title: SimQ: real-time retrieval of similar consumer health questions.
abstract: BACKGROUND
  There has been a significant increase in the popularity of Web-based question-and-answer (Q&A) services that provide health care information for consumers. Large amounts of Q&As have been archived in these online communities, which form a valuable knowledge base for consumers who seek answers to their health care concerns. However, due to consumers' possible lack of professional knowledge, it is still very challenging for them to find Q&As that are closely relevant to their own health problems. Consumers often repeatedly ask similar questions that have already been answered previously by other users.
  
  
  OBJECTIVE
  In this study, we aim to develop efficient informatics methods that can retrieve similar Web-based consumer health questions using syntactic and semantic analysis.
  
  
  METHODS
  We propose the "SimQ" to achieve this objective. SimQ is an informatics framework that compares the similarity of archived health questions and retrieves answers to satisfy consumers' information needs. Statistical syntactic parsing was used to analyze each question's syntactic structure. Standardized Unified Medical Language System (UMLS) was employed to annotate semantic types and extract medical concepts. Finally, the similarity between sentences was calculated using both semantic and syntactic features.
  
  
  RESULTS
  We used 2000 randomly selected consumer questions to evaluate the system's performance. The results show that SimQ reached the highest precision of 72.2%, recall of 78.0%, and F-score of 75.0% when using compositional feature representations.
  
  
  CONCLUSIONS
  We demonstrated that SimQ complements the existing Q&A services of Netwellness, a not-for-profit community-based consumer health information service that consists of nearly 70,000 Q&As and serves over 3 million users each year. SimQ not only reduces response delay by instantly providing closely related questions and answers, but also helps consumers to improve the understanding of their health concerns.
authors.affiliation: Center for Biomedical Data and Language Processsing, Department of Health Informatics and Administration, University of Wisconsin Milwaukee, Milwaukee, WI, United States. luojake@gmail.com.
authors.forename: Jake
authors.identifier: http://orcid.org/0000-0002-3900-643X
authors.initials: J
authors.lastname: Luo
authors.forename: Guo-Qiang
authors.identifier: http://orcid.org/0000-0003-3241-5377
authors.initials: GQ
authors.lastname: Zhang
authors.forename: Susan
authors.identifier: http://orcid.org/0000-0001-8930-7743
authors.initials: S
authors.lastname: Wentz
authors.forename: Licong
authors.identifier: http://orcid.org/0000-0001-5549-8780
authors.initials: L
authors.lastname: Cui
authors.forename: Rong
authors.identifier: http://orcid.org/0000-0003-3127-4795
authors.initials: R
authors.lastname: Xu
mesh_terms: D054626:Consumer Health Information / Q000379:methods*; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods* / Q000706:statistics & numerical data; D020407:Internet; D011795:Surveys and Questionnaires; D017216:Telemedicine / Q000379:methods* / Q000706:statistics & numerical data*
keywords: Consumer Health Informatics; Consumer Question Retrieval; Health Care Questions; Health Information Delivery; Netwellness.org; Online Health Information Seeking; Search and Query; Similarity Analysis