@CONFERENCE\{IMM2011-06016,
    author       = "A. Jimeno-Yepes and B. Wilkowski and J. G. Mork and E. Van Lenten and D. Demner Fushman and A. R. Aronson",
    title        = "A bottom-up approach to {MEDLINE} indexing recommendations",
    year         = "2011",
    month        = "mar",
    keywords     = "MeSH, indexing, {MTI,} machine learning",
    booktitle    = "{AMIA} 2011",
    volume       = "",
    series       = "",
    editor       = "",
    publisher    = "",
    organization = "",
    address      = "",
    url          = "http://www2.compute.dtu.dk/pubdb/pubs/6016-full.html",
    abstract     = "{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. In the first step, a rule-based triage significantly
reduces the number of candidate citations to which the MeSH heading is recommended. In the second step, the
candidate citation list is further reduced using supervised machine learning. 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."
}