Natural language word embeddings as a glimpse into healthcare language and associated mortality surrounding end of life.
BMJ Health Care Inform
; 28(1)2021 Oct.
Article
em En
| MEDLINE
| ID: mdl-34711578
ABSTRACT
OBJECTIVES:
To 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).DESIGN:
Retrospective cross-sectional study of real-world clinical data.SETTING:
Secondary care, urban and suburban teaching hospitals.PARTICIPANTS:
All inpatients in 12-month period from 1 October 2018 to 30 September 2019.METHODS:
Using 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.RESULTS:
Word 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%).CONCLUSION:
Vocabulary 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.Palavras-chave
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Processamento de Linguagem Natural
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Morte
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Atenção à Saúde
Idioma:
En
Ano de publicação:
2021
Tipo de documento:
Article