Evidence map›Paper›PMID 24729747›Full record

ArticleJournal of the American Society for Information Science and Technology : JASIST2013

Extending SemRep to the Public Health Domain.

Graciela Rosemblat, Melissa P Resnick, Ione Auston, Dongwook Shin, Charles Sneiderman, Marcelo Fizsman, Thomas C Rindflesch

Abstract read
In one paragraph

Article in Journal of the American Society for Information Science and Technology : JASIST, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 1 pooled it
–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

7 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. A methodology for extending domain coverage in SemRep.Journal of biomedical informatics · 2013
    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

7 authors.

Graciela RosemblatNational Library of Medicine, NIH, Bethesda, MD.
Melissa P ResnickNational Library of Medicine, NIH, Bethesda, MD.
Ione AustonNational Library of Medicine, NIH, Bethesda, MD.
Dongwook ShinNational Library of Medicine, NIH, Bethesda, MD.
Charles SneidermanNational Library of Medicine, NIH, Bethesda, MD.
Marcelo FizsmanNational Library of Medicine, NIH, Bethesda, MD.
Thomas C RindfleschNational Library of Medicine, NIH, Bethesda, MD.

Funding

Intramural NIH HHS Z99 LM999999
6 · The paper itself

Abstract

We describe the use of a domain-independent methodology to extend a natural language processing (NLP) application, SemRep (Rindflesch, Fiszman, & Libbus, 2005), based on the knowledge sources afforded by the Unified Medical Language System (UMLS®) (Humphreys, Lindberg, Schoolman, & Barnett, 1998) to support the area of health promotion within the public health domain. Public health professionals require good information about successful health promotion policies and programs that might be considered for application within their own communities. Our effort seeks to improve access to relevant information for the public health profession, to help those in the field remain an information-savvy workforce. NLP and semantic techniques hold promise to help public health professionals navigate the growing ocean of information by organizing and structuring this knowledge into a focused public health framework paired with a user-friendly visualization application as a way to summarize results of PubMed searches in this field of knowledge.

Indexed as

Health PromotionNatural Language ProcessingPublic HealthSemantic PredicationsSemi-Automated Ontology Development

Identifiers

PMID24729747
PMCPMC3981099

What OpenQuestion holds

Textmetadata
LicenceTDM
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.