Personal Health Knowledge Graph for Clinically Relevant Diet Recommendations

TL;DR

This paper introduces a Personal Health Ontology and knowledge graph to integrate medical, social, and daily data, enabling personalized dietary recommendations for diabetic patients through semantic reasoning.

Contribution

It presents a novel semantic knowledge model and graph-based approach for personalized diet recommendations based on comprehensive patient data.

Findings

Knowledge model effectively captures diverse patient information.

Knowledge graph reveals temporal dietary patterns.

Semantic reasoning supports personalized dietary advice.

Abstract

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.