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

Development of a Computable Phenotype for Prehospital Pediatric Asthma Encounters.

Prehospital emergency care - 1 Jan 2025

Harmon Ira, Brailsford Jennifer, Sanchez-Cano Isabel, Fishe Jennifer

Abstract excerpt

INTRODUCTION: Asthma exacerbations are a common cause of pediatric Emergency Medical Services (EMS) encounters. Accordingly, prehospital management of pediatric asthma exacerbations has been designated an EMS research priority. However, accurate identification of pediatric asthma exacerbations from the prehospital record is nuanced and difficult due to the heterogeneity of asthma symptoms, especially in children....

Read the complete abstract on PubMed

Topics

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

Share to Adolescent
Share to Asthma
Share to Child

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Child, Preschool

Share to CP gene

Share to Machine Learning

Share to Phenotype

Source

Open in PubMed Open full text

Identifiers

PMID: 38713633
DOI: 10.1080/10903127.2024.2352583
PMCID: PMC11579243

Genes

CP

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026. Manifest publication-graph-cohort-2026-09-07.1.

Source receipt: pubmed, retrieved 7 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Development of a Computable Phenotype for Prehospital Pediatric Asthma Encounters.PMID 38713633Read ↗

COMPAC: COMputable Phenotype for Asthma in ChildrenSemantic proximity Read ↗

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

Developing and evaluating a pediatric asthma severity computable phenotype derived from electronic health records.Semantic proximity Read ↗

Accuracy of Asthma Computable Phenotypes to Identify Pediatric Asthma at an Academic Institution.Semantic proximity Read ↗

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

A computable phenotype for asthma case identification in adult and pediatric patients: External validation in the Chicago Area Patient-Outcomes Research Network (CAPriCORN).Semantic proximity Read ↗

Machine learning reveals limited predictive value of clinical factors for asthma exacerbations: insights from a real-world studySemantic proximity Read ↗

A novel Computable phENotype To Identify Pulmonary Embolism in chilDrEn: The CENTIPEDE Study.Semantic proximity Read ↗

Machine learning: A modern approach to pediatric asthma.Semantic proximity Read ↗

Development and Validation of an Electronic Health Record-Based, Pediatric Acute Respiratory Distress Syndrome Subphenotype Classifier Model.Semantic proximity Read ↗

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

Integrating clinical phenotypes, impulse oscillometry and fractional exhaled nitric oxide: a robust machine learning model for early asthma prediction in preschool children.Semantic proximity Read ↗

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