Evidence map›Paper›PMID 22195224›Full record

ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2011

A bottom-up approach to MEDLINE indexing recommendations.

Antonio Jimeno-Yepes, Bartłomiej Wilkowski, James G Mork, Elizabeth Van Lenten, Dina Demner Fushman, Alan R Aronson

Abstract read
In one paragraph

Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Comparison and combination of several MeSH indexing approaches.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2013
    Article
  5. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Antonio Jimeno-YepesNational Library of Medicine, 8600 Rockville Pike, Bethesda, MD 20894, USA.
Bartłomiej Wilkowski
James G Mork
Elizabeth Van Lenten
Dina Demner Fushman
Alan R Aronson

Funding

Intramural NIH HHS
6 · The paper itself

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. 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.

Indexed as

Artificial IntelligenceMedical Subject HeadingsMEDLINENatural Language ProcessingAbstracting and IndexingAlgorithmsCarbohydrate SequenceUnified Medical Language System

Identifiers

PMID22195224
PMCPMC3243198

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.