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.
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Zeng, Q., Kim, E., Crowell, J., Tse, T. (2005). A Text Corpora-Based Estimation of the Familiarity of Health Terminology. In: Oliveira, J.L., Maojo, V., Martín-Sánchez, F., Pereira, A.S. (eds) Biological and Medical Data Analysis. ISBMDA 2005. Lecture Notes in Computer Science(), vol 3745. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11573067_19
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DOI: https://doi.org/10.1007/11573067_19
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