Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

Machine learning: A modern approach to pediatric asthma.

Pediatric allergy and immunology : official publication of the European Society of Pediatric Allergy and Immunology - 1 Jan 2022

Cilluffo Giovanna, Fasola Salvatore, Ferrante Giuliana, Licari Amelia, Marseglia Giuseppe Roberto, Albarelli Andrea, Marseglia Gian Luigi, La Grutta Stefania

Abstract excerpt

Among modern methods of statistical and computational analysis, the application of machine learning (ML) to healthcare data has been gaining recognition in helping us understand the heterogeneity of asthma and predicting its progression. In pediatric research, ML approaches may provide rapid advances in uncovering asthma phenotypes with potential translational impact in clinical practice. Also, several accurate...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Asthma
Share to Child

Methodology Data sources Editorial policy Privacy Delete account & data About Contact Helena Bioinformatics

Share to Machine Learning

Share to Phenotype

Source

Open in PubMed Open full text

Identifiers

PMID: 35080316
DOI: 10.1111/pai.13624
PMCID: PMC9303472

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026. Manifest publication-graph-cohort-2026-09-08.1.

Source receipt: pubmed, retrieved 7 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence. Graph snapshot: 2026-09-29.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Machine learning: A modern approach to pediatric asthma.PMID 35080316Read ↗

Distinguishing Asthma Phenotypes Using Machine Learning Approaches.Semantic proximity Read ↗

The evolution of scientific knowledge in childhood asthma over time.Semantic proximity Read ↗

Novel Machine Learning Identifies 5 Asthma Phenotypes Using Cluster Analysis of Real-World Data.Semantic proximity Read ↗

Machine learning-derived asthma phenotypes in a representative Swedish adult populationSemantic proximity Read ↗

Understanding progression from pre-school wheezing to school-age asthma: Can modern data approaches help?Semantic proximity Read ↗

Development of Childhood Asthma Prediction Models using Machine Learning ApproachesSemantic proximity Read ↗

Identification of severe acute pediatric asthma phenotypes using unsupervised machine learning.Semantic proximity Read ↗

Phenotype and endotype based treatment of preschool wheeze.Semantic proximity Read ↗

Discovering Pediatric Asthma Phenotypes on the Basis of Response to Controller Medication Using Machine Learning.Semantic proximity Read ↗

Machine learning-derived phenotypic trajectories of asthma and allergy in children and adolescents: protocol for a systematic review.Semantic proximity Read ↗

Pediatric and adult asthma clinical phenotypes: a real world, big data study based on acute exacerbations.Semantic proximity Read ↗

COMPAC: COMputable Phenotype for Asthma in ChildrenSemantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.