Predictive modeling of complications arising from early-onset preeclampsia in pregnant women.

ABSTRACT:

Background

Preeclampsia, a complex and unpredictable pregnancy complication, poses significant challenges in predicting maternal outcomes, often leading to heightened anxiety among patients, families, and clinicians. This study introduces an innovative approach to enhance the prediction of complications in early-onset preeclampsia, leveraging advanced machine learning techniques inspired by bio-algorithms.

Objective

Our goal is to enhance the clinical management of preeclampsia by improving risk stratification and offering a more personalized approach to patient care.

Design

A single-center, observational, retrospective cohort study with 246 singleton pregnancies diagnosed with early-onset preeclampsia between January 2007 and December 2020 was conducted at 12 de Octu

SUBMITTER: Dominguez Del Olmo P 

PROVIDER: S-EPMC12280542 | biostudies-literature | 2025 Jan-Dec

REPOSITORIES: biostudies-literature