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Article

Identification of severe acute pediatric asthma phenotypes using unsupervised machine learning.

Pediatric pulmonology - 1 Dec 2024

Rogerson Colin, Nelson Sanchez-Pinto L, Gaston Benjamin, Wiehe Sarah, Schleyer Titus, Tu Wanzhu, Mendonca Eneida

Abstract excerpt

RATIONALE: More targeted management of severe acute pediatric asthma could improve clinical outcomes. OBJECTIVES: To identify distinct clinical phenotypes of severe acute pediatric asthma using variables obtained in the first 12 h of hospitalization. METHODS: We conducted a retrospective cohort study in a quaternary care children's hospital from 2014 to 2022. Encounters for children ages 2-18 years admitted to...

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Identifiers

PMID: 39073377
DOI: 10.1002/ppul.27197
PMCID: PMC11601023

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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.

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