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Identification of chronic urticaria subtypes using machine learning algorithms.

Authors :
Türk, Murat
Ertaş, Ragıp
Zeydan, Engin
Türk, Yekta
Atasoy, Mustafa
Gutsche, Annika
Maurer, Marcus
Source :
Allergy. Jan2022, Vol. 77 Issue 1, p323-326. 4p.
Publication Year :
2022

Abstract

Keywords: chronic urticarial; cluster analysis; endotype; machine learning; phenotype EN chronic urticarial cluster analysis endotype machine learning phenotype 323 326 4 12/31/21 20220101 NES 220101 ACKNOWLEDGEMENTS This project benefitted from the support (non-financial) of the GA SP 2 sp LEN network of urticaria centers of reference and excellence (UCARE, www.ga2len-ucare.com). The results of our study provide proof of concept that the use of unsupervised ML algorithms can identify meaningful and distinct groups of patients with CU and cluster CU into four different and distinct subtypes. [Extracted from the article]

Subjects

Subjects :
*URTICARIA
*MACHINE learning

Details

Language :
English
ISSN :
01054538
Volume :
77
Issue :
1
Database :
Academic Search Index
Journal :
Allergy
Publication Type :
Academic Journal
Accession number :
154389766
Full Text :
https://doi.org/10.1111/all.15119