title: Integrating unified medical language system and association mining techniques into relevance feedback for biomedical literature search.
abstract: BACKGROUND
  Finding highly relevant articles from biomedical databases is challenging not only because it is often difficult to accurately express a user's underlying intention through keywords but also because a keyword-based query normally returns a long list of hits with many citations being unwanted by the user. This paper proposes a novel biomedical literature search system, called BiomedSearch, which supports complex queries and relevance feedback.
  
  
  METHODS
  The system employed association mining techniques to build a k-profile representing a user's relevance feedback. More specifically, we developed a weighted interest measure and an association mining algorithm to find the strength of association between a query and each concept in the article(s) selected by the user as feedback. The top concepts were utilized to form a k-profile used for the next-round search. BiomedSearch relies on Unified Medical Language System (UMLS) knowledge sources to map text files to standard biomedical concepts. It was designed to support queries with any levels of complexity.
  
  
  RESULTS
  A prototype of BiomedSearch software was made and it was preliminarily evaluated using the Genomics data from TREC (Text Retrieval Conference) 2006 Genomics Track. Initial experiment results indicated that BiomedSearch increased the mean average precision (MAP) for a set of queries.
  
  
  CONCLUSIONS
  With UMLS and association mining techniques, BiomedSearch can effectively utilize users' relevance feedback to improve the performance of biomedical literature search.
authors.affiliation: Department of Electrical and Computer Engineering, Gonzaga University, Spokane, WA, USA. ji@gonzaga.edu.
authors.forename: Yanqing
authors.initials: Y
authors.lastname: Ji
authors.affiliation: Department of Electrical and Computer Engineering, Wayne State University, Detroit, MI, USA.
authors.forename: Hao
authors.initials: H
authors.lastname: Ying
authors.affiliation: Frontier Behavioral Health, Spokane, WA, USA.
authors.forename: John
authors.initials: J
authors.lastname: Tran
authors.affiliation: Department of Medicine, St. Mary Mercy Hospital, Livonia, MI, USA.
authors.forename: Peter
authors.initials: P
authors.lastname: Dews
authors.affiliation: Research for The Critical Junctures Institute, Bellingham, WA, USA.
authors.forename: R Michael
authors.initials: RM
authors.lastname: Massanari
mesh_terms: D000465:Algorithms; D057225:Data Mining / Q000379:methods*; D005246:Feedback; D023281:Genomics; D006801:Humans; D008513:Medicine in Literature; D057386:Search Engine / Q000379:methods*; D012984:Software; D017432:Unified Medical Language System
keywords: Association mining; Biomedical literature search; Relevance feedback; UMLS