Evidence map›Paper›PMID 32712157›Full record

ArticleJournal of biomedical informatics2020

A computational ecosystem to support eHealth Knowledge Discovery technologies in Spanish.

Alejandro Piad-Morffis, Yoan Gutiérrez, Yudivian Almeida-Cruz, Rafael Muñoz

Abstract read
In one paragraph

Article in Journal of biomedical informatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. 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

4 authors.

Alejandro Piad-MorffisFaculty of Math and Computer Science, University of Habana, La Habana 10200, Cuba. Electronic address: apiad@matcom.uh.cu.
Yoan GutiérrezUniversity Institute for Computing Research (IUII), University of Alicante, Alicante 03690, Spain; Department of Language and Computing Systems, University of Alicante, Alicante 03690, Spain. Electronic address: ygutierrez@dlsi.ua.es.
Yudivian Almeida-CruzFaculty of Math and Computer Science, University of Habana, La Habana 10200, Cuba. Electronic address: yudy@matcom.uh.cu.
Rafael MuñozUniversity Institute for Computing Research (IUII), University of Alicante, Alicante 03690, Spain; Department of Language and Computing Systems, University of Alicante, Alicante 03690, Spain. Electronic address: rafael@dlsi.ua.es.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The massive amount of biomedical information published online requires the development of automatic knowledge discovery technologies to effectively make use of this available content. To foster and support this, the research community creates linguistic resources, such as annotated corpora, and designs shared evaluation campaigns and academic competitive challenges. This work describes an ecosystem that facilitates research and development in knowledge discovery in the biomedical domain, specifically in Spanish language. To this end, several resources are developed and shared with the research community, including a novel semantic annotation model, an annotated corpus of 1045 sentences, and computational resources to build and evaluate automatic knowledge discovery techniques. Furthermore, a research task is defined with objective evaluation criteria, and an online evaluation environment is setup and maintained, enabling researchers interested in this task to obtain immediate feedback and compare their results with the state-of-the-art. As a case study, we analyze the results of a competitive challenge based on these resources and provide guidelines for future research. The constructed ecosystem provides an effective learning and evaluation environment to encourage research in knowledge discovery in Spanish biomedical documents.

Indexed as

Knowledge DiscoveryTelemedicineEcosystemLanguageNatural Language ProcessingSemanticsAnnotated corporaEntity recognitionKnowledge discoveryNatural language processingRelation extractionSemantic annotation models

Identifiers

PMID32712157
PMCPMC7377985

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.