Prediction of spontaneous preterm birth using supervised machine learning on metabolomic data: A case–cohort study
Yasmina Al Ghadban,
Yuheng Du,
D. Stephen Charnock‐Jones
et al.
Abstract:ObjectivesTo identify and internally validate metabolites predictive of spontaneous preterm birth (sPTB) using multiple machine learning methods and sequential maternal serum samples, and to predict spontaneous early term birth (sETB) using these metabolites.DesignCase–cohort design within a prospective cohort study.SettingCambridge, UK.Population or sampleA total of 399 Pregnancy Outcome Prediction study participants, including 98 cases of sPTB.MethodsAn untargeted metabolomic analysis of maternal serum sampl… Show more
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