Predicting Clinical Remission of Chronic Urticaria Using Random Survival Forests: Machine Learning Applied to Real-World Data-Reference-Cited by-同舟云学术

Predicting Clinical Remission of Chronic Urticaria Using Random Survival Forests: Machine Learning Applied to Real-World Data

Published:2022-10-27 Issue:12 Volume:12 Page:2747-2763
ISSN:2193-8210
Container-title:Dermatology and Therapy
language:en
Short-container-title:Dermatol Ther (Heidelb)

Author:

Pivneva Irina,Balp Maria-Magdalena,Geissbühler Yvonne,Severin Thomas,Smeets Serge,Signorovitch James,Royer Jimmy,Liang Yawen,Cornwall Tom,Pan Jutong,Danyliv Andrii,McKenna Sarah Jane,Marsland Alexander M.,Soong Weily

Funder

Novartis Pharma AG, Switzerland

Publisher

Springer Science and Business Media LLC

Subject

Dermatology

Link

https://link.springer.com/content/pdf/10.1007/s13555-022-00827-6.pdf

Reference42 articles.

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2. Dressler C, Werner RN, Eisert L, et al. Chronic inducible urticaria: a systematic review of treatment options. J Allergy Clin Immunol. 2018;141(5):1726–34. https://doi.org/10.1016/j.jaci.2018.01.031 (PubMed PMID: 29438771).

3. Lavery WJ, Bernstein JA. Allergy and Asthma: acute and chronic urticaria. Mahmoudi M (ed). Springer, Cham. 2019.

4. Zuberbier T, Aberer W, Asero R, et al. The EAACI/GA(2)LEN/EDF/WAO guideline for the definition, classification, diagnosis and management of urticaria. Allergy. 2018;73(7):1393–414. https://doi.org/10.1111/all.13397 (PubMed PMID: 29336054).

5. Fricke J, Avila G, Keller T, et al. Prevalence of chronic urticaria in children and adults across the globe: systematic review with meta-analysis. Allergy. 2020;75(2):423–32. https://doi.org/10.1111/all.14037 (PubMed PMID: 31494963).

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