A Text Corpora-Based Estimation of the Familiarity of Health Terminology

  • Qing Zeng
  • Eunjung Kim
  • Jon Crowell
  • Tony Tse
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3745)


In a pilot effort to improve health communication we created a method for measuring the familiarity of various medical terms. To obtain term familiarity data, we recruited 21 volunteers who agreed to take medical terminology quizzes containing 68 terms. We then created predictive models for familiarity based on term occurrence in text corpora and reader’s demographics. Although the sample size was small, our preliminary results indicate that predicting the familiarity of medical terms based on an analysis of the frequency in text corpora is feasible. Further, individualized familiarity assessment is feasible when demographic features are included as predictors.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Ratzan, S.C., Parker, R.M.: Introduction. In: National Library of Medicine Current Bibliographies in Medicine: Health Literacy. In: Selden, C.R., Zorn, M., Ratzan, S.C. (eds.) NLM Pub No. CBM 2000-1. Bethesda, National Institutes of Health, MD U.S. Department of Health and Human Services (2000)Google Scholar
  2. 2.
    Rudd, R., Moeykens, B., et al.: Health and Literacy: A Review of Medical and Public Health Literature. In: Comings, J., Garner, B., Smith, C. (eds.) Annual Review of Adult Learning and Literacy, vol. 1, pp. 158–199. Jossey-Bass, San Francisco (2000)Google Scholar
  3. 3.
    Osborne, H.: Health Literacy From A To Z: Practical Ways To Communicate Your Health. Jones & Bartlett Pub., USA (2004)Google Scholar
  4. 4.
    AHRQ, IOM weigh in on developing a health-literate America. Qual. Lett. Healthc. Lead 16(5), 6–8 (2004)Google Scholar
  5. 5.
    McCray, A.T.: Promoting health literacy. J. Am. Med. Inform. Assoc. 12(2), 152–163 (2005)CrossRefGoogle Scholar
  6. 6.
    Davis, T.C., Long, S.W., et al.: Rapid estimate of adult literacy in medicine: a shortened screening instrument. Fam. Med. 25(6), 391–395 (1993)Google Scholar
  7. 7.
    Parker, R.M., Baker, D.W., et al.: The test of functional health literacy in adults: a new instrument for measuring patients literacy skills. J. Gen. Intern. Med. 10(10), 537–541 (1995)CrossRefGoogle Scholar
  8. 8.
    Zakaluk, B.L., Samuels, S.J.: Readability: Its Past, Present, and Future, Intl Reading Assn (1988)Google Scholar
  9. 9.
    Gemoets, D., Rosemblat, G., Tse, T.: Assessing readability of consumer health information: an exploratory study. Medinfo. 869–873 (2004)Google Scholar
  10. 10.
    Zeng, Q.T., Tse, T., Crowell, J., Divita, G., Roth, R., Browne, A.C.: Identifying consumer-friendly display (CFD) names for health concepts. Technical Report, DSG-TR- 2005-003. Boston: Decision Systems Group (DSG), Brigham and Women’s Hospital, Harvard Medical School (2005)Google Scholar
  11. 11.
    Chall, J.S., Dale, E. Readability Revisited: The New Dale-Chall Readability Formula, Brookline Books (May 1, 1995)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Qing Zeng
    • 1
  • Eunjung Kim
    • 1
  • Jon Crowell
    • 1
  • Tony Tse
    • 2
  1. 1.Decision Systems GroupHarvard Medical School and Brigham & Women’s HospitalBoston
  2. 2.Lister Hill National Center for Biomedical CommunicationsNational Library of MedicineBethesda

Personalised recommendations