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

Identifying clinical phenotypes in extremely low birth weight infants-an unsupervised machine learning approach.

European journal of pediatrics - 1 Mar 2022

Matsushita Felipe Yu, Krebs Vera Lúcia Jornada, de Carvalho Werther Brunow

Abstract excerpt

There is increasing evidence that patient heterogeneity significantly hinders advancement in clinical trials and individualized care. This study aimed to identify distinct phenotypes in extremely low birth weight infants. We performed an agglomerative hierarchical clustering on principal components. Cluster validation was performed by cluster stability assessment with bootstrapping method. A total of 215 newborns...

Read the complete abstract on PubMed

Topics

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

Share to Birth Weight
Share to Gestational Age

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Infant, Newborn

Share to Neonatal Respiratory Health Research

Share to Neuroscience of respiration and sleep

Share to Phenotype

Share to Respiratory Support and Mechanisms

Share to Unsupervised Machine Learning

Source

Identifiers

PMID: 34734319
DOI: 10.1007/s00431-021-04298-3

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

Identifying clinical phenotypes in extremely low birth weight infants-an unsupervised machine learning approach.PMID 34734319Read ↗

Identifying Clinical Phenotypes in Extremely Low Birth Weight Infants – A Machine Learning ApproachSemantic proximity Read ↗

Clinical phenotypes for risk stratification in small-for-gestational-age fetuses.Semantic proximity Read ↗

The distribution of clinical phenotypes of preterm birth syndrome: implications for prevention.Semantic proximity Read ↗

Clustering and classical analysis of clinical and placental phenotypes in fetal growth restriction and constitutional fetal smallness.Semantic proximity Read ↗

Phenotypic subclassification of preeclampsia through cluster analysis of preterm birth-related factors.Semantic proximity Read ↗

Clinicoplacental phenotypes vary with gestational age: an analysis by classical and clustering methods.Semantic proximity Read ↗

Similarity network fusion to identify phenotypes of small-for-gestational-age fetusesSemantic proximity Read ↗

Clinical Subphenotypes and Short-Term Outcomes in PICU Patients Receiving Continuous Blood Purification.Semantic proximity Read ↗

Phenotyping Preeclampsia Using Unsupervised Machine Learning: A Prospective Cohort Study.Semantic proximity Read ↗

Transcriptomic mortality signature defines high-risk neonatal sepsis endotypeSemantic proximity Read ↗

Maternal Risk Factors and Short-Term Clinical Outcomes of Different Small Vulnerable Newborn (SVN) Phenotypes: A Retrospective Cohort Study of 998 CasesSemantic proximity Read ↗

Phenotypic Characteristics Associated with Preterm Births in the Indian PopulationSemantic proximity Read ↗

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