The widely known vocabulary gap between health consumers and healthcare 
professionals hinders information seeking and health dialogue of consumers on 
end-user health applications. The Open Access and Collaborative Consumer Health 
Vocabulary (OAC CHV), which contains health-related terms used by lay consumers, 
has been created to bridge such a gap. Specifically, the OAC CHV facilitates 
consumers' health information retrieval by enabling consumer-facing health 
applications to translate between professional language and consumer friendly 
language. To keep up with the constantly evolving medical knowledge and language 
use, new terms need to be identified and added to the OAC CHV. User-generated 
content on social media, including social question and answer (social Q&A) 
sites, afford us an enormous opportunity in mining consumer health terms. 
Existing methods of identifying new consumer terms from text typically use 
ad-hoc lexical syntactic patterns and human review. Our study extends an 
existing method by extracting n-grams from a social Q&A textual corpus and 
representing them with a rich set of contextual and syntactic features. Using 
K-means clustering, our method, simiTerm, was able to identify terms that are 
both contextually and syntactically similar to the existing OAC CHV terms. We 
tested our method on social Q&A corpora on two disease domains: diabetes and 
cancer. Our method outperformed three baseline ranking methods. A post-hoc 
qualitative evaluation by human experts further validated that our method can 
effectively identify meaningful new consumer terms on social Q&A.