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Traditional screening methods, reliant on isolated biomarkers or linear models, often fail to address the complex pathophysiology of these conditions.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Method<\/jats:title>\n                    <jats:p>This review synthesizes current literature on machine learning applications in obstetric care, analyzing multimodal data integration from electronic health records, biochemical markers, multi\u2010omics, and imaging. It outlines model development workflows, including preprocessing for class imbalance (e.g., SMOTE) and interpretability tools (e.g., SHAP), while addressing ethical and technical challenges.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Ensemble methods (e.g., Random Forest, XGBoost) and deep learning (e.g., CNNs) outperform logistic regression, achieving AUC values &gt;\u20090.90. Key advancements include federated learning for privacy and bias mitigation strategies to enhance generalizability across populations.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Machine learning\u2010based models enable predictive, preventive, and personalized obstetrics, facilitating early interventions and improved perinatal outcomes, though external validation and regulatory frameworks are essential for clinical adoption.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/bdr2.70038","type":"journal-article","created":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T11:40:17Z","timestamp":1773315617000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Machine Learning\u2010Based Risk Prediction Models for Pregnancy\u2010Related Syndromes"],"prefix":"10.1002","volume":"118","author":[{"given":"Yanqi","family":"Wu","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering Eindhoven University of Technology  Eindhoven the Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2026,3,12]]},"reference":[{"key":"e_1_2_15_2_1","unstructured":"Administration USF and D.2024.\u201cFDA proposes framework to advance credibility of AI models used for drug and biological product submissions.\u201d"},{"key":"e_1_2_15_3_1","doi-asserted-by":"publisher","DOI":"10.2196\/70358"},{"key":"e_1_2_15_4_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10995-020-02942-2"},{"issue":"3","key":"e_1_2_15_5_1","article-title":"Advancing Obstetric Care Through Artificial Intelligence\u2010Enhanced Clinical Decision Support Systems: A Systematic Review","volume":"17","author":"Ali M. 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