A text corpora-based estimation of the familiarity of health terminology

Authors:
Qing Zeng;Eunjung Kim;Jon Crowell;Tony Tse
Affiliations:
Decision Systems Group, Harvard Medical School and Brigham & Women's Hospital, Boston, MA;Decision Systems Group, Harvard Medical School and Brigham & Women's Hospital, Boston, MA;Decision Systems Group, Harvard Medical School and Brigham & Women's Hospital, Boston, MA;Lister Hill National Center for Biomedical Communications, National Library of Medicine, Bethesda, MD
Venue:
ISBMDA'05 Proceedings of the 6th International conference on Biological and Medical Data Analysis
Year:
2005

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Abstract

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.