Observational studyWomen's health (London, England)
Predictive modeling of complications arising from early-onset preeclampsia in pregnant women.
Observational study in Women's health (London, England). The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- 3D Placental Bed Perfusion Ultrasound: Development and Internal Validation of Trimester-Specific Prediction Models for Early- and Late-Onset Preeclampsia.Reproductive sciences (Thousand Oaks, Calif.) · 2026Article
- Integration of angiogenic pathway-related proteomics and clinical features via ensemble learning for precision risk stratification of early-onset preeclampsia.European journal of obstetrics & gynecology and reproductive biology: X · 2026Article
- Protocol for the external validation of machine learning models for risk prediction in early-onset preeclampsia: a multicenter prospective cohort study.BMC pregnancy and childbirth · 2026Article
- Development and internal validation of a nomogram for predicting adverse pregnancy outcomes in women with early-onset preeclampsia.Frontiers in medicine · 2026Article
- Complement as a driver of immune-vascular heterogeneity across preeclampsia subtypes: toward a precision medicine framework.Frontiers in immunology · 2026Review
- Artificial intelligence in maternal and child health: Current applications, translational gaps, and future research priorities.Women's health (London, England)Review
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPreeclampsia, 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.
objectiveOur goal is to enhance the clinical management of preeclampsia by improving risk stratification and offering a more personalized approach to patient care.
designA 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 Octubre Hospital. Exclusions included pregnancies with congenital anomalies, lack of angiogenesis biomarker determination or loss of follow-up, resulting in a cohort of 234 patients.
methodsWe employed innovative genetic algorithm strategies, integrating two distinct supervised machine learning models. These aim to accurately forecast key maternal risks associated with preeclampsia and determine the optimal timing for delivery. This approach culminates in a unique ensemble framework, comprising a primary model for assessing the risk of adverse outcomes and two specialized sub-models focusing on Hemolysis, Elevated Liver enzymes, and Low Platelets-abruption and temporal factors.
resultsOur findings are promising. The mono-objective genetic algorithm strategy yielded predictive
conclusionThis study introduces a novel approach to risk stratification in early-onset preeclampsia, integrating baseline and delivery data within a machine learning framework. Our results demonstrate that refined risk prediction with a minimal number of variables can complement existing clinical tools. Further validation in larger cohorts is needed to confirm its potential impact on decision-making and maternal outcomes.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.