AIMC Topic: Infant, Newborn

Clear Filters Showing 681 to 690 of 815 articles

Machine learning-based approaches for distinguishing viral and bacterial pneumonia in paediatrics: A scoping review.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Pneumonia is the leading cause of hospitalisation and mortality among children under five, particularly in low-resource settings. Accurate differentiation between viral and bacterial pneumonia is essential for guiding approp...

Fast and Stable Neonatal Brain MR Imaging Using Integrated Learned Subspace Model and Deep Learning.

IEEE transactions on bio-medical engineering
OBJECTIVE: To enable fast and stable neonatal brain MR imaging by integrating learned neonate-specific subspace model and model-driven deep learning.

Heart Rate and Body Temperature Relationship in Children Admitted to PICU: A Machine Learning Approach.

IEEE transactions on bio-medical engineering
UNLABELLED: Vital signs are crucial clinical measures, with body temperature (BT) and heart rate (HR) being particularly significant. While their association has been studied in adults and children, research in Pediatric Intensive Care Unit (PICU) se...

[Problems and countermeasures in eye care and vision screening services for children aged 0 to 6 years].

[Zhonghua yan ke za zhi] Chinese journal of ophthalmology
The critical period for visual function and ocular structure development occurs from 0 to 6 years of age, making standardized eye care and vision screening services during this stage vital for improving lifelong visual outcomes and quality of life. I...

Fetal growth disorders detection during first trimester gestation through comprehensive maternal circulating DNA profiling.

Human molecular genetics
BACKGROUND: Early diagnosis, close follow-up and timely delivery constitute the main elements for appropriate detection and management of Fetal Growth Disorders (FGD). We hypothesized that fetoplacental FGD-associated alterations can be detected in c...

Hybrid time series and machine learning models for forecasting cardiovascular mortality in India: an age specific analysis.

BMC public health
Cardiovascular disease (CVD) is a primary cause of death in India, accounting for a significant portion of the global CVD burden. This study looks at statistics on heart disease mortality from the Institute for Health Metrics and Evaluation (IHME) fr...

Machine Learning for Clinical Decision Support in the Neonatal Intensive Care Unit.

NeoReviews
The neonatal intensive care unit (NICU) is a data-rich environment that is an ideal setting for the implementation of machine learning (ML) and artificial intelligence (AI) in clinical decision support (CDS). Despite their potential, ML and AI applic...

Interpretable deep neural networks for advancing early neonatal birth weight prediction using multimodal maternal factors.

Journal of biomedical informatics
BACKGROUND: Neonatal low birth weight (LBW) is a significant predictor of increased morbidity and mortality among newborns. Predominantly, traditional prediction methods depend heavily on ultrasonography, which does not consider risk factors affectin...

Machine learning models for predicting malnutrition in NICU patients: A comprehensive benchmarking study.

Computers in biology and medicine
Malnutrition, affecting both adults and children globally, results from inadequate nutrient intake or loss of body mass. Traditional screening tools, reliant on detailed questionnaires, are costly, time-consuming, and often lack accuracy and generali...

Machine Learning for Predicting Waitlist Mortality in Pediatric Heart Transplantation.

Pediatric transplantation
BACKGROUND: Waitlist mortality remains a critical issue for pediatric heart transplant (HTx) candidates, particularly for candidates with congenital heart disease. Listing center organ offer acceptance practices have been identified as a factor influ...