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Natural Language Word-Embeddings as a glimpse into healthcare language and associated mortality surrounding End Of Life

Research output: Contribution to journalArticlepeer-review

Ivan Lau, Zeljko Kraljevic, Mohammad Al-Agil, Shelley Charing, Alan Quarterman, Harold Parkes, Victoria Metaxa, Katherine Sleeman, Wei Gao, Richard Dobson, James Teo, Phil Hopkins

Original languageEnglish
JournalBMJ health & care informatics
Accepted/In press8 Oct 2021

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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 1st October 2018 to 30th
September 2019.
Methods: Using unsupervised natural language processing (NLP), word embedding
in latent space were 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.

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