AI Medical Compendium Topic

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Infant, Newborn

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O blood usage trends in the pediatric population 2015-2019: A multi-institutional analysis.

Transfusion
BACKGROUND: In 2019, AABB released the bulletin "Recommendations on the Use of Group O Red Blood Cells" in which the recommendations about pediatric and neonatal blood transfusions were limited. Eight U.S. pediatric hospitals sought to determine tren...

Evaluating how different balancing data techniques impact on prediction of premature birth using machine learning models.

PloS one
Premature birth can be defined as birth before 37 weeks of gestation, which is a significant global health issue, being the main cause for neonatal deaths. In this work, we evaluate machine learning models for predicting premature birth using Brazili...

Machine learning combined with infrared spectroscopy for detection of hypertension pregnancy: towards newborn and pregnant blood analysis.

BMC pregnancy and childbirth
Biochemical changes in the cervix during labor are not well understood. This gap in knowledge is significant, as understanding the precise biochemical processes can provide critical insights into the mechanisms of labor and potentially inform better ...

Machine learning prediction of preterm birth in women under 35 using routine biomarkers in a retrospective cohort study.

Scientific reports
Preterm birth (PTB), defined as delivery before 37 weeks, affects 15 million infants annually, accounting for 11% of live births and over 35% of neonatal deaths. While advanced maternal age (≥ 35 years) is a known risk factor, PTB risk in women under...

Early prediction of intraventricular hemorrhage in very low birth weight infants using deep neural networks with attention in low-resource settings.

Scientific reports
Early prediction of intraventricular hemorrhage (IVH) in very low-birthweight infants (VLBWIs) remains challenging because of multifactorial risk factors. IVH often occurs within a few hours after birth, yet its onset cannot be reliably predicted usi...

Machine learning-based prediction of vesicoureteral reflux outcomes in infants under antibiotic prophylaxis.

Scientific reports
We aimed to investigate the independent outcome predictors of continuous antibiotic prophylaxis (CAP) in vesicoureteral reflux, train a model to predict the outcome, and evaluate which infants should be referred for endoscopic vesicoureteral reflux c...

Predicting low birth weight risks in pregnant women in Brazil using machine learning algorithms: data from the Araraquara cohort study.

BMC pregnancy and childbirth
BACKGROUND: Low birth weight (LBW) is a critical factor linked to neonatal morbidity and mortality. Early prediction is essential for timely interventions. This study aimed to develop and evaluate predictive models for LBW using machine learning algo...

Predicting infant brain connectivity with federated multi-trajectory GNNs using scarce data.

Medical image analysis
The understanding of the convoluted evolution of infant brain networks during the first postnatal year is pivotal for identifying the dynamics of early brain connectivity development. Thanks to the valuable insights into the brain's anatomy, existing...

Prediction of IUGR condition at birth by means of CTG recordings and a ResNet model.

Computers in biology and medicine
OBJECTIVE: Sub-optimal uterine-placental perfusion and fetal nutrition can lead to intrauterine growth restriction (IUGR), also called fetal growth restriction (FGR). Antenatal cardiotocography (CTG) can aid in the early detection of IUGR. Reliably d...

Interpretable machine learning model for early prediction of disseminated intravascular coagulation in critically ill children.

Scientific reports
Disseminated intravascular coagulation (DIC) is a thrombo-hemorrhagic disorder that can be life-threatening in critically ill children, and the quest for an accurate and efficient method for early DIC prediction is of paramount importance. Candidate ...