Evidence map›Paper›PMID 34868401›Full record

ArticleProcedia computer science2021

An Overview of Biomedical Ontologies for Pandemics and Infectious Diseases Representation.

Leila Bayoudhi, Najla Sassi, Wassim Jaziri

Abstract read
In one paragraph

Article in Procedia computer science, 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

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

3 authors.

Leila BayoudhiMiracl Laboratory, Universiy of Sfax, Sfax, Tunisia.
Najla SassiMiracl Laboratory, Universiy of Sfax, Sfax, Tunisia.
Wassim JaziriMiracl Laboratory, Universiy of Sfax, Sfax, Tunisia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Several infectious diseases and pandemics have so far emerged. Pandemics are by nature rapidly evolving. In this context, COVID-19 cases, seen recently in a growing number of countries around the world, have been increasing exponentially. So, researchers and responsible actors should take quick decisions to mitigate the spread of such diseases. To do so, several computer science solutions, including ontologies, have been proposed to cope with these issues and save humanity. The ontology is the key formalism which allows modelling knowledge along with its semantics in a formal way. Indeed, the ontology provides unambiguous definitions of a discourse's domain terms in a machine understandable way. Particularly, biomedical ontologies have ever been developed to capture and represent pandemics and infectious diseases. In this context, this paper aims to scrutinize and study these state-of-the-art ontologies.

Indexed as

BiomedicalInfectious diseasesOntologyPandemicsRepresentation

Identifiers

PMID34868401
PMCPMC8629358

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.