Abstract Machine Learning (ML) has been widely used in predicting the mode of childbirth
and assessing the potential maternal risks during pregnancy. The primary aim of this review …

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

Introduction Machine learning (ML) corresponds to a wide variety of methods that use
mathematics, statistics and computational science to learn from multiple variables …

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

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

Preterm births affect around 15 million children a year worldwide. Current medical efforts
focus on mitigating the effects of prematurity, not on preventing it. Diagnostic methods are …

Preterm birth is the leading cause defining the infant mortality and morbidity globally. Non-
invasive surface uterine electromyogram (sEMG) also known as Electrohysterogram (EHG) …

Artificial Intelligence has been widely applied to a majority of research areas, including
health and medicine. Certain complications or disorders that can appear during pregnancy …

Preterm births (PTB) affect nearly 15 million kids worldwide. At present, medical fields aim to
reduce the possessions of prematurity rather than avoid it. The cervix is currently measured …

One of the significant health problems around the globe is associated with neonatal mortality
or morbidity and disability in later life. The primary cause of neonatal death is preterm labor …