PRAESIIDIUM
Physics informed machine learning-based prediction and reversion of impaired fasting glucose management
The aim of project PRAESIIDIUM is to develop a tool aimed at providing a real-time prediction of the prediabetic risk of an individual. The prediction algorithm will be based on a “physics-informed machine learning” approach: a rich dataset of real-life data, obtained from already existing previous and a new clinical trial with continuous data ingestion through wearable sensors, will be combined with mathematical models and eXplainable AI (XAI) techniques, to overcome the limits of “black-box” ML approaches while improving the prediction performances and reducing the computational time of the risk calculation based on the simulations of the mathematical model. The final algorithm will be implemented in a web-based platform, where medical doctors and patients can inject data from several sources (acquisition form connected sensors and manual insertion) and obtain a real time analysis of the risk to develop the prediabetic condition over time.
Project synopsis | Project website
PARTNERS & CNR-IEIIT ROLE
Partners: The consortium includes 11 partners from Italy, Austria, Belgium, Latvia, Sweden, and Switzerland.
IEIIT takes part in project PRAESIIDIUM as a partner and, in collaboration with CNR-IAC, will develop personalized models to characterize the risk and prevent type 2 diabetes.
OTHER INFORMATION
Funding: European Commission, EUROPEAN HEALTH AND DIGITAL EXECUTIVE AGENCY (HADEA) (topic HORIZON-HLTH-2022-STAYHLTH-02-01 Grant n. 101095672)
Timeline: Jan 2023 - Dec 2025
