Evidence map›Paper›PMID 39588066›Full record

ArticleNatural language engineering2024

Lightweight transformers for clinical natural language processing.

Omid Rohanian, Mohammadmahdi Nouriborji, Hannah Jauncey, Samaneh Kouchaki, Farhad Nooralahzadeh, ISARIC Clinical Characterisation Group, Lei Clifton, Laura Merson, David A Clifton

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Natural language engineering, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. 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

9 authors.

Omid RohanianDepartment of Engineering Science, University of Oxford, Oxford, UK.ORCID https://orcid.org/0000-0002-8771-8386
Mohammadmahdi NouriborjiNLPie Research, Oxford, UK.
Hannah JaunceyInfectious Diseases Data Observatory (IDDO), University of Oxford, Oxford, UK.
Samaneh KouchakiDepartment of Electrical and Electronic Engineering, University of Surrey, Guildford, UK.
Farhad NooralahzadehUniversity of Zürich, Zürich, Switzerland.
ISARIC Clinical Characterisation Group
Lei CliftonNuffield Department of Population Health, University of Oxford, Oxford, UK.
Laura MersonISARIC, Pandemic Sciences Institute, University of Oxford, Oxford, UK.
David A CliftonDepartment of Engineering Science, University of Oxford, Oxford, UK.

Funding

Medical Research Council MC_PC_19059
6 · The paper itself

Abstract

Specialised pre-trained language models are becoming more frequent in Natural language Processing (NLP) since they can potentially outperform models trained on generic texts. BioBERT (Sanh et al., Distilbert, a distilled version of bert: smaller, faster, cheaper and lighter.

Indexed as

Machine learningnatural language processing for biomedical texts

Identifiers

PMID39588066
PMCPMC11586117

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.