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AMIA Jt Summits Transl Sci Proc. 2015 Mar 23;2015:56-63. eCollection 2015.

Ranking Medical Subject Headings using a factor graph model.

Author information

1
Division of Biomedical Informatics, University of California, San Diego, La Jolla, CA 92093 USA, Email: { w2wei@ucsd.edu , shw070@ucsd.edu , x1jiang@ucsd.edu , lohnomachado@ucsd.edu.
2
National Center for Biomedical Communications, National Library of Medicine, Bethesda, MD, 20894, Email: ddemner@mail.nih.gov.

Abstract

Automatically assigning MeSH (Medical Subject Headings) to articles is an active research topic. Recent work demonstrated the feasibility of improving the existing automated Medical Text Indexer (MTI) system, developed at the National Library of Medicine (NLM). Encouraged by this work, we propose a novel data-driven approach that uses semantic distances in the MeSH ontology for automated MeSH assignment. Specifically, we developed a graphical model to propagate belief through a citation network to provide robust MeSH main heading (MH) recommendation. Our preliminary results indicate that this approach can reach high Mean Average Precision (MAP) in some scenarios.

PMID:
26306236
PMCID:
PMC4525219

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