AIMC Topic: Pediatric Obesity

Clear Filters Showing 31 to 39 of 39 articles

Developing an Algorithm to Detect Early Childhood Obesity in Two Tertiary Pediatric Medical Centers.

Applied clinical informatics
OBJECTIVE: The objective of this study is to develop an algorithm to accurately identify children with severe early onset childhood obesity (ages 1-5.99 years) using structured and unstructured data from the electronic health record (EHR).

Interpretable Probabilistic Latent Variable Models for Automatic Annotation of Clinical Text.

AMIA ... Annual Symposium proceedings. AMIA Symposium
We propose Latent Class Allocation (LCA) and Discriminative Labeled Latent Dirichlet Allocation (DL-LDA), two novel interpretable probabilistic latent variable models for automatic annotation of clinical text. Both models separate the terms that are ...

Exploring the forest instead of the trees: An innovative method for defining obesogenic and obesoprotective environments.

Health & place
Past research has assessed the association of single community characteristics with obesity, ignoring the spatial co-occurrence of multiple community-level risk factors. We used conditional random forests (CRF), a non-parametric machine learning appr...

Machine Learning Techniques for Prediction of Early Childhood Obesity.

Applied clinical informatics
OBJECTIVES: This paper aims to predict childhood obesity after age two, using only data collected prior to the second birthday by a clinical decision support system called CHICA.

Meta-analysis of shotgun sequencing of gut microbiota in obese children with MASLD or MASH.

Gut microbes
Alterations in the gut microbiome affect the development and severity of metabolic dysfunction-associated steatotic liver disease (MASLD) or metabolic dysfunction-associated steatohepatitis (MASH). We analyzed microbiomes of obese children with and w...

Exploring the effect of the triglyceride-glucose index on bone metabolism in prepubertal children, a retrospective study: insights from traditional methods and machine-learning-based bone remodeling prediction.

PeerJ
BACKGROUND: Childhood obesity poses a significant risk to bone health, but the impact of insulin resistance (IR) on bone metabolism in prepubertal children, as assessed by the triglyceride-glucose (TyG) index, remains underexplored. Bone turnover mar...

An interpretable predictive deep learning platform for pediatric metabolic diseases.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVES: Metabolic disease in children is increasing worldwide and predisposes a wide array of chronic comorbid conditions with severe impacts on quality of life. Tools for early detection are needed to promptly intervene to prevent or slow the de...

Discovering Pediatric Asthma Phenotypes on the Basis of Response to Controller Medication Using Machine Learning.

Annals of the American Thoracic Society
RATIONALE: Pediatric asthma has variable underlying inflammation and symptom control. Approaches to addressing this heterogeneity, such as clustering methods to find phenotypes and predict outcomes, have been investigated. However, clustering based o...