RT Journal Article
SR Electronic
T1 Natural language word embeddings as a glimpse into healthcare language and associated mortality surrounding end of life
JF BMJ Health & Care Informatics
JO BMJ Health Care Inform
FD BMJ Publishing Group Ltd
SP e100464
DO 10.1136/bmjhci-2021-100464
VO 28
IS 1
A1 Ivan Shun Lau
A1 Zeljko Kraljevic
A1 Mohammad Al-Agil
A1 Shelley Charing
A1 Alan Quarterman
A1 Harold Parkes
A1 Victoria Metaxa
A1 Katherine Sleeman
A1 Wei Gao
A1 Richard J B Dobson
A1 James T Teo
A1 Phil Hopkins
YR 2021
UL http://informatics.bmj.com/content/28/1/e100464.abstract
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.