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Title:

Machine Learning-Based Deep Phenotyping of Atopic Dermatitis: Severity-Associated Factors in Adolescent and Adult Patients.

Document type:
Article; Journal Article; Research Support, Non-U.S. Gov't
Author(s):
Maintz, Laura; Welchowski, Thomas; Herrmann, Nadine; Brauer, Juliette; Kläschen, Anna Sophie; Fimmers, Rolf; Schmid, Matthias; Bieber, Thomas; Schmid-Grendelmeier, Peter; Traidl-Hoffmann, Claudia; Akdis, Cezmi; Lauener, Roger; Brüggen, Marie-Charlotte; Rhyner, Claudio; Bersuch, Eugen; Renner, Ellen; Reiger, Matthias; Dreher, Anita; Hammel, Gertrud; Luschkova, Daria; Lang, Claudia
Abstract:
Importance: Atopic dermatitis (AD) is the most common chronic inflammatory skin disease and is driven by a complex pathophysiology underlying highly heterogeneous phenotypes. Current advances in precision medicine emphasize the need for stratification. Objective: To perform deep phenotyping and identification of severity-associated factors in adolescent and adult patients with AD. Design, Setting, and Participants: Cross-sectional data from the baseline visit of a prospective longitudinal study...     »
Journal title abbreviation:
JAMA Dermatol
Year:
2021
Journal volume:
157
Journal issue:
12
Pages contribution:
1414-1424
Fulltext / DOI:
doi:10.1001/jamadermatol.2021.3668
Pubmed ID:
http://view.ncbi.nlm.nih.gov/pubmed/34757407
Print-ISSN:
2168-6068
TUM Institution:
Klinik und Poliklinik für Kinder- und Jugendmedizin
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