Machine learning approaches to predict gestational age in normal and complicated pregnancies via urinary metabolomics analysis

The elucidation of dynamic metabolomic changes during gestation is particularly important for the development of methods to evaluate pregnancy status or achieve earlier detection of pregnancy-related complications. Some studies have constructed models to evaluate pregnancy status and predict gestational age using omics data from blood biospecimens; however, less invasive methods are desired. Here we propose a model to predict gestational age,...