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What is Machine Learning-Augmented Biomarkers in Mid-Pregnancy Down Syndrome Screening Improve Prediction of Small-For-Gestational-Age Infants about?

This study investigates the use of machine learning to enhance the prediction of adverse fetal growth outcomes (AFGO) during mid-pregnancy Down syndrome screening. It finds that serum unconjugated estriol (uE3) is more effective than other biomarkers in predicting small-for-gestational-age infants, and machine learning models, particularly Gradient Boosting Machine (GBM) and Generalized Linear Model (GLM), significantly improve prediction accuracy. The research emphasizes the potential of integrating routin

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dinhky1809

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EN