Personalised Nutrition Support for People Living with Multiple Chronic Conditions, Powered by MRPN‑AI
To develop a scalable, cross-platform mobile application powered by Multi‑Risk Precision Nutrition AI (MRPN‑AI) and an Agentic AI orchestration layer that can scientifically assess food-related risks for chronic diseases; both individually and in combination while delivering personalised, culturally relevant, and explainable dietary guidance. The system will initially focus on South Asian foods, enabling intelligent reasoning, adaptive user interaction, and precise nutrition recommendations for individuals living with multiple chronic conditions.
Chronic conditions such as diabetes, kidney disease, and cardiovascular disorders require highly accurate and personalised dietary guidance to support effective management and reduce the risk of complications. However, many existing nutrition applications are designed for single conditions and often struggle to provide consistent recommendations when multiple health issues coexist. They also tend to overlook indigenous and traditional foods, limiting their relevance for individuals whose diets are shaped by cultural and regional eating practices.
To address these challenges, this project aims not only to develop a scientifically grounded Multi Risk Precision Nutrition AI (MRPN AI) model, but also to design an Agentic AI system that can intelligently orchestrate its components. This will enable adaptive reasoning, personalised recommendation generation, user-friendly interaction, and explainable decision support, making the system more practical, transparent, and effective for people living with multiple chronic conditions.
Multi Risk Precision Nutrition AI (MRPN-AI) is an AI discipline focused on computing safe, personalised dietary guidance for people who are managing multiple chronic conditions simultaneously. It brings together condition aware nutrient risk modelling, multi objective optimisation with safety constraints, culturally faithful food ontologies and preparation methods, explainability with clear oversight, and rigorous evaluation with validation by qualified nutrition professionals. The result is guidance that respects clinical guardrails and local cuisines usable at the point of decision.
An Agentic AI orchestration layer is an intelligent coordination framework that oversees and manages the interaction between multiple MRPN AI components, such as risk assessment, conflict resolution, personalisation, and explainability modules. It enables these components to work together in a structured and adaptive manner to generate personalised, explainable, and context-aware dietary recommendations for individuals living with multiple chronic conditions. By integrating reasoning, user context, and decision support, the orchestration layer helps transform separate AI outputs into meaningful, user-centred nutrition guidance.
