Machine Learning-Based Risk Prediction Models for Pregnancy-Related Syndromes

Area of Science:

• Obstetrics and Gynecology
• Medical Informatics
• Computational Biology

Background:

• Pregnancy complications (hypertensive disorders, gestational diabetes, preterm birth) present a major global health challenge.
• Current screening methods using isolated biomarkers or linear models are insufficient for complex pregnancy pathophysiology.

Purpose of the Study:

• To review machine learning (ML) applications in obstetric care.
• To analyze multimodal data integration for improved prediction of pregnancy-related syndromes.

Main Methods:

• Literature synthesis of ML applications in obstetrics.
• Analysis of multimodal data integration (EHR, biochemical, multi-omics, imaging).
• Review of model development workflows, including data preprocessing (SMOTE) and interpretability (SHAP).

Main Results:

• Ensemble methods and deep learning models achieve high predictive accuracy (AUC > 0.90), outperforming traditional logistic regression.
• Key advancements include federated learning for data privacy and bias mitigation for enhanced generalizability.
• Integration of multimodal data sources improves predictive performance for pregnancy complications.

Conclusions:

• ML models facilitate predictive, preventive, and personalized obstetrics.
• Early interventions enabled by ML can improve perinatal outcomes.
• External validation and regulatory frameworks are crucial for clinical implementation of ML in obstetrics.