Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Obesity01:24

Obesity

564
The Body Mass Index (BMI) is a numerical value derived from a person's weight and height, used to categorize individuals into weight ranges. It is calculated using the formula: weight in kilograms divided by height in meters squared. Obesity is a health condition characterized by excessive accumulation of adipose tissue that poses health risks, often diagnosed with a BMI ≥ 30. This excess fat storage occurs when surplus dietary calories are converted into triglycerides and stored in...
564
Methods of Documentation VII: EMR01:30

Methods of Documentation VII: EMR

881
Electronic Medical Records (EMRs) primarily center around electronically documenting patients' health information within a single healthcare organization or practice. They contain essential clinical data related to a patient's medical history, diagnoses, medications, treatment plans, lab results, and other pertinent information relevant to the specific encounter or episode of care. EMRs are designed to streamline documentation and workflow processes within individual healthcare...
881
Binge Eating Disorders01:23

Binge Eating Disorders

153
Binge eating disorder is a significant mental health condition characterized by recurrent episodes of excessive food consumption within a short period, accompanied by a perceived loss of control over eating behavior. Unlike occasional overeating, binge eating disorder is marked by distressing emotions such as guilt, shame, and anxiety following binge episodes. The disorder affects individuals across different ages and backgrounds, with profound implications for physical and psychological...
153
Classification of Illness01:17

Classification of Illness

7.7K
The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
An illness is a response to a disease in which the person's level of functioning is changed compared with a previous level. The general classification of illness includes acute and chronic.
Acute illness is severe...
7.7K
Chronic Obstructive Pulmonary Disease-IV: Assessement and Diagnostic Studies01:27

Chronic Obstructive Pulmonary Disease-IV: Assessement and Diagnostic Studies

2.6K
Assessing and diagnosing Chronic Obstructive Pulmonary Disease (COPD) involves a detailed approach that includes a comprehensive review of medical history, physical examination, and a variety of diagnostic tests. This thorough evaluation is essential to ensure an accurate diagnosis and guide effective management strategies.
Medical History
2.6K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Minimum data requirements and automated preprocessing for reliable EEG biomarkers in Rett syndrome.

Frontiers in neurology·2026
Same author

Normative modeling for quantitative brain MRI phenotyping and biomarker discovery for pediatric leukodystrophies.

medRxiv : the preprint server for health sciences·2026
Same author

Developmental trajectory of individuals with Pelizaeus-Merzbacher Disease (PMD).

Molecular genetics and metabolism·2026
Same author

Integrating AI Into Governmental Public Health Decision Making: Challenges, Considerations, and a Path Forward.

JMIR public health and surveillance·2026
Same author

Target Trial Emulation of Vaccine Effectiveness in 5- to 17-years-olds with Prior SARS-CoV-2 Infection.

Nature communications·2026
Same author

Beyond Missingness: Systematizing Methods for Comprehensive Data Fitness Assessment in Clinical Research.

Journal of medical Internet research·2026

Related Experiment Video

Updated: Aug 9, 2025

Assessment of Child Anthropometry in a Large Epidemiologic Study
09:36

Assessment of Child Anthropometry in a Large Epidemiologic Study

Published on: February 2, 2017

27.2K

Characterizing clinical pediatric obesity subtypes using electronic health record data.

Elizabeth A Campbell1,2, Mitchell G Maltenfort2, Justine Shults2

  • 1Department of Information Science, College of Computing & Informatics, Drexel University, Philadelphia, Pennsylvania, United States of America.

PLOS Digital Health
|February 22, 2023
PubMed
Summary

This study used Latent Class Analysis on electronic health records to identify distinct pediatric obesity subtypes. These subtypes reveal common co-occurring conditions, aiding in personalized care for obese children.

More Related Videos

A Computer-Based Platform for Aiding Clinicians in Eating Disorder Analysis and Diagnosis
04:19

A Computer-Based Platform for Aiding Clinicians in Eating Disorder Analysis and Diagnosis

Published on: May 10, 2022

3.9K
Multidisciplinary Approach to Obesity Management: A Case Report
05:10

Multidisciplinary Approach to Obesity Management: A Case Report

Published on: May 30, 2025

259

Related Experiment Videos

Last Updated: Aug 9, 2025

Assessment of Child Anthropometry in a Large Epidemiologic Study
09:36

Assessment of Child Anthropometry in a Large Epidemiologic Study

Published on: February 2, 2017

27.2K
A Computer-Based Platform for Aiding Clinicians in Eating Disorder Analysis and Diagnosis
04:19

A Computer-Based Platform for Aiding Clinicians in Eating Disorder Analysis and Diagnosis

Published on: May 10, 2022

3.9K
Multidisciplinary Approach to Obesity Management: A Case Report
05:10

Multidisciplinary Approach to Obesity Management: A Case Report

Published on: May 30, 2025

259

Area of Science:

  • Pediatric Endocrinology
  • Clinical Informatics
  • Data Science in Healthcare

Background:

  • Childhood obesity is a complex health issue with diverse clinical presentations.
  • Understanding clinical subtypes can improve targeted interventions and patient management.
  • Electronic Health Records (EHR) offer rich data for identifying patient heterogeneity.

Purpose of the Study:

  • To identify distinct clinical subtypes of pediatric obesity using temporal condition patterns from EHR data.
  • To characterize the demographic and clinical features of identified pediatric obesity subtypes.
  • To explore the utility of Latent Class Analysis (LCA) for subtype discovery in pediatric obesity.

Main Methods:

  • Latent Class Analysis (LCA) was applied to EHR data from a large retrospective cohort of pediatric patients.
  • Temporal condition patterns surrounding obesity incidence were analyzed to form patient clusters.
  • Demographic characteristics and comorbidity prevalence were examined within each identified class.

Main Results:

  • An 8-class LCA model identified distinct pediatric obesity subtypes based on temporal condition patterns.
  • Subtypes were characterized by specific comorbidities, including respiratory/sleep disorders, skin conditions, seizures, asthma, gastrointestinal issues, and neurodevelopmental disorders.
  • High class membership probability (>70%) indicated strong clinical homogeneity within identified subtypes.

Conclusions:

  • Latent Class Analysis successfully identified clinically meaningful subtypes of pediatric obesity.
  • These subtypes correlate with known obesity-related comorbidities, offering insights into disease heterogeneity.
  • Findings support the use of EHR data and LCA for characterizing pediatric obesity and informing personalized treatment strategies.