Evidence map›Paper›PMID 37332929›Full record

ArticleHeliyon2023

Quality of word and concept embeddings in targetted biomedical domains.

Salvatore Giancani, Riccardo Albertoni, Chiara Eva Catalano

Abstract read
In one paragraph

Article in Heliyon, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Salvatore GiancaniInstitut de Neurosciences de la Timone, Unité Mixte de Recherche 7289 Centre National de la Recherce Scientifique and Aix-Marseille Université, Faculty of Medicine, 27, Boulevard Jean Moulin, 13385 Marseille Cedex 05, France.
Riccardo AlbertoniIstituto di Matematica Applicata e Tecnologie Informatiche, Consiglio Nazionale delle Ricerche, Via De Marini 16, 16149 Genova, Italy.
Chiara Eva CatalanoIstituto di Matematica Applicata e Tecnologie Informatiche, Consiglio Nazionale delle Ricerche, Via De Marini 16, 16149 Genova, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Embeddings are fundamental resources often reused for building intelligent systems in the biomedical context. As a result, evaluating the quality of previously trained embeddings and ensuring they cover the desired information is critical for the success of applications. This paper proposes a new evaluation methodology to test the coverage of embeddings against a targetted domain of interest. It defines measures to assess the terminology, similarity, and analogy coverage, which are core aspects of the embeddings. Then, it discusses the experimentation carried out on existing biomedical embeddings in the specific context of pulmonary diseases. The proposed methodology and measures are general and may be applied to any application domain.

Indexed as

Chronic obstructive pulmonary diseaseCoverageEmbeddingQualityUMLS

Identifiers

PMID37332929
PMCPMC10272317

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.