A bottom-up approach to MEDLINE indexing recommendations

AMIA Annu Symp Proc. 2011;2011:1583-92. Epub 2011 Oct 22.


MEDLINE indexing performed by the US National Library of Medicine staff describes the essence of a biomedical publication in about 14 Medical Subject Headings (MeSH). Since 2002, this task is assisted by the Medical Text Indexer (MTI) program. We present a bottom-up approach to MEDLINE indexing in which the abstract is searched for indicators for a specific MeSH recommendation in a two-step process. Supervised machine learning combined with triage rules improves sensitivity of recommendations while keeping the number of recommended terms relatively small. Improvement in recommendations observed in this work warrants further exploration of this approach to MTI recommendations on a larger set of MeSH headings.

Publication types

  • Research Support, N.I.H., Intramural
  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Abstracting and Indexing / methods*
  • Algorithms
  • Artificial Intelligence*
  • Carbohydrate Sequence
  • Medical Subject Headings*
  • Natural Language Processing*
  • Unified Medical Language System