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 interpretable longitudinal preeclampsia risk prediction using machine learning

10 Jun 2025

Abstract excerpt

Preeclampsia is a pregnancy-specific disease characterized by new onset hypertension after 20 weeks of gestation that affects 2-8% of all pregnancies and contributes to up to 26% of maternal deaths. Despite extensive clinical research, current predictive tools fail to identify up to 66% of patients who develop preeclampsia. We sought to develop a tool to longitudinally predict preeclampsia risk. In this...

Read the complete abstract on PubMed

Topics

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

Share to Birth, Development, and Health
Share to Gestational Diabetes Research and Management
Share to Pregnancy and preeclampsia studies

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Source

Identifiers

PMID: 40493626
DOI: 10.1371/journal.pone.0323873

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

Development and validation of an interpretable longitudinal preeclampsia risk prediction using machine learningPMID 40493626Read ↗

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

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

Deep Survival Analysis for Interpretable Time-Varying Prediction of Preeclampsia RiskSemantic proximity Read ↗

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

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

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

Improving postpartum hemorrhage risk prediction using longitudinal electronic medical recordsSemantic proximity Read ↗

Machine learning for the prediction of spontaneous preterm birth using early second and third trimester maternal blood gene expression: A Cautionary TaleSemantic proximity Read ↗

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

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

Prediction model development of late-onset preeclampsia using machine learning-based methodsSemantic proximity Read ↗

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