A few weeks ago, on June 10, STRATIF-AI had its first meeting with fellow EU projects funded under the same call, all focusing on the use of AI for improved patient stratification. This initial two-hour meeting served as an introduction to each project and a discussion of key shared challenges (see Figure below). These challenges include data interoperability, federated learning, trustworthy and explainable AI (xAI), and MDR certifications.
During the meeting, we presented some core concepts of STRATIF-AI, such as our hybrid approach that combines multi-level, multi-timescale, and multi-organ simulations using mechanistic models as inputs to machine-learning models. This hybrid approach enhances the explainability of our models.
Furthermore, we explained shortly how STRATIF-AI uses data harmonization in two places: i) in connection to the federated learning set up by a collaboration between Catalina Martinez Costa (University of Murcia) and Lucia Gregorio Rodriguez (TREE), and ii) in connection to integrating a copy data from a variety of different sources into a personal data vault (Jesper Fellenius at Z2).
This collaboration and network of related projects will be useful for us to use in various related topics as we move forward, and we from STRATIF-AI are looking forward to new meetings. Thanks to the team at PREPARE for setting this meeting up.
