Evidence map›Paper›PMID 33355359›Full record

ArticleJournal of the American Medical Informatics Association : JAMIA2021

Enhancing filter-based parenthetic abbreviation extraction methods.

Houcemeddine Turki, Mohamed Ali Hadj Taieb, Mohamed Ben Aouicha

Abstract readLetter
In one paragraph

Article in Journal of the American Medical Informatics Association : JAMIA, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

3 authors.

Houcemeddine TurkiFaculty of Medicine of Sfax, University of Sfax, Tunisia.
Mohamed Ali Hadj TaiebData Engineering and Semantics Research Unit, Faculty of Sciences of Sfax, University of Sfax, Tunisia.
Mohamed Ben AouichaData Engineering and Semantics Research Unit, Faculty of Sciences of Sfax, University of Sfax, Tunisia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This letter discusses the limitations of the use of filters to enhance the accuracy of the extraction of parenthetic abbreviations from scholarly publications and proposes the usage of the parentheses level count algorithm to efficiently extract entities between parentheses from raw texts as well as of machine learning-based supervised classification techniques for the identification of biomedical abbreviations to significantly reduce the removal of acronyms including disallowed punctuations.

Indexed as

Abbreviations as TopicAlgorithmsInformation Storage and RetrievalMachine Learningacronym extractiondata mininginformation retrievalparenthetic acronym

Identifiers

PMID33355359
PMCPMC7936512

What OpenQuestion holds

Textmetadata
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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.