Article

Early Prediction of Hypertensive Disorders of Pregnancy Using Machine Learning and Medical Records from the First and Second Trimesters

2024-11-25

Abstract excerpt

Hypertensive disorders of pregnancy (HDPs) remain a major challenge in maternal health. Early prediction of HDPs is crucial for timely intervention. Most existing predictive machine learning (ML) models rely on costly methods like blood, urine, genetic tests, and ultrasound, often extracting features from data gathered throughout pregnancy, delaying intervention. This study developed an ML model to identify HDP ri...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 89a8b069-7986-576a-872b-adf7d76c1302
DOI: 10.1101/2024.11.21.24317720

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Early Prediction of Hypertensive Disorders of Pregnancy Using Machine Learning and Medical Records from the First and Second TrimestersDOI 10.1101/2024.11.21.24317720Read

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

Development of phenotyping algorithms for hypertensive disorders of pregnancy (HDP) and their application in more than 22,000 pregnant womenSemantic proximity Read

An Interpretable Longitudinal Preeclampsia Risk Prediction Using Machine LearningSemantic proximity Read

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

Machine Learning-Based Risk Stratification for Gestational Diabetes ManagementSemantic proximity Read

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

Cohort profile: the Cohort for Risk Prediction Model Evaluation (CORE) for external validation of models identifying high-risk pregnant women in the early second trimesterSemantic proximity Read

Association of Pre-pregnancy Mental Health Disorders with Hypertensive Disorders of PregnancySemantic proximity Read

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