OBJECTIVE: To analyze the influencing factors of early-onset preeclampsia (EOPE). And to construct and validate the prediction model of EOPE using machine learning algorithm.
The value of machine learning capacity in maternal health, and in particular prediction of preeclampsia will only be realised when there are high quality clinical data provided, representative populations included, different health systems and models...
OBJECTIVE: To predict risk of pre-eclampsia (PE) in women using machine learning (ML) algorithms, based on electronic health records (EHR) collected at the early second trimester.
INTRODUCTION: It might in the future be valuable to screen for increased maternal arterial stiffness, i.e. low compliance, since it is associated with development of hypertensive complications in pregnancy. Digital pulse wave analysis (DPA) is an eas...
Preeclampsia (PE), new onset hypertension during pregnancy, is associated with a proinflammatory profile compared to normal pregnancy (NP). We hypothesize that CD4 T cells from PE patient placentas cause PE symptoms during pregnancy compared to those...