AIMC Topic: Child

Clear Filters Showing 101 to 110 of 3433 articles

Predicting outcomes in pediatric patients with acute kidney injury: a retrospective single-center cohort study using machine learning models.

BMC medical informatics and decision making
OBJECTIVE: To develop and evaluate machine learning models combined with survival analysis for predicting 7-, 14-, and 28-day mortality in critically ill children with acute kidney injury (AKI), identifying key predictors to guide risk stratification...

Predictive modeling of adaptive behavior trajectories in autism: insights from a clinical cohort study.

Translational psychiatry
Research aimed at understanding how baseline clinical and demographic characteristics influence outcomes over time is critically important to inform individualized therapeutic programs for children with neurodevelopmental differences. This study char...

Clinical, biochemical, and molecular characterization of a cohort of Egyptian patients with Sanfilippo B syndrome (MPS IIIB): Bayesian Gaussian mixture model.

Molecular biology reports
BACKGROUND: Lysosomal storage diseases (LSDs) are a group of genetically heterogeneous inherited metabolic disorders that affect the functions of the lysosomes in different human tissues. Mucopolysaccharidosis IIIB (MPS IIIB), Sanfilippo B syndrome, ...

Performance of several large language models when answering common patient questions about type 1 diabetes in children: accuracy, comprehensibility and practicality.

BMC pediatrics
BACKGROUND: The use of large language models (LLMs) in healthcare has expanded significantly with advances in natural language processing. Models, such as ChatGPT and Google Gemini, are increasingly used to generate human-like responses to questions,...

SHAP-enhanced machine learning identifies modifiable obesity predictors across adolescent weight groups: A 2021 YRBSS analysis.

PloS one
BACKGROUND: The growing prevalence of obesity in adolescents around the world poses a major threat to public health. This research uses machine learning models to examine the main causes of obesity, in contrast to standard information that typically ...

A machine learning-based predictive model for multilobar pulmonary consolidation induced by macrolide-resistant pneumonia caused by the 23S rRNA A2063G mutation.

Microbiology spectrum
This study aims to develop a machine learning (ML)-based predictive model for assessing the risk of multilobar pulmonary consolidation in children with macrolide-resistant pneumonia (MRMP) caused by the 23S rRNA A2063G mutation, a subgroup underrepr...

The Alongside Digital Wellness Program for Youth: Longitudinal Pre-Post Outcomes Study.

JMIR formative research
BACKGROUND: Youth are increasingly experiencing psychological distress. Schools are ideal settings for disseminating mental health support, but they are often insufficiently resourced to do so. Digital mental health tools represent a unique avenue to...

Age estimation of children and adolescents from mandibles using machine learning.

Scientific reports
Age estimation is a crucial step in forensic identification, particularly in scenarios where dental structures may be absent. This study aimed to develop and evaluate supervised machine learning models to predict chronological age based on mandibular...

AI-driven prognostics in pediatric bone marrow transplantation: a CAD approach with Bayesian and PSO optimization.

BMC medical informatics and decision making
Bone marrow transplantation (BMT) is a critical treatment for various hematological diseases in children, offering a potential cure and significantly improving patient outcomes. However, the complexity of matching donors and recipients and predicting...

Research hotspots and trends of pediatric bone age: A bibliometric and visualization analysis.

Lasers in medical science
PURPOSE: Research related to pediatric bone age has gained substantial scholarly attention over recent decades, given its critical importance in monitoring growth and guiding clinical decision-making in children. This study aims to identify research ...