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A decision support system on the obesity management and consultation during childhood and adolescence using ontology

Özgü Taçyıldız1, Duygu Çelik Ertuğrul1

  • 1Department of Computer Engineering, Engineering Faculty, Eastern Mediterranean University, 99628, Famagusta, North Cyprus via Mersin 10, Turkey.

Journal of Biomedical Informatics
|September 10, 2020
PubMed

Insights

This study introduces an ontology-based system to help health experts manage childhood obesity by providing personalized suggestions. The system analyzes patient data to offer tailored recommendations for treatment and lifestyle adjustments.

Area of Science:

  • Medical Informatics
  • Pediatric Health
  • Artificial Intelligence

Background:

  • Childhood obesity is a global health concern requiring constant monitoring and expert management.
  • Effective pediatric obesity management necessitates regular patient examination and comparison against growth percentiles.

Purpose of the Study:

  • To design and implement an ontology-based decision support system for health experts managing pediatric obesity.
  • To develop a smart medical assistant for children and adolescents that provides personalized, data-driven suggestions.

Main Methods:

  • Developed an ontology-based inference engine for personalized patient suggestions.
  • Utilized Semantic Web Rule Knowledge base for data analysis.
  • Focused on unique aspects of pediatric obesity management, distinct from adult approaches.

Main Results:

  • The system provides personalized suggestions in four categories: Development Feedback, Calorie Intake/Physical Activity, Mom Suggestions, and Treatment Stage.
  • Analyzed 80 case studies of pediatric patients, demonstrating the system's applicability and effectiveness.
  • The inference engine and ontology show promise for integration into intelligent decision support systems.

Conclusions:

  • The proposed ontology-based system is effective and efficient for pediatric obesity management.
  • The system's inference engine and ontology can serve as valuable knowledge bases for similar intelligent systems.
  • Intensive pediatric healthcare expert involvement is a key component of the system's success.
Abstract

Keywords:
Decision support systemsOntologyPediatric obesityPersonalized inferenceSemantic rules

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