Evidence map›Paper›PMID 32823319›Full record

ReviewYearbook of medical informatics2020

A Year of Papers Using Biomedical Texts.

Cyril Grouin, Natalia Grabar, Section Editors for the IMIA Yearbook Section on Natural Language Processing

Abstract readReview
In one paragraph

Review in Yearbook of medical informatics, 2020. 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.

Cyril GrouinUniversité Paris-Saclay, CNRS, LIMSI, Orsay, France.
Natalia GrabarUniversité Paris-Saclay, CNRS, LIMSI, Orsay, France.
Section Editors for the IMIA Yearbook Section on Natural Language Processing

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesAnalyze papers published in 2019 within the medical natural language processing (NLP) domain in order to select the best works of the field.

methodsWe performed an automatic and manual pre-selection of papers to be reviewed and finally selected the best NLP papers of the year. We also propose an analysis of the content of NLP publications in 2019.

resultsThree best papers have been selected this year including the generation of synthetic record texts in Chinese, a method to identify contradictions in the literature, and the BioBERT word representation.

conclusionsThe year 2019 was very rich and various NLP issues and topics were addressed by research teams. This shows the will and capacity of researchers to move towards robust and reproducible results. Researchers also prove to be creative in addressing original issues with relevant approaches.

Indexed as

Electronic Health RecordsNatural Language ProcessingData MiningInformation Storage and Retrieval

Identifiers

PMID32823319
PMCPMC7442503

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.