BMC Pregnancy and Childbirth called for submissions to our Collection on Machine learning to build predictive models in maternal-fetal medicine.
Maternal-fetal medicine encompasses a broad spectrum of healthcare aimed at ensuring the well-being of both the mother and the unborn child during pregnancy and childbirth. Despite significant advancements in medical technology and obstetric care, maternal and fetal complications continue to pose substantial challenges, contributing to adverse outcomes such as maternal mortality, stillbirths, and neonatal morbidity. With the advent of machine learning technologies, there is a significant opportunity to leverage vast amounts of clinical data to develop predictive models that can aid healthcare professionals to identify subtle patterns and predictors of adverse outcomes that may elude conventional diagnostic approaches.
BMC Pregnancy and Childbirth launched a Collection, Machine learning to build predictive models in maternal-fetal medicine, to facilitate the understanding of how machine learning algorithms can enhance clinical decision-making, improve patient outcomes, and ultimately reduce maternal and fetal morbidity and mortality rates. The Collection invited researchers and clinicians in fields including maternal-fetal medicine, high-risk obstetrics, midwifery, gynecology, perinatology, computer science, and statistics to contribute research that explores topics including, but not limited to, the prediction of gestational complications, fetal anomaly detection, preterm birth forecasting, preeclampsia risk assessment, anticipation of maternal hemorrhage, neonatal outcome prediction, integration of multi-modal data, and the development of clinical decision support systems using machine learning techniques in maternal-fetal medicine.
This Collection supports and amplifies research related to SDG 3: Good Health and Well-Being.
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