1. Term Identification Methods for Consumer Health Vocabulary Development
- Author
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Guy Divita, Allen C. Browne, Tony Tse, Long Ngo, Qing Treitler Zeng, Alla Keselman, Jon Crowell, and Sergey Goryachev
- Subjects
Vocabulary ,020205 medical informatics ,Computer science ,media_common.quotation_subject ,Health Informatics ,02 engineering and technology ,lcsh:Computer applications to medicine. Medical informatics ,Logistic regression ,computer.software_genre ,Machine learning ,Automation ,03 medical and health sciences ,0302 clinical medicine ,0202 electrical engineering, electronic engineering, information engineering ,Humans ,030212 general & internal medicine ,natural language processing ,Cooperative Behavior ,Set (psychology) ,Health Education ,vocabulary ,media_common ,Original Paper ,business.industry ,lcsh:Public aspects of medicine ,lcsh:RA1-1270 ,Ambiguity ,Models, Theoretical ,Vocabulary development ,Term (time) ,Identification (information) ,Logistic Models ,ROC Curve ,Vocabulary, Controlled ,Consumer health information ,lcsh:R858-859.7 ,Health education ,Data mining ,Artificial intelligence ,business ,computer - Abstract
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.
- Published
- 2007