Improved biomedical document retrieval system with PubMed term statistics and expansions | Zendy

Huian Li | Zendy; Jake Y. Chen | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

Improved biomedical document retrieval system with PubMed term statistics and expansions

Author(s) -

Huian Li,

Jake Y. Chen

Publication year - 2009

Publication title -

international journal of computational intelligence in bioinformatics and systems biology

Language(s) - English

Resource type - Journals

eISSN - 1755-8042

pISSN - 1755-8034

DOI - 10.1504/ijcibsb.2009.024052

Subject(s) - term (time) , information retrieval , medical statistics , computer science , statistics , mathematics , physics , quantum mechanics

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.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research