Pediatrics

Comparative analysis of machine learning versus traditional method for early detection of parental depression symptoms in the NICU.

INTRODUCTION: Neonatal intensive care unit (NICU) admission is a stressful experience for parents. NICU parents are twice at risk of depression symptoms compared to the general birthing population. Parental mental health problems have harmful long-term effects on both parents and infants. Timely screening and treatment can reduce these negative consequences.

Screening and Identification of Neutrophil Extracellular Trap-related Diagnostic Biomarkers for Pediatric Sepsis by Machine Learning.

Neutrophil extracellular trap (NET) is released by neutrophils to trap invading pathogens and can lead to dysregulation of immune responses and disease pathogenesis. However, systematic evaluation of NET-related genes (NETRGs) for the diagnosis of pediatric sepsis is still lacking. Three datasets were taken from the Gene Expression Omnibus (GEO) database: GSE13904, GSE26378, and GSE26440. After NE...

Machine learning model with output correction: Towards reliable bradycardia detection in neonates.

Bradycardia is a commonly occurring condition in premature infants, often causing serious consequences and cardiovascular complications. Reliable and ...

Identifying ESG types of Chinese solid waste disposal companies based on machine learning methods.

In the context of China's efforts to combat climate change and promote sustainable development, the solid waste treatment industry's environmental, so...

Intelligent wearable olfactory interface for latency-free mixed reality and fast olfactory enhancement.

Olfaction feedback systems could be utilized to stimulate human emotion, increase alertness, provide clinical therapy, and establish immersive virtual...

DeepFace: Deep-learning-based framework to contextualize orofacial-cleft-related variants during human embryonic craniofacial development.

Orofacial clefts (OFCs) are among the most common human congenital birth defects. Previous multiethnic studies have identified dozens of associated lo...

nBEST: Deep-learning-based non-human primates Brain Extraction and Segmentation Toolbox across ages, sites and species.

Accurate processing and analysis of non-human primate (NHP) brain magnetic resonance imaging (MRI) serves an indispensable role in understanding brain...

Modeling mortality prediction in older adults with dementia receiving COVID-19 vaccination.

OBJECTIVE: This study compared COVID-19 outcomes between vaccinated and unvaccinated older adults with and without cognitive impairment.

Applications of Artificial Intelligence for Pediatric Cancer Imaging.

Artificial intelligence (AI) is transforming the medical imaging of adult patients. However, its utilization in pediatric oncology imaging remains con...

Art or Artifact: Evaluating the Accuracy, Appeal, and Educational Value of AI-Generated Imagery in DALL·E 3 for Illustrating Congenital Heart Diseases.

Artificial Intelligence (AI), particularly AI-Generated Imagery, has the potential to impact medical and patient education. This research explores the...

Socio-demographic predictors of not having private dental insurance coverage: machine-learning algorithms may help identify the disadvantaged.

BACKGROUND: For accessing dental care in Canada, approximately 62% of the population has employment-based insurance, 6% have some publicly funded cove...

Improved pediatric ICU mortality prediction for respiratory diseases: machine learning and data subdivision insights.

The growing concern of pediatric mortality demands heightened preparedness in clinical settings, especially within intensive care units (ICUs). As res...

A deep learning model for brain segmentation across pediatric and adult populations.

Automated quantification of brain tissues on MR images has greatly contributed to the diagnosis and follow-up of neurological pathologies across vario...

Prediction of incomplete immunization among under-five children in East Africa from recent demographic and health surveys: a machine learning approach.

The World Health Organization as part of the goal of universal vaccination coverage by 2030 for all individuals. The global under-five mortality rate ...

Embedded Machine Learning System for Muscle Patterns Detection in a Patient with Shoulder Disarticulation.

The integration of artificial intelligence (AI) models in the classification of electromyographic (EMG) signals represents a significant advancement i...

Identifying Cardiovascular Disease Risk Endotypes of Adolescent Major Depressive Disorder Using Exploratory Unsupervised Machine Learning.

OBJECTIVE: Adolescents with major depressive disorder (MDD) are at increased risk of premature atherosclerosis and cardiovascular disease (CVD). The a...

Prediction of adolescent weight status by machine learning: a population-based study.

BACKGROUND: Adolescent weight problems have become a growing public health concern, making early prediction of non-normal weight status crucial for ef...

Machine learning-based classification of petrofacies in fine laminated limestones.

Characterization and development of hydrocarbon reservoirs depends on the classification of lithological patterns from well log data. In thin reservoi...

A complex fuzzy decision model for analysing the post-pandemic immuno-sustainability.

The post-effects of the COronaVIrus Disease (COVID-19) vary depending on socioeconomic and biological factors. Similarly, the effects of vaccination o...

Quantifying dysmorphologies of the neurocranium using artificial neural networks.

BACKGROUND: Craniosynostosis, a congenital condition characterized by the premature fusion of cranial sutures, necessitates objective methods for eval...