Documentation of Palliative and End-of-Life Care Process Measures Among Young Adults Who Died of Cancer: A Natural Language Processing Approach

2020 ◽  
Vol 9 (1) ◽  
pp. 100-104 ◽  
Author(s):  
Hanneke Poort ◽  
Sophia N. Zupanc ◽  
Richard E. Leiter ◽  
Alexi A. Wright ◽  
Charlotta Lindvall
Keyword(s):  
Young Adults ◽  
Natural Language Processing ◽  
Natural Language ◽  
End Of Life ◽  
Language Processing ◽  
End Of Life Care ◽  
Care Process ◽  
Life Care ◽  
Process Measures ◽  
Processing Approach

scholarly journals Natural Language Word-Embeddings as a glimpse into healthcare at the End Of Life

2021 ◽  
Author(s):  
Ivan Shun Lau ◽  
Zeljko Kraljevic ◽  
Mohammad Al Agil ◽  
Shelley Charing ◽  
Alan Quarterman ◽  
...  
Keyword(s):  
Natural Language ◽  
End Of Life ◽  
Language Processing ◽  
Real World ◽  
End Of Life Care ◽  
Word Embeddings ◽  
Life Care ◽  
Individual Level ◽  
Life Sustaining Treatment ◽  
Prognostic Profile

Introduction: Planning in advance and personalised discussions on limitation of life sustaining treatment (LST) is an indicator of good care. However, there are many linguistic nuances and misunderstandings around dying in hospital as well as inaccuracy in individual-level prognostication. Methods: Using unsupervised natural language processing (NLP), we explored real-world terminology using phrase clusters with most similar sematic embeddings to 'Ceiling of Treatment' and their prognostication value in the electronic health record of an urban teaching hospital. Results: Word embeddings with most similar to 'Ceiling of Treatment' clustered around phrases describing end-of-life care, ceiling of care and resuscitation discussions. The phrases have differing prognostic profile with the highest 7-day mortality in the phrases most implicitly referring to end of life - 'terminal care', 'end of life care' (57.5%) and 'unsurvivable' (57.6%). Conclusion: NLP can quantify and analyse real-world end of life discussions around prognosis and appropriate LST.

scholarly journals Creating research topic map for NIMS SAMURAI database using natural language processing approach

2021 ◽  
Vol 1 (1) ◽  
pp. 2-11
Author(s):  
Sae Dieb ◽  
Kou Amano ◽  
Kosuke Tanabe ◽  
Daitetsu Sato ◽  
Masashi Ishii ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Research Topic ◽  
Processing Approach ◽  
Topic Map
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