Integrating unified medical language system and association mining techniques into relevance feedback for biomedical literature search

BMC Bioinformatics

Research

Yanqing Ji¹  Hao Ying²  Peter Dews³  John Tran⁴  R. Michael Massanari⁵ 

[1] Department of Electrical and Computer Engineering, Gonzaga University, Spokane, WA, USA;Department of Electrical and Computer Engineering, Wayne State University, Detroit, MI, USA;Department of Medicine, St. Mary Mercy Hospital, Livonia, MI, USA;Frontier Behavioral Health, Spokane, WA, USA;Research for The Critical Junctures Institute, Bellingham, WA, USA;

关键词: Biomedical literature search;    Relevance feedback;    Association mining;    UMLS;   

DOI  :  10.1186/s12859-016-1129-z

来源: Springer

【 摘 要 】

BackgroundFinding 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.MethodsThe 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.ResultsA 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.ConclusionsWith UMLS and association mining techniques, BiomedSearch can effectively utilize users’ relevance feedback to improve the performance of biomedical literature search.

【 授权许可】

CC BY   
© The Author(s). 2016

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