Machine learning: A modern approach to pediatric asthma-Reference-Cited by-同舟云学术

Machine learning: A modern approach to pediatric asthma

Published:2022-01 Issue:S27 Volume:33 Page:34-37
ISSN:0905-6157
Container-title:Pediatric Allergy and Immunology
language:en
Short-container-title:Pediatric Allergy Immunology

Author:

Cilluffo Giovanna¹^ORCID,Fasola Salvatore¹^ORCID,Ferrante Giuliana²^ORCID,Licari Amelia³⁴^ORCID,Marseglia Giuseppe Roberto⁵^ORCID,Albarelli Andrea⁵^ORCID,Marseglia Gian Luigi³⁴^ORCID,La Grutta Stefania¹^ORCID

Affiliation:

1. Institute for Biomedical Research and Innovation National Research Council Palermo Italy

2. Department of Health Promotion, Mother and Child Care Internal Medicine and Medical Specialties University of Palermo Palermo Italy

3. Pediatric Clinic Fondazione IRCCS Policlinico San Matteo Pavia Italy

4. Department of Clinical‐Surgical, Diagnostic and Pediatric Sciences University of Pavia Pavia Italy

5. Department of Environmental Sciences, Informatics and Statistics Ca' Foscari University Venice Italy

Publisher

Wiley

Subject

Immunology,Immunology and Allergy,Pediatrics, Perinatology and Child Health

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1111/pai.13624

Reference10 articles.

1. Features of asthma which provide meaningful insights for understanding the disease heterogeneity

2. Childhood asthma clusters reveal neutrophil‐predominant phenotype with distinct gene expression

3. Heterogeneity of Mild to Moderate Persistent Asthma in Children: Confirmation by Latent Class Analysis and Association with 1-Year Outcomes

4. Heterogeneity of severe asthma in childhood: Confirmation by cluster analysis of children in the National Institutes of Health/National Heart, Lung, and Blood Institute Severe Asthma Research Program

5. Two novel, severe asthma phenotypes identified during childhood using a clustering approach

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