PT  - JOURNAL ARTICLE
AU  - Ivan Shun Lau
AU  - Zeljko Kraljevic
AU  - Mohammad Al-Agil
AU  - Shelley Charing
AU  - Alan Quarterman
AU  - Harold Parkes
AU  - Victoria Metaxa
AU  - Katherine Sleeman
AU  - Wei Gao
AU  - Richard J B Dobson
AU  - James T Teo
AU  - Phil Hopkins
TI  - Natural language word embeddings as a glimpse into healthcare language and associated mortality surrounding end of life
AID  - 10.1136/bmjhci-2021-100464
DP  - 2021 Oct 01
TA  - BMJ Health & Care Informatics
PG  - e100464
VI  - 28
IP  - 1
4099  - http://informatics.bmj.com/content/28/1/e100464.short
4100  - http://informatics.bmj.com/content/28/1/e100464.full
SO  - BMJ Health Care Inform2021 Oct 01; 28
AB  - 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.No data are available.