Clinical Decision Support for Improved Neonatal Care: The Development of a Machine Learning Model for the Prediction of Late-onset Sepsis and Necrotizing Enterocolitis.

Objective: To develop an artificial intelligence-based software system for predicting late-onset sepsis (LOS) and necrotizing enterocolitis (NEC) in infants admitted to the neonatal intensive care unit (NICU).

Study Design: Single-center, retrospective cohort study, conducted in the NICU of the Antwerp University Hospital. Continuous monitoring data of 865 preterm infants born at <32 weeks gestational age, admitted to the NICU in the first week of life, were used to train an XGBoost machine learning (ML) algorithm for LOS and NEC prediction in a cross-validated setup.

Expectant Management or Early Ibuprofen for Patent Ductus Arteriosus.

Background: Cyclooxygenase inhibitors are commonly used in infants with patent ductus arteriosus (PDA), but the benefit of these drugs is uncertain.

Methods: In this multicenter, noninferiority trial, we randomly assigned infants with echocardiographically confirmed PDA (diameter, >1.5 mm, with left-to-right shunting) who were extremely preterm (<28 weeks' gestational age) to receive either expectant management or early ibuprofen treatment.

Application and potential of artificial intelligence in neonatal medicine.

Neonatal care is becoming increasingly complex with large amounts of rich, routinely recorded physiological, diagnostic and outcome data. Artificial intelligence (AI) has the potential to harness this vast quantity and range of information and become a powerful tool to support clinical decision making, personalised care, precise prognostics, and enhance patient safety. Current AI approaches in neonatal medicine include tools for disease prediction and risk stratification, neurological diagnostic support and novel image recognition technologies.

Association between maternal cervicovaginal swab positivity for Ureaplasma spp. or other microorganisms and neonatal respiratory outcome and mortality.

Objective: We investigated the association between maternal cervicovaginal cultures, its antibiotic treatment, and neonatal outcome.

Study Design: This retrospective cohort study enrolled 480 neonates born prior to 32 weeks' gestation. They were divided into groups according to maternal cervicovaginal culture results.

Machine Learning to Support Hemodynamic Intervention in the Neonatal Intensive Care Unit.

Hemodynamic support in neonatal intensive care is directed at maintaining cardiovascular wellbeing. At present, monitoring of vital signs plays an essential role in augmenting care in a reactive manner. By applying machine learning techniques, a model can be trained to learn patterns in time series data, allowing the detection of adverse outcomes before they become clinically apparent.