Improved biomedical document retrieval system with PubMed term statistics and expansions

Authors:
Huian Li;Jake Yue Chen
Affiliations:
University Information Technology Services, Indiana University, Indianapolis, IN 46202, USA.;Department of Computer and Information Science Purdue University School of Science, Indiana University School of Informatics, Indianapolis, IN 46202, USA
Venue:
International Journal of Computational Intelligence in Bioinformatics and Systems Biology
Year:
2009

Citing 3
Cited 3

Introduction to Modern Information Retrieval

Introduction to Modern Information Retrieval
Text mining: generating hypotheses from MEDLINE

Journal of the American Society for Information Science and Technology
Exploiting lexical expansions and Boolean compositions for web querying

RANLPIR '00 Proceedings of the ACL-2000 workshop on Recent advances in natural language processing and information retrieval: held in conjunction with the 38th Annual Meeting of the Association for Computational Linguistics - Volume 11

Mining disease-specific molecular association profiles from biomedical literature: a case study

Proceedings of the 2008 ACM symposium on Applied computing
Indexing ICD-9 codes for free-textual clinical diagnosis records by a new ensemble classifier

International Journal of Computational Intelligence in Bioinformatics and Systems Biology
Discovering breast cancer drug candidates from biomedical literature

International Journal of Data Mining and Bioinformatics

Quantified Score

Hi-index	0.00

Visualization

Abstract

Large biomedical abstract databases such as MEDLINE enable users to search for large bodies of biomedical knowledge quickly. In this study, we describe a new framework to improve the performance of MEDLINE document retrieval. We first analysed and built a normalized term frequency distributions for 1.8 million terms by sampling from 1,500,000 MEDLINE abstracts. Then, we developed a statistical model to identify significantly observed terms ('gists') in a document as additional document keywords to help improve document retrieval precisions. To improve document recalls, we integrated several biological ontologies that can expand user queries with semantically compatible terms. The framework was implemented in Oracle 10g.