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

TY - JOUR

T1 - Natural language word embeddings as a glimpse into healthcare language and associated mortality surrounding end of life

AU - Lau, Ivan Shun

AU - Kraljevic, Zeljko

AU - Al-Agil, Mohammad

AU - Charing, Shelley

AU - Quarterman, Alan

AU - Parkes, Harold

AU - Metaxa, Victoria

AU - Sleeman, Katherine

AU - Gao, Wei

AU - Dobson, Richard J B

AU - Teo, James T

AU - Hopkins, Phil

N1 - © Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY. Published by BMJ.

PY - 2021/10

Y1 - 2021/10

N2 - 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.

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.

KW - Cross-Sectional Studies

KW - Death

KW - Delivery of Health Care/statistics & numerical data

KW - Humans

KW - Natural Language Processing

KW - Retrospective Studies

U2 - 10.1136/bmjhci-2021-100464

DO - 10.1136/bmjhci-2021-100464

M3 - Article

C2 - 34711578

SN - 2632-1009

VL - 28

JO - BMJ health & care informatics

JF - BMJ health & care informatics

IS - 1

ER -