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 and Validation of an Electronic Health Record-Based, Pediatric Acute Respiratory Distress Syndrome Subphenotype Classifier Model.

Pediatric critical care medicine : a journal of the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies - 1 May 2025

Balcarcel Daniel R, Mai Mark V, Mehta Sanjiv D, Chiotos Kathleen, Sanchez-Pinto L Nelson, Himes Blanca E, Yehya Nadir

Abstract excerpt

OBJECTIVE: To determine if hyperinflammatory and hypoinflammatory pediatric acute respiratory distress syndrome (PARDS) subphenotypes defined using serum biomarkers can be determined solely from electronic health record (EHR) data using machine learning. DESIGN: Retrospective, exploratory analysis using data from 2014 to 2022. SETTING: Single-center quaternary care PICU. PATIENTS: Two temporally distinct cohorts...

Read the complete abstract on PubMed

Topics

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

Share to Biomarkers
Share to Child

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Child, Preschool

Share to Electronic Health Records

Share to Infant

Share to Machine Learning

Share to Respiratory Distress Syndrome

Share to ROC Curve

Source

Open in PubMed Open full text

Identifiers

PMID: 40047501
DOI: 10.1097/PCC.0000000000003709
PMCID: PMC12061561

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 5 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Development and Validation of an Electronic Health Record-Based, Pediatric Acute Respiratory Distress Syndrome Subphenotype Classifier Model.PMID 40047501Read ↗

Clinical Subphenotypes of Multisystem Inflammatory Syndrome in Children: An EHR-based cohort study from the RECOVER programSemantic proximity Read ↗

A machine learning-based phenotype for long COVID in children: an EHR-based study from the RECOVER programSemantic proximity Read ↗

A machine learning-based phenotype for long COVID in children: An EHR-based study from the RECOVER program.Semantic proximity Read ↗

Development and validation of a federated learning framework for detection of subphenotypes of multisystem inflammatory syndrome in childrenSemantic proximity Read ↗

Identification of molecular subphenotypes in two cohorts of paediatric ARDS.Semantic proximity Read ↗

Phenotypic Classification of Multisystem Inflammatory Syndrome in Children: A Latent Class AnalysisSemantic proximity Read ↗

External validation and biomarker assessment of a high-risk, data-driven pediatric sepsis phenotype characterized by persistent hypoxemia, encephalopathy, and shock.Semantic proximity Read ↗

Admission inflammatory biomarker phenotypes and risk of severe community-acquired pneumonia and PARDS in young children.Semantic proximity Read ↗

Identification of phenotypes in paediatric patients with acute respiratory distress syndrome: a latent class analysis.Semantic proximity Read ↗

Biomarker Assessment of a High-Risk, Data-Driven Pediatric Sepsis Phenotype Characterized by Persistent Hypoxemia, Encephalopathy, and Shock.Semantic proximity Read ↗

Subphenotypes in acute respiratory distress syndrome: A scoping review across clinical, biological, computational, imaging, omics, and artificial intelligence approaches.Semantic proximity Read ↗

Stratification of Pediatric COVID-19 cases by inflammatory biomarker profiling and machine learningSemantic proximity Read ↗

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