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

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 investigating the phenotype among inpatients and outpatients with AD from the Department of Dermatology and Allergy of the University Hospital Bonn enrolled between November 2016 and February 2020.

Main outcomes and measures

Patients were stratified by severity groups using

SUBMITTER: Maintz L 

PROVIDER: S-EPMC8581798 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature