PMID- 27453982
OWN - NLM
STAT- MEDLINE
DCOM- 20170726
LR  - 20181202
IS  - 1471-2105 (Electronic)
IS  - 1471-2105 (Linking)
VI  - 17 Suppl 9
IP  - Suppl 9
DP  - 2016 Jul 19
TI  - Integrating unified medical language system and association mining techniques 
      into relevance feedback for biomedical literature search.
PG  - 264
LID - 10.1186/s12859-016-1129-z [doi]
LID - 264
AB  - 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.
FAU - Ji, Yanqing
AU  - Ji Y
AD  - Department of Electrical and Computer Engineering, Gonzaga University, Spokane, 
      WA, USA. ji@gonzaga.edu.
FAU - Ying, Hao
AU  - Ying H
AD  - Department of Electrical and Computer Engineering, Wayne State University, 
      Detroit, MI, USA.
FAU - Tran, John
AU  - Tran J
AD  - Frontier Behavioral Health, Spokane, WA, USA.
FAU - Dews, Peter
AU  - Dews P
AD  - Department of Medicine, St. Mary Mercy Hospital, Livonia, MI, USA.
FAU - Massanari, R Michael
AU  - Massanari RM
AD  - Research for The Critical Junctures Institute, Bellingham, WA, USA.
LA  - eng
PT  - Journal Article
DEP - 20160719
PL  - England
TA  - BMC Bioinformatics
JT  - BMC bioinformatics
JID - 100965194
SB  - IM
MH  - Algorithms
MH  - Data Mining/*methods
MH  - Feedback
MH  - Genomics
MH  - Humans
MH  - Medicine in Literature
MH  - Search Engine/*methods
MH  - Software
MH  - Unified Medical Language System
PMC - PMC4959361
OTO - NOTNLM
OT  - Association mining
OT  - Biomedical literature search
OT  - Relevance feedback
OT  - UMLS
EDAT- 2016/07/28 06:00
MHDA- 2017/07/27 06:00
PMCR- 2016/07/19
CRDT- 2016/07/26 06:00
PHST- 2016/07/26 06:00 [entrez]
PHST- 2016/07/28 06:00 [pubmed]
PHST- 2017/07/27 06:00 [medline]
PHST- 2016/07/19 00:00 [pmc-release]
AID - 10.1186/s12859-016-1129-z [pii]
AID - 1129 [pii]
AID - 10.1186/s12859-016-1129-z [doi]
PST - epublish
SO  - BMC Bioinformatics. 2016 Jul 19;17 Suppl 9(Suppl 9):264. doi: 
      10.1186/s12859-016-1129-z.