Characterizing Health-Related Information Needs of Domain Experts

  • Eya Znaidi
  • Lynda Tamine
  • Cecile Chouquet
  • Chiraz Latiri
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7885)


In information retrieval literature, understanding the users’ intents behind the queries is critically important to gain a better insight of how to select relevant results. While many studies investigated how users in general carry out exploratory health searches in digital environments, a few focused on how are the queries formulated, specifically by domain expert users. This study intends to fill this gap by studying 173 health expert queries issued from 3 medical information retrieval tasks within 2 different evaluation compaigns. A statistical analysis has been carried out to study both variation and correlation of health-query attributes such as length, clarity and specificity of either clinical or non clinical queries. The knowledge gained from the study has an immediate impact on the design of future health information seeking systems.


Health Information Retrieval Information Needs Statistical Analysis 


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  1. 1.
    Andrews, J.E., Pearce, K.A., Ireson, C., Love, M.M.: Information-seeking behaviors of practitioners in a primary care practice-based research network (PBRN). Journal of the Medical Library Association, JMLA 93(2), 206–212 (2005)Google Scholar
  2. 2.
    Arora, N., Hesse, B., Rimer, B.K., Viswanath, K., Clayman, M., Croyle, R.: Frustrated and confused: the american and public rates its cancer-related information-seeking experiences. Journal of General Internal Medicine 23(3), 223–228 (2007)CrossRefGoogle Scholar
  3. 3.
    Bhavnani, S.: Important cognitive components of domain-specific knowledge. In: Proceedings of Text Rerieval Conference TREC, TREC 2001, pp. 571–578 (2001)Google Scholar
  4. 4.
    Bhavnani, S.: Domain specific strategies for the effective retrieval of health care and shopping information. In: Proceedings of SIGCHI, pp. 610–611 (2002)Google Scholar
  5. 5.
    Boudin, F., Nie, J., Bartlett, J.C., Grad, R., Pluye, P., Dawes, M.: Combining classifiers for robust pico element detection. BMC Medical Informatics and Decision Making, 1–6 (2010)Google Scholar
  6. 6.
    Dinh, D., Tamine, L.: Biomedical concept extraction based on combining the content-based and word order similarities. In: Proceedings of the 2011 ACM Symposium on Applied Computing, SAC 2011, pp. 1159–1163. ACM, New York (2011)CrossRefGoogle Scholar
  7. 7.
    Dinh, D., Tamine, L.: Combining Global and Local Semantic Contexts for Improving Biomedical Information Retrieval. In: Clough, P., Foley, C., Gurrin, C., Jones, G.J.F., Kraaij, W., Lee, H., Mudoch, V. (eds.) ECIR 2011. LNCS, vol. 6611, pp. 375–386. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  8. 8.
    Dogan, R., Muray, G., Névéol, A., Lu, Z.: Understanding pubmed user search behavior through log analysis. Database Journal, 1–19 (2009)Google Scholar
  9. 9.
    Ely, J.W., Osheroff, J.A., Ebell, M.H., Chambliss, M.L., Vinson, D.C., Stevermer, J.J., Pifer, E.A.: Obstacles to answering doctors’ questions about patient care with evidence: qualitative study. BMJ 324(7339), 710 (2002)CrossRefGoogle Scholar
  10. 10.
    Eysenbach, G.: Consumer health informatics. Biomedical Journal (3), 543–557 (2012)Google Scholar
  11. 11.
    Hong, Y., Cruz, N., Marnas, G., Early, E., Gillis, R.: A query analysis of consumer health information retrieval. In: Proceedings of Annual Symposium for Biomedical and Health Informatics, pp. 791–792 (2002)Google Scholar
  12. 12.
    Jones, S.: A statistical interpretation of term specificity and its application to retrieval. Journal of Documentation 28(1), 11–20 (1972)CrossRefGoogle Scholar
  13. 13.
    Oh, S.: The characteristics and motivations of health answerers for sharing information, knowledge, and experiences in online environments. Journal of the American Society for Information Science and Technology 63(3), 543–557 (2012)CrossRefGoogle Scholar
  14. 14.
    Spink, A., Jansen, B.: Web Search: Public Searching of the Web. Kluwer Academic Publishers (2004)Google Scholar
  15. 15.
    Steve, C.R., Croft, W.: Quantifying query ambiguity. In: Proceedings of the Second International Conference on Human Language Technology Research, HLT 2002, San Francisco, CA, USA, pp. 104–109 (2002)Google Scholar
  16. 16.
    Tomes, E., Latter, C.: How consumers search for health information. Health Informatics Journal 13(3), 223–235 (2007)CrossRefGoogle Scholar
  17. 17.
    White, R., Moris, D.: How medical expertise influences seb search behaviour. In: Proceedings of the 31st International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2008, pp. 791–792 (2008)Google Scholar
  18. 18.
    Wildemuth, B.: The effects of domain-knowledge on search tactic formulation, vol. 55, pp. 246–258 (2004)Google Scholar
  19. 19.
    Zeng, Q., Crowell, J., Plovnick, R., Kim, E., Ngo, L., Dibble, E.: Research paper: Assisting consumer health information retrieval with query recommendations. Journal of American Medical Informatics Associations 13(1), 80–90 (2006)CrossRefGoogle Scholar
  20. 20.
    Zeng, Q., Kogan, S., Ash, N., Greenes, R., Boxwala, A.: Characteristics of consumer technology for health information retrieval. Methods of Information in Medicine 41, 289–298 (2002)Google Scholar
  21. 21.
    Zeng, Q., Kogan, S., Plovnick, R., Croweel, J., Lacroix, E., Greens, R.: Positive attitudes and failed queries: An exploration of the conundrums of health information retrieval. International Journal of Medical Informatics 73(1), 45–55 (2004)CrossRefGoogle Scholar
  22. 22.
    Zhang, J., Wolfram, D., Wang, P., Hong, Y., Gillis, R.: Visualization of health-subject analysis based on query term co-occurrences. Journal of American Society in Information Science and Technology 59(12), 1933–1947 (2008)CrossRefGoogle Scholar
  23. 23.
    Zickuhr, K.: Generations 2010. Technical report, Pew Internet & American Life Project (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Eya Znaidi
    • 1
  • Lynda Tamine
    • 1
  • Cecile Chouquet
    • 2
  • Chiraz Latiri
    • 3
  1. 1.IRITUniversity of ToulouseFrance
  2. 2.Institute of MathematicsUniversity of ToulouseFrance
  3. 3.Computer Sciences DepartmentFaculty of Sciences of TunisTunisia

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