Personal Health Knowledge Graph for Clinically Relevant Diet Recommendations
This work addresses personalized diet recommendations for diabetic patients, but it is incremental as it builds on existing semantic technologies without major methodological breakthroughs.
The authors tackled the problem of providing personalized dietary recommendations by developing a Personal Health Ontology and knowledge graph to model patient data, resulting in a system that generates clinically relevant diet suggestions based on medical and lifestyle needs.
We propose a knowledge model for capturing dietary preferences and personal context to provide personalized dietary recommendations. We develop a knowledge model called the Personal Health Ontology, which is grounded in semantic technologies, and represents a patient's combined medical information, social determinants of health, and observations of daily living elicited from interviews with diabetic patients. We then generate a personal health knowledge graph that captures temporal patterns from synthetic food logs, annotated with concepts from the Personal Health Ontology. We further discuss how lifestyle guidelines grounded in semantic technologies can be reasoned with the generated personal health knowledge graph to provide appropriate dietary recommendations that satisfy the user's medical and other lifestyle needs.