A machine-learning–based algorithm improves prediction of preeclampsia-associated adverse outcomes
Leon J. Schmidt, J. Rieger, Mark Neznansky, Max Hackelöer, Lisa Antonia Dröge, Wolfgang Henrich, David Higgins, Stefan Verlohren
Topics & Concepts
MedicinePreeclampsiaMachine learningRandom forestUmbilical arteryAlgorithmUterine arteryAdverse effectPlacental growth factorObstetricsPregnancyArtificial intelligenceInternal medicineFetusGestationComputer scienceVascular endothelial growth factorVEGF receptorsBiologyGeneticsPregnancy and preeclampsia studiesMaternal and fetal healthcarePreterm Birth and Chorioamnionitis