (Eds.). (1992). Information retrieval: Data structures and algorithms. Englewood Cliffs, NJ: Prentice Hall.
15. Herskovic, J.R., Tanaka, L.Y., Hersh, W., & Bemstam, E. V. (2007). A day in the life of PubMed: Analysis of a typical day's query log. Journal of the American Medical Informatics Association, 14, 212-222.
16. Hoelscher, C. (1998). How Internet experts search for information on the Web. In H. Maurer & R.G. Olson (Eds.), In Proceedings of WebNet98: World Conference on the WWW. Internet, and Intranet.
17. Hong. Y. (2004. September). Real users with real information needs: User-centered evaluation of a consumer health Web site. Poster presented at the Eleventh World Congress on Medical Informatics (Medinfo 2004): Building High Performance Healthcare Organizations. San Francisco, CA.
18. Jiang, R., Fu, W., Wang, J. M., Lin. V., & Chen, C.C. (2005). Efficient statistical capacitance variability modeling with orthogonal principle factor analysis. In Proceedings of the 2005 IEEE/ACM international Conference on Computer-Aided Design (pp. 683-690). Washington, DC: IEEE Computer Society.
19. Keselman, A., Tse. T., Crowell. J., Browne, A., Ngo, L., & Zeng, Q. (2007). Assessing consumer health vocabulary familiarity: An exploratory study. Journal of Medical Internet Research. 9(1). Retrieved May 16, 2008, from http://www.jmir.org/2007/1/e5
20. Kohonen, T. (2001). Self-organizing maps. Springer Series in Information Sciences (Vol. 30). Berlin: Springer.
21. Kohonen, T., Kaski, S., Lagus, K., Salojärvi, J., Honkela, J., Paatero, V., et al. (2000). Self-organization of a massive document collection. IEEE Transactions on Neural Networks, Special Issue on Neural Networks for Data Mining and Knowledge Discovery, 11(3), 574-585.
22. Korfhage, R.R. (1997). Information storage and retrieval. New York: Wiley.
23. Kruskal, J.B. (1964a). Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis. Psychometrika, 29, 1-27.
24. Kruskal, J.B. (1964b). Nonmetric multidimensional scaling: a numerical method. Psychometrika. 29, 115-129.
25. Lin, X., Soergel, D., & Marchionini, G. (1991). A self-organizing semantic map for information retrieval. In Proceedings of the 14th Annual International ACM/SIGIR Conference on Research and Development in Information Retrieval (pp. 262-269). New York: ACM.
26. Masulli, F., & Schenone, A. (1999). A fuzzy clustering based segmentation system as support to diagnosis in medical imaging. Artificial Intelligence in Medicine, 16, 129-147.
27. McCormick, B.H., DeFanti, T.A., & Brown, M.D. (1987). Visualization in scientific computing. Computer Graphics. 21(6), 1-14.
28. McCray, A.T., Ide, N.C., Loane, R.R., & Tse, T. (2004). Strategies for supporting consumer health information seeking. Medinfo., 11(2), 1152-1156.
29. Meadow, C.T. (1992). Text information retrieval systems. San Diego, CA: Academic Press.
30. National Institute for Mental Health (2008). The numbers count: Mental disorders in America. Retrieved April 9, 2008, from http://www.nimh.nih.gov/ health/publications/the-numbers-count-mental-disorders-in-america.shtml
31. Nenadić, G, Mima, H, Spasić, I, Ananiadou, S, & Tsujii, J. (2002). Terminology-driven literature mining and knowledge acquisition in biomedicine. International Journal of Medical Informatics, 67, 33-48.
32. Ramsay, J. (1997). A factor analysis of user cognition and emotion. In S. Pemberton (Ed.), Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI '97) (pp. 546-547). New York: ACM.
33. Robertson, G., Card, S.K., & Mackinlay, J.D. (1989). The cognitive coprocessor architecture for interactive user interfaces. In Proceedings of the Second Annual ACM SIGGRAPH Symposium on User Interface Software and Technology (UIST '89). (pp. 10-18), New York: ACM.
34. Rousseau, R. (1998). Jaccard similarity leads to the Marczewski-Steinhaus topology for information retrieval. Information Processing & Management, 34, 87-94.
35. Salton, G. (1968), Automatic information organization and retrieval. New York: McGraw-Hill.
36. Salton, G. (1989). Automatic text processing: The transformation, analysis, and retrieval of information by computer. New York: Addison-Wesley.
37. Schvaneveldt, R.W., Durso, F.T., & Dearholt, D.W. (1989). Networking in proximity data. In G. Bower (Ed.), The psychology of learning and motivation (Vol. 24, pp. 249-284). Academic Press.
38. Shim, J., & Xu, L.(2003). Medical data-mining model for oriental medicine via BYY binary independent factor analysis. In Proceedings of the 2003 International Symposium on Circuits and Systems (Vol. 5, pp. 717-720). Washington DC: IEEE.
39. Silverstein, C., Marais, H., Henzinger, M., & Moricz, M. (1999). Analysis of a very large Web search engine query log. SIGIR Forum, 33( 1), 6-12.
40. Spink, A., Jansen, B.J., Wolfram, D., & Saracevic, T. (2002). From e-sex to e-commerce: Web search changes. Computer Magazine, 35(3), 107-109.
41. Takane, Y., Young, F.W., & De Leeuw, J. (1977). Nonmetric individual differences multidimensional scaling: An alternating least-squares method with optimal scaling features. Psychometrika, 42, 7-67.
42. Torgerson, W.S. (1952). Multidimensional scaling: I. Theory and method. Psychometrika, 17, 401-419.
43. Tsymbal, A. (2005). Local dimensionality reduction within natural clusters for medical data analysis. In Proceedings of the 18th IEEE Symposium on Computer-Based Medical Systems (pp. 365-370). CBMS. Washington, DC: IEEE Computer Society.
44. Turner, W.A., Chartron, G., Laville, E, & Michelet, B. (1988). Packaging information for peer review: New coword analysis techniques. In A.F.J. Van Raan (Ed.), Handbook of quantitative studies of science and technology (pp. 291-323). Amsterdam: Elsevier.
45. Vaughan, L. (2006). Visualizing linguistic and cultural differences using Web colink data. Journal of the American Society for Information Science and Technology, 57, 1178-1193.
46. Villmann, T., & Albani, C. (2001). Clustering of categoric data in medicine: Application of evolutionary algorithms. In B. Reusch (Ed.), Proceedings of the Seventh International Conference, 7th Fuzzy Days on Computational intelligence, theory and Applications. Lecture Notes in Computer Science, Vol. 2206. (pp.619-627). London: Springer.
47. Walter, J.A., & Ritter, H. (2002). On interactive visualization of high-dimensional data using the hyperbolic plane. In Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, (pp.123-132). New York: ACM.
48. Wang. P., Berry, M.W., & Yang, Y. (2003). Mining longitudinal Web queries: Trends and patterns. Journal of the American Society for Information Science and Technology, 54, 743-758.
49. Wood, F.B., Benson, D., LaCroix, E.M., Siegel. E.R., & Fariss, S. (2005). Use of Internet audience measurement data to gauge market share for online health information services. Journal of Medical Internet Research, 7(3). Retrieved January 14, 2008, from http://www.jmir.org/2005/3/e31/
50. Yi, K., Beheshti, J., Cole, C., Leide, J. E., & Large, A. (2006). User search behavior of domain-specific information retrieval systems: An analysis of the query logs from PsycINFO and ABC-Clio's Historical Abstracts/America: History and Life: Research Articles. Journal of the American Society for Information Science and Technology, 57, 1208-1220.
51. Zeng Q.T., & Crowell, J. (2006). Semantic classification of consumer health content. MEDNET. Retrieved May, 19, 2008, from http://www.mednetcongress. org/fullpapers/MEDNET-57_zengqingA1_e.pdf
52. Zeng, Q.T., Crowell, J., Plovnick, R.M., Kim, E., Ngo, L., & Dibble, E. (2006). Assisting consumer health information retrieval with query recommendations. Journal of the American Medical Informatics Association, 13, 80-90.
53. Zeng, Q.T., & Tse, T. (2006). Exploring and developing consumer health vocabularies. Journal of the American Medical Informatics Association, 13, 24-29.
54. Zeng, Q.T., Tse, T., Divita, C., Keselman, A., Crowell, J., Browne, A. C., Goryachev, S., & Ngo, L. (2007). Term identification methods for consumer health vocabulary development. Journal of Medical Internet Research, 9(1). Retrieved April 8, 2008, from http://www.jmir.org/2007/1/e4
55. Zhang, J., & Korfhage, R.R. (1999). A distance and angle similarity measure. Journal of the American Society for Information Science, 50, 772-778.
56. Zhang, J., & Rasmussen, E. (2001). Developing a new similarity measure from two different perspectives. Information Processing & Management, 37, 279-294.
57. Zielstorff, R.D. (2003). Controlled vocabularies for consumer health. Journal of Biomedical Informatics, 36, 326-333.
58. Zorn, M., Allen, M.P., & Horowitz, A.M. (2004). Current bibliographies in medicine: Understanding health literacy and its barriers. Retrieved January 14, 2008, from http://www.nlm.nih.gov/pubs/cbm/ healthliteracybarriers.html