%0 Journal Article
%@ 1438-8871
%I Gunther Eysenbach
%V 9
%N 1
%P e4
%T Term Identification Methods for Consumer Health Vocabulary Development
%A Zeng,Qing T
%A Tse,Tony
%A Divita,Guy
%A Keselman,Alla
%A Crowell,Jon
%A Browne,Allen C
%A Goryachev,Sergey
%A Ngo,Long
%+ Harvard Medical School, Decision Systems Group, Brigham and Women's Hospital, Thorn 304, 75 Francis Street, Boston, MA 02115, USA, +1 617 732 7694, qzeng@dsg.harvard.edu
%K Consumer health information
%K vocabulary
%K natural language processing
%D 2007
%7 14.3.2007
%9 Original Paper
%J J Med Internet Res
%G English
%X Background:  The development of consumer health information applications such as health education websites has motivated the research on consumer health vocabulary (CHV). Term identification is a critical task in vocabulary development. Because of the heterogeneity and ambiguity of consumer expressions, term identification for CHV is more challenging than for professional health vocabularies. Objective:  For the development of a CHV, we explored several term identification methods, including collaborative human review and automated term recognition methods. Methods:  A set of criteria was established to ensure consistency in the collaborative review, which analyzed 1893 strings. Using the results from the human review, we tested two automated methods—C-value formula and a logistic regression model. Results:  The study identified 753 consumer terms and found the logistic regression model to be highly effective for CHV term identification (area under the receiver operating characteristic curve = 95.5%). Conclusions:  The collaborative human review and logistic regression methods were effective for identifying terms for CHV development. 
%M 17478413
%R 10.2196/jmir.9.1.e4
%U http://www.jmir.org/2007/1/e4/
%U https://doi.org/10.2196/jmir.9.1.e4
%U http://www.ncbi.nlm.nih.gov/pubmed/17478413