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.