Name
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. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1007/11573067_19 | ISBMDA |
Keywords | Field | DocType |
sample size,prediction model | Medical terminology,Terminology,Computer science,Text corpus,Natural language processing,Demographics,Artificial intelligence,Health communication,Sample size determination | Conference |
Volume | ISSN | ISBN |
3745 | 0302-9743 | 3-540-29674-3 |
Citations | PageRank | References |
17 | 2.81 | 3 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qing Zeng | 1 | 547 | 67.98 |
| Eunjung Kim | 2 | 17 | 2.81 |
| Jonathan Crowell | 3 | 44 | 7.71 |
| Tony Tse | 4 | 117 | 13.40 |