A chronic disease dietary consultation system using OWL-based ontologies and semantic rules

Area of Science:

• Biomedical Informatics
• Artificial Intelligence in Healthcare
• Dietary Management Systems

Background:

• Chronic diseases necessitate complex dietary management, integrating illness stage, patient condition, and nutrient restrictions.
• Current dietary recommendations often lack personalized, dynamic adjustments for chronic disease patients.

Purpose of the Study:

• To develop and evaluate a knowledge-based system (KBS) for personalized dietary consultations in chronic kidney disease (CKD).
• To demonstrate the application of Web Ontology Language (OWL) and Semantic Web Rule Language (SWRL) for effective knowledge inference in dietary planning.

Main Methods:

• Constructed a KBS for CKD dietary consultation using OWL and SWRL.
• Integrated multiple knowledge sources for problem-solving modeling.
• Evaluated system performance using data from 84 CKD patients to assess food serving recommendations.

Main Results:

• The OWL-based KBS achieved accurate problem-solving reasoning for dietary recommendations.
• The system demonstrated effectiveness in recommending appropriate food serving amounts for balanced nutrient intake.
• The knowledge base exhibited shareability and extensibility.

Conclusions:

• A KBS approach using OWL and SWRL is effective for complex dietary management in CKD.
• The developed system provides accurate and adaptable dietary recommendations, supporting patient self-management.
• This methodology offers a scalable solution for personalized nutrition guidance in chronic disease care.