AIMC Topic: Child, Preschool

Clear Filters Showing 1231 to 1240 of 1394 articles

Detection of pediatric developmental delay with machine learning technologies.

PloS one
OBJECTIVE: Accurate identification of children who will develop delay (DD) is challenging for therapists because recent studies have reported that children who underwent early intervention achieved more favorable outcomes than those who did not. In t...

A machine learning approach to predicting postoperative recurrence in pediatric chronic rhinosinusitis: identification of key metabolic biomarkers.

American journal of otolaryngology
BACKGROUND: Pediatric chronic rhinosinusitis (CRS) is a common chronic inflammatory disease with a high recurrence rate after surgery. This study aimed to construct and validate a machine learning-based predictive model to predict the risk of postope...

Modeling protective meningococcal antibody responses and factors influencing antibody persistence following vaccination with MenAfriVac using machine learning.

PloS one
Meningococcal meningitis poses a significant public health burden in the meningitis belt region of sub-Saharan Africa. The introduction of the meningococcal PsA-TT vaccine (MenAfriVac®) has successfully eliminated Neisseria meningitidis serogroup A (...

Forecasting acute childhood malnutrition in Kenya using machine learning and diverse sets of indicators.

PloS one
OBJECTIVES: Malnutrition is a leading cause of morbidity and mortality for children under-5 globally. Low- and middle-income countries, such as Kenya, bear the greatest burden of malnutrition. The Kenyan government has been collecting clinical indica...

Leveraging artificial intelligence for predicting spontaneous closure of perimembranous ventricular septal defect in children: a multicentre, retrospective study in China.

The Lancet. Digital health
BACKGROUND: Perimembranous ventricular septal defect (PMVSD) is a prevalent congenital heart disease, presenting challenges in predicting spontaneous closure, which is crucial for therapeutic decisions. Existing models mainly rely on structured echoc...

PROGNOSTIC ACCURACY OF MACHINE LEARNING MODELS FOR IN-HOSPITAL MORTALITY AMONG CHILDREN WITH PHOENIX SEPSIS ADMITTED TO THE PEDIATRIC INTENSIVE CARE UNIT.

Shock (Augusta, Ga.)
Objective: The Phoenix sepsis criteria define sepsis in children with suspected or confirmed infection who have ≥2 in the Phoenix Sepsis Score. The adoption of the Phoenix sepsis criteria eliminated the Systemic Inflammatory Response Syndrome criteri...

Automated Evaluation of Antibiotic Prescribing Guideline Concordance in Pediatric Sinusitis Clinical Notes.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
BACKGROUND: Ensuring antibiotics are prescribed only when necessary is crucial for maintaining their effectiveness and is a key focus of public health initiatives worldwide. In cases of sinusitis, among the most common reasons for antibiotic prescrip...

Brain-region specific autism prediction from electroencephalogram signals using graph convolution neural network.

Technology and health care : official journal of the European Society for Engineering and Medicine
BACKGROUND: Brain variations are responsible for developmental impairments, including autism spectrum disorder (ASD). EEG signals efficiently detect neurological conditions by revealing crucial information about brain function abnormalities.

Novel machine learning technique further clarifies unrelated donor selection to optimize transplantation outcomes.

Blood advances
We investigated the impact of donor characteristics on outcomes in allogeneic hematopoietic cell transplantation (HCT) recipients using a novel machine learning approach, the Nonparametric Failure Time Bayesian Additive Regression Trees (NFT BART). N...