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

The new era of risk assessment for hypertension in pregnancy: From clinical to biochemical markers in a comprehensive predictive model

1 Mar 2025

Abstract excerpt

OBJECTIVE: This study aims to develop and validate a model based on the weighted random forest (WRF) algorithm to predict early-onset preeclampsia (PE) and to assess the importance of various clinical and biochemical markers in early risk identification. MATERIALS AND METHODS: This study was conducted at the Jiangxi Maternal and Child Health Hospital and involved 12,699 pregnant women from January 2019 to June...

Read the complete abstract on PubMed

Topics

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

Share to Gestational Diabetes Research and Management
Share to Neonatal and fetal brain pathology
Share to Pregnancy and preeclampsia studies

Methodology Data sources Editorial policy Privacy Delete account & data About Contact Helena Bioinformatics

Source

Identifiers

PMID: 40049809
DOI: 10.1016/j.tjog.2024.10.014

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 22 Oct 2026.

Related research

Semantic proximity does not establish scientific evidence. Graph snapshot: 2026-09-29.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

The new era of risk assessment for hypertension in pregnancy: From clinical to biochemical markers in a comprehensive predictive modelPMID 40049809Read ↗

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

Early Prediction of Hypertensive Disorders of Pregnancy Using Machine Learning and Medical Records from the First and Second TrimestersSemantic proximity Read ↗

Preeclampsia Prediction Using Machine Learning and Polygenic Risk Scores From Clinical and Genetic Risk Factors in Early and Late PregnanciesSemantic proximity Read ↗

A New Model for Screening for Late-Onset Preeclampsia in the Third TrimesterSemantic proximity Read ↗

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

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

Machine Learning for Dynamic and Short-term Prediction of Preeclampsia Using Routine Clinical and Laboratory DataSemantic proximity Read ↗

The Role of Mid-Trimester BUN and Creatinine Assessment in Predicting Preeclampsia: Retrospective Case-Control StudySemantic proximity Read ↗

Development and validation of an interpretable longitudinal preeclampsia risk prediction using machine learningSemantic proximity Read ↗

Improved Prediction Accuracy for Late-Onset Preeclampsia Using cfRNA Profiles: A Comparative Study of Marker Selection StrategiesSemantic proximity Read ↗

Associations of polygenic risk scores for preeclampsia and blood pressure with hypertensive disorders of pregnancy.Semantic proximity Read ↗

A Second Trimester Prediction Algorithm for Early-Onset Hypertensive Disorders of Pregnancy Occurrence and Severity Based on Soluble fms-like Tyrosine Kinase 1 (sFlt-1)/Placental Growth Factor (PlGF) Ratio and Uterine Doppler Ultrasound in Women at RiskSemantic proximity Read ↗

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