New marker for chronic kidney disease progression and mortality in medical-word virtual space.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
18 Jan 2024
18 Jan 2024
Historique:
received:
07
04
2023
accepted:
16
01
2024
medline:
19
1
2024
pubmed:
19
1
2024
entrez:
18
1
2024
Statut:
epublish
Résumé
A new marker reflecting the pathophysiology of chronic kidney disease (CKD) has been desired for its therapy. In this study, we developed a virtual space where data in medical words and those of actual CKD patients were unified by natural language processing and category theory. A virtual space of medical words was constructed from the CKD-related literature (n = 165,271) using Word2Vec, in which 106,612 words composed a network. The network satisfied vector calculations, and retained the meanings of medical words. The data of CKD patients of a cohort study for 3 years (n = 26,433) were transformed into the network as medical-word vectors. We let the relationship between vectors of patient data and the outcome (dialysis or death) be a marker (inner product). Then, the inner product accurately predicted the outcomes: C-statistics of 0.911 (95% CI 0.897, 0.924). Cox proportional hazards models showed that the risk of the outcomes in the high-inner-product group was 21.92 (95% CI 14.77, 32.51) times higher than that in the low-inner-product group. This study showed that CKD patients can be treated as a network of medical words that reflect the pathophysiological condition of CKD and the risks of CKD progression and mortality.
Identifiants
pubmed: 38238488
doi: 10.1038/s41598-024-52235-9
pii: 10.1038/s41598-024-52235-9
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
1661Subventions
Organisme : Kawasaki Medical School
ID : Research Project R05B005
Organisme : The Japan Society for the Promotion of Science
ID : KAKENHI JP 22K08346
Informations de copyright
© 2024. The Author(s).
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