Cited by 18Dermatology and Skin DiseasesAsthma and respiratory diseasesArtificial Intelligence in Healthcare

Identifying Phenotypes of Atopic Dermatitis in a Longitudinal United States Cohort Using Unbiased Statistical Clustering

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Berna, Ronald, Mitra, Nandita, Hoffstad, Ole, Wan, Joy, Margolis, David J.·2020·Journal of Investigative Dermatology

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Crossref · 10.1016/j.jid.2019.08.432

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  • Unsupervised clustering of baseline clinical data from over 8000 children in the Pediatric Eczema Elective Registry identified 5 distinct AD subphenotypes, defined by factors such as race, age of onset, and allergy profiles, with statistically significant differences in long-term disease clearance.

    “Berna et al (2020) clustered baseline clinical data from over 8000 children in the Pediatric Eczema Elective Registry and identified 5 distinct AD subphenotypes, defined by factors such as race, age of onset, and allergy profiles, with statistically significant differences in long-term disease clearance.”

    From Artificial intelligence–enabled precision medicine for inflammatory skin diseases (2026) · cites this paper as Berna et al, 2020

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Authors
Berna, R., Mitra, N., Hoffstad, O., Wan, J., & Margolis, D. J.
Affiliations
University of Pennsylvania (US)
Corresponding author
Berna, Ronald
Venue
Journal of Investigative Dermatology
Publisher
Elsevier BV
Published
2020-02
Volume
140
Issue
2
Pages
477-479
ISSN
0022-202X (ISSN-L)
OpenAlex
W2969422761
Cited by
18
References
16
Citations by year
2026: 3 · 2025: 1 · 2024: 2 · 2023: 2 · 2022: 4 · 2021: 4 · 2020: 2
FWCI
2.47
Type
journal article
Language
en
Open access
Locations
  • Journal of Investigative Dermatology (publishedVersion)
  • PubMed (publishedVersion)
  • Europe PMC (PubMed Central) (acceptedVersion)
  • PubMed Central (acceptedVersion)
Funders
  • Valeant Pharmaceuticals International
  • National Institute of Arthritis and Musculoskeletal and Skin Diseases (P30-AR069589, R01-AR069062, T32-AR007465)
  • Pfizer
  • University of Pennsylvania (R01-AR060962, R01-AR070873, R01-DK116199)
  • Sunovion
Topics
  • Health Sciences › Medicine › Dermatology › Dermatology and Skin Diseases
  • Health Sciences › Medicine › Physiology › Asthma and respiratory diseases
  • Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
Keywords
atopic dermatitis, phenotypes, clustering, machine learning, longitudinal cohort, patient subgroups, skin disease classification
MeSH terms
Machine Learning, Child, Child, Preschool, Dermatitis, Atopic, Diagnosis, Computer-Assisted, Female, Humans, Infant, Infant, Newborn, Longitudinal Studies, Male, Phenotype, Registries, Severity of Illness Index, United States, Cluster Analysis, Age of Onset

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