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

Phenotypic subclassification of preeclampsia through cluster analysis of preterm birth-related factors.

BMC medical informatics and decision making - 6 Mar 2026

Liang Zewei, Xu Yanhong, Liu Xinying, Zhang Ying, Jin Chengcheng, Qi Xingyi, Xu Xia, Yan Jianying

Abstract excerpt

BACKGROUND: Preeclampsia is a multifactorial hypertensive disorder of pregnancy with heterogeneous clinical presentation and outcomes. Current classification systems inadequately capture the substantial clinical heterogeneity of preeclampsia. This study aimed to identify and characterize distinct phenotypic clusters of the disease using preterm birth-related risk factors. METHODS: This retrospective cohort study...

Read the complete abstract on PubMed

Topics

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

Share to Cluster Analysis
Share to Clustering Algorithms

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Phenotype

Share to Pre-Eclampsia

Share to Pregnancy

Share to Pregnancy Outcome

Share to Premature Birth

Share to Risk Factors

Source

Open in PubMed Open full text

Identifiers

PMID: 41792719
DOI: 10.1186/s12911-026-03405-4
PMCID: PMC13081625

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Phenotypic subclassification of preeclampsia through cluster analysis of preterm birth-related factors.PMID 41792719Read ↗

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

Subtypes of Preeclampsia: Recognition and Determining Clinical UsefulnessSemantic proximity Read ↗

Rethinking Risk Prediction in Preeclampsia: From Biomarkers to Mechanistic Phenotypes and Longitudinal Models.Semantic proximity Read ↗

Classification of preeclampsia according to molecular clusters with the goal of achieving personalized preventionSemantic proximity Read ↗

Prediction of Preeclampsia from Clinical and Genetic Risk Factors in Early and Late Pregnancy Using Machine Learning and Polygenic Risk ScoresSemantic proximity Read ↗

Genetic Risk Factors Associated With Preeclampsia and Hypertensive Disorders of PregnancySemantic proximity Read ↗

Molecular subclasses of preeclampsia characterized by a longitudinal maternal proteomics study: distinct biomarkers, disease pathways and options for preventionSemantic proximity Read ↗

An Interpretable Longitudinal Preeclampsia Risk Prediction Using Machine LearningSemantic proximity Read ↗

Predictive Factors for Fetal Growth Restriction in Patients with Preeclampsia: A Clinical Prediction StudySemantic proximity Read ↗

Identification and verification of diagnostic biomarkers based on mitochondria-related genes related to immune microenvironment for preeclampsia using machine learning algorithmsSemantic proximity Read ↗

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

The new era of risk assessment for hypertension in pregnancy: From clinical to biochemical markers in a comprehensive predictive modelSemantic proximity Read ↗

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