Evidence map›Paper›PMID 36463883›Full record

ArticleYearbook of medical informatics2022

Year 2021: COVID-19, Information Extraction and BERTization among the Hottest Topics in Medical Natural Language Processing.

Natalia Grabar, Cyril Grouin

Abstract read
In one paragraph

Article in Yearbook of medical informatics, 2022. 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. Inclusive Digital Health.Yearbook of medical informatics · 2022
    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

2 authors.

Natalia GrabarSTL, CNRS, Université de Lille, Domaine du Pont-de-bois, Villeneuve-d'Ascq cedex, France.
Cyril GrouinUniversité Paris Saclay, CNRS, Laboratoire Interdisciplinaire des Sciences du Numérique, Orsay, France.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesAnalyze the content of publications within the medical natural language processing (NLP) domain in 2021.

methodsAutomatic and manual preselection of publications to be reviewed, and selection of the best NLP papers of the year. Analysis of the important issues.

resultsFour best papers have been selected in 2021. We also propose an analysis of the content of the NLP publications in 2021, all topics included.

conclusionsThe main issues addressed in 2021 are related to the investigation of COVID-related questions and to the further adaptation and use of transformer models. Besides, the trends from the past years continue, such as information extraction and use of information from social networks.

Indexed as

COVID-19Natural Language ProcessingHumansInformation Storage and RetrievalSocial Networking

Identifiers

PMID36463883
PMCPMC9719758

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.