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Effects of Language and Terminology of Query Suggestions on the Precision of Health Searches

  • Carla Teixeira Lopes
  • Cristina Ribeiro
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11018)

Abstract

Health information is highly sought on the Web by users that naturally have different levels of expertise in the topics they search for. Assisting users with query formulation is important when users are searching for topics about which they have little knowledge or familiarity. To assist users with health query formulation, we developed a query suggestion system that provides alternative queries combining Portuguese and English language with lay and medico-scientific terminology. Here, we analyze how this system affects the precision of search sessions. Results show that a system providing these suggestions tends to perform better than a system without them. On specific groups of users, clicking on suggestions has positive effects on precision while using them as sources of new terms has the opposite effect. This suggests that a personalized suggestion system might have a good impact on precision.

Keywords

Query suggestion Health Language Terminology English proficiency Health literacy Topic familiarity 

Notes

Acknowledgments

This work was supported by Project “NORTE-01-0145- FEDER-000016” (NanoSTIMA), financed by the North Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, and through the European Regional Development Fund (ERDF).

References

  1. 1.
    Borlund, P.: The IIR evaluation model: a framework for evaluation of interactive information retrieval systems. Inf. Res. 8(3) (2003). http://informationr.net/ir/8-3/paper152.html
  2. 2.
    Fox, S.: Health topics. Technical report, Pew Internet & American Life Project, Washington, DC (2011)Google Scholar
  3. 3.
    Gao, W., Niu, C., Nie, J.Y., Zhou, M., Wong, K.F., Hon, H.W.: Exploiting query logs for cross-lingual query suggestions. ACM Trans. Inf. Syst. 28(2), 1–33 (2010).  https://doi.org/10.1145/1740592.1740594CrossRefGoogle Scholar
  4. 4.
    Jansen, B.J., McNeese, M.D.: Evaluating the effectiveness of and patterns of interactions with automated searching assistance. J. Am. Soc. Inf. Sci. 56(14), 1480–1503 (2005).  https://doi.org/10.1002/asi.20242CrossRefGoogle Scholar
  5. 5.
    Kelly, D., Gyllstrom, K., Bailey, E.W.: A comparison of query and term suggestion features for interactive searching. In: Proceedings of the 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2009, pp. 371–378. ACM, New York (2009).  https://doi.org/10.1145/1571941.1572006
  6. 6.
    Kogan, S., Zeng, Q., Ash, N., Greenes, R.A.: Problems and challenges in patient information retrieval: a descriptive study. In: Proceedings AMIA Symposium, pp. 329–333 (2001). http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2243602/
  7. 7.
    Kriewel, S., Fuhr, N.: Evaluation of an adaptive search suggestion system. In: Gurrin, C., et al. (eds.) ECIR 2010. LNCS, vol. 5993, pp. 544–555. Springer, Heidelberg (2010).  https://doi.org/10.1007/978-3-642-12275-0_47CrossRefGoogle Scholar
  8. 8.
    Lopes, C.T., Ribeiro, C.: Measuring the value of health query translation: an analysis by user language proficiency. J. Am. Soc. Inf. Sci. Technol. 64(5), 951–963 (2013).  https://doi.org/10.1002/asi.22812CrossRefGoogle Scholar
  9. 9.
    Lopes, C.T., Ribeiro, C.: Effects of terminology on health queries: an analysis by user’s health literacy and topic familiarity, vol. 39, chap. 10, pp. 145–184. Emerald Group Publishing Limited (2015).  https://doi.org/10.1108/S0065-283020150000039013
  10. 10.
    Lopes, C.T., Ribeiro, C.: Effects of language and terminology on the usage of health query suggestions. In: Fuhr, N., et al. (eds.) CLEF 2016. LNCS, vol. 9822, pp. 83–95. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-44564-9_7CrossRefGoogle Scholar
  11. 11.
    Luo, G., Tang, C.: On iterative intelligent medical search. In: Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2008, pp. 3–10. ACM, New York (2008).  https://doi.org/10.1145/1390334.1390338
  12. 12.
    Luo, G., Tang, C., Yang, H., Wei, X.: MedSearch: a specialized search engine for medical information retrieval. In: Proceeding of the 17th ACM Conference on Information and Knowledge Mining, CIKM 2008, pp. 143–152. ACM, New York (2008).  https://doi.org/10.1145/1458082.1458104
  13. 13.
    McCray, A.T., Tse, T.: Understanding search failures in consumer health information systems. In: AMIA Annual Symposium Proceedings, pp. 430–434 (2003). http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1479930/
  14. 14.
    NLM: 2012AA consumer health vocabulary source information (2012). http://www.nlm.nih.gov/research/umls/sourcereleasedocs/current/CHV/index.html
  15. 15.
    Rawson, K.A., et al.: The METER: a brief, self-administered measure of health literacy. J. Gen. Intern. Med. 25(1), 67–71 (2010).  https://doi.org/10.1007/s11606-009-1158-7CrossRefGoogle Scholar
  16. 16.
    Robertson, S.E., Kanoulas, E., Yilmaz, E.: Extending average precision to graded relevance judgments. In: Proceeding of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2010, pp. 603–610. ACM, New York, July 2010.  https://doi.org/10.1145/1835449.1835550
  17. 17.
    Saracevic, T.: Relevance: a review of the literature and a framework for thinking on the notion in information science. Part III: behavior and effects of relevance. J. Am. Soc. Inf. Sci. Technol. 58(13), 2126–2144 (2007)CrossRefGoogle Scholar
  18. 18.
    Lopes, C.T., Paiva, D., Ribeiro, C.: Effects of language and terminology of query suggestions on medical accuracy considering different user characteristics. J. Assoc. Inf. Sci. Technol. 68(9), 2063–2075 (2017).  https://doi.org/10.1002/asi.23874CrossRefGoogle Scholar
  19. 19.
    Toms, E.G., Latter, C.: How consumers search for health information. Health Inform. J. 13(3), 223–235 (2007).  https://doi.org/10.1177/1460458207079901CrossRefGoogle Scholar
  20. 20.
    Zarro, M., Lin, X.: Using social tags and controlled vocabularies as filters for searching and browsing: a health science experiment. In: Fifth Workshop on Human-Computer Interaction and Information Retrieval, October 2011Google Scholar
  21. 21.
    Zeng, Q.T., Crowell, J., Plovnick, R.M., Kim, E., Ngo, L., Dibble, E.: Assisting consumer health information retrieval with query recommendations. J. Am. Med. Inform. Assoc. (JAMIA) 13(1), 80–90 (2006).  https://doi.org/10.1197/jamia.m1820CrossRefGoogle Scholar
  22. 22.
    Zhang, Y.: Contextualizing consumer health information searching: an analysis of questions in a social Q&A community. In: Proceedings of the 1st ACM International Health Informatics Symposium, pp. 210–219 (2010)Google Scholar
  23. 23.
    Zielstorff, R.: Controlled vocabularies for consumer health. J. Biomed. Inform. 36(4–5), 326–333 (2003).  https://doi.org/10.1016/j.jbi.2003.09.015CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.DEI, Faculdade de EngenhariaUniversidade do Porto and INESC TECPortoPortugal

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