Term identification methods for consumer health vocabulary development.

Abstract

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. For the development of a CHV, we explored several term identification methods, including collaborative human review and automated term recognition 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. 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%). The collaborative human review and logistic regression methods were effective for identifying terms for CHV development.