title: Recommending MeSH terms for annotating biomedical articles.
abstract: BACKGROUND
  Due to the high cost of manual curation of key aspects from the scientific literature, automated methods for assisting this process are greatly desired. Here, we report a novel approach to facilitate MeSH indexing, a challenging task of assigning MeSH terms to MEDLINE citations for their archiving and retrieval.
  
  
  METHODS
  Unlike previous methods for automatic MeSH term assignment, we reformulate the indexing task as a ranking problem such that relevant MeSH headings are ranked higher than those irrelevant ones. Specifically, for each document we retrieve 20 neighbor documents, obtain a list of MeSH main headings from neighbors, and rank the MeSH main headings using ListNet-a learning-to-rank algorithm. We trained our algorithm on 200 documents and tested on a previously used benchmark set of 200 documents and a larger dataset of 1000 documents.
  
  
  RESULTS
  Tested on the benchmark dataset, our method achieved a precision of 0.390, recall of 0.712, and mean average precision (MAP) of 0.626. In comparison to the state of the art, we observe statistically significant improvements as large as 39% in MAP (p-value <0.001). Similar significant improvements were also obtained on the larger document set.
  
  
  CONCLUSIONS
  Experimental results show that our approach makes the most accurate MeSH predictions to date, which suggests its great potential in making a practical impact on MeSH indexing. Furthermore, as discussed the proposed learning framework is robust and can be adapted to many other similar tasks beyond MeSH indexing in the biomedical domain. All data sets are available at: http://www.ncbi.nlm.nih.gov/CBBresearch/Lu/indexing.
authors: State Key Laboratory of Intelligent Technology and Systems, Tsinghua National Laboratory for Information Science and Technology, Department of Computer Science and Technology, Tsinghua University, Beijing, PR China.
authors: Minlie
authors: M
authors: Huang
authors: Aurélie
authors: A
authors: Névéol
authors: Zhiyong
authors: Z
authors: Lu
references: Proc AMIA Symp. 2000;:17-21
references: 11079836
references: Proc AMIA Symp. 2001;:17-21
references: 11825149
references: BMC Bioinformatics. 2007 Oct 30;8:423
references: 17971238
references: Stud Health Technol Inform. 2004;107(Pt 1):268-72
references: 15360816
references: Bioinformatics. 2011 Apr 1;27(7):1032-3
references: 21303863
references: Bull Med Libr Assoc. 1983 Apr;71(2):176-83
references: 6344946
references: J Biomed Inform. 2010 Oct;43(5):694-700
references: 20382265
references: Bioinformatics. 2009 Jun 1;25(11):1412-8
references: 19376821
references: Bioinformatics. 2005 Aug 1;21(15):3324-6
references: 15919728
references: Bioinformatics. 2007 Jul 1;23(13):i41-8
references: 17646325
references: Proc AMIA Symp. 2001;:319-23
references: 11825203
references: Bioinformatics. 2009 Aug 1;25(15):1944-51
references: 19497938
references: Bioinformatics. 2006 Mar 15;22(6):658-64
references: 16287934
references: Proc Annu Symp Comput Appl Med Care. 1994;:157-61
references: 7949911
references: AMIA Annu Symp Proc. 2005;:266-70
references: 16779043
references: AMIA Annu Symp Proc. 2010 Nov 13;2010:147-51
references: 21346958
references: J Am Med Inform Assoc. 2008 Jul-Aug;15(4):546-53
references: 18436913
references: J Biomed Inform. 2009 Oct;42(5):814-23
references: 19166973
references: AMIA Annu Symp Proc. 2005;:271-5
references: 16779044