Evidence map›Paper›PMID 34711578›Full record

ArticleBMJ health & care informatics2021

Natural language word embeddings as a glimpse into healthcare language and associated mortality surrounding end of life.

Ivan Shun Lau, Zeljko Kraljevic, Mohammad Al-Agil, Shelley Charing, Alan Quarterman, Harold Parkes, Victoria Metaxa, Katherine Sleeman, Wei Gao, Richard J B Dobson and 2 more

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMJ health & care informatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.1field-weighted citation impact, top 43% of its field
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

2 citing papers in PubMed, 5 citations in OpenAlex.

  1. Review
  2. 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

12 authors at 3 institutions in 1 country.

Ivan Shun LauKings College Hospital, King's College Hospital NHS Foundation Trust, London, UK.
Zeljko KraljevicDepartment of Biostatistics and Health Informatics, King's College London, London, UK.
Mohammad Al-AgilKings College Hospital, King's College Hospital NHS Foundation Trust, London, UK.
Shelley CharingPatients, (Private Individuals), London, UK.
Alan QuartermanPatients, (Private Individuals), London, UK.
Harold ParkesPatients, (Private Individuals), London, UK.
Victoria MetaxaKings College Hospital, King's College Hospital NHS Foundation Trust, London, UK.
Katherine SleemanDepartment of Palliative Care, Policy and Rehabilitation, King's College London, London, UK.
Wei GaoDepartment of Palliative Care, Policy and Rehabilitation, King's College London, London, UK.ORCID http://orcid.org/0000-0001-8298-3415
Richard J B DobsonDepartment of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
James T TeoKings College Hospital, King's College Hospital NHS Foundation Trust, London, UK jamesteo@nhs.net.ORCID http://orcid.org/0000-0002-6899-8319
Phil HopkinsIntensive Care Medicine, Anaesthesia and Trauma, King's College Hospital NHS Foundation Trust, London, UK.
King's College London · GBKing's College Hospital NHS Foundation Trust · GBThe Patients Association · GB

Funding

Medical Research Council MC_PC_17214
6 · The paper itself

Abstract

objectivesTo clarify real-world linguistic nuances around dying in hospital as well as inaccuracy in individual-level prognostication to support advance care planning and personalised discussions on limitation of life sustaining treatment (LST).

designRetrospective cross-sectional study of real-world clinical data.

settingSecondary care, urban and suburban teaching hospitals.

participantsAll inpatients in 12-month period from 1 October 2018 to 30 September 2019.

methodsUsing unsupervised natural language processing, word embedding in latent space was used to generate phrase clusters with most similar semantic embeddings to 'Ceiling of Treatment' and their prognostication value.

resultsWord embeddings with most similarity to 'Ceiling of Treatment' clustered around phrases describing end-of-life care, ceiling of care and LST discussions. The phrases have differing prognostic profile with the highest 7-day mortality in the phrases most explicitly referring to end of life-'Withdrawal of care' (56.7%), 'terminal care/end of life care' (57.5%) and 'un-survivable' (57.6%).

conclusionVocabulary used at end-of-life discussions are diverse and has a range of associations to 7-day mortality. This highlights the importance of correct application of terminology during LST and end-of-life discussions.

Indexed as

DeathDelivery of Health CareNatural Language ProcessingCross-Sectional StudiesHumansRetrospective Studiesdata sciencedocumentationelectronic health recordsmedical recordspatient-centered care

Identifiers

PMID34711578
PMCPMC8557276
OpenAlexW3206071737

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.