Background: Predictions in pregnancy care are complex because of interactions among
multiple factors. Hence, pregnancy outcomes are not easily predicted by a single predictor …

Objective Development of novel informatics methods focused on improving pregnancy
outcomes remains an active area of research. The purpose of this study is to systematically …

Datacenter workloads demand high computational capabilities, flexibility, power efficiency,
and low cost. It is challenging to improve all of these factors simultaneously. To advance …

Although cross-validation is a standard procedure for performance evaluation, its joint
application with oversampling remains an open question for researchers farther from the …

Abstract Information extracted from electrohysterography recordings could potentially prove
to be an interesting additional source of information to estimate the risk on preterm birth …

Objective To predict preterm birth in nulliparous women using logistic regression and
machine learning. Design Population-based retrospective cohort. Participants Nulliparous …

To evaluate and holistically treat the mental health sequelae and potential psychiatric
comorbidities associated with obstetric and gynaecological conditions, it is important to …

This study reviews the current status and future prospective of knowledge on the use of
artificial intelligence for the prediction of spontaneous preterm labor and birth (“preterm birth” …

Preterm birth is the leading cause of neonatal morbidity and mortality. Early identification of
high-risk deliveries, combined with appropriate medication appears as the way to treat the …

This study reviews recent advances on the application of artificial intelligence for the early
diagnosis of various maternal-fetal conditions such as preterm birth and abnormal fetal …