FAITH

About the project

FAITH aimed to provide an Artificial Intelligence application that remotely identified depression markers, using Federated Learning, in people that had undergone cancer treatment to better model and predict disease/treatment trajectories. To protect privacy of the individual, but still gain insights that are beneficial to the broader population, FAITH applied the concept of federated machine learning, which makes it possible to build machine learning systems without direct access to personal treatment data that was used for training in machine learning. Devices private to the patient ran their own personalised AI models, via the project’s ‘AI Angel’ application, while a global AI model aggregated the individual model learnings (rather than the traditional approach of a central repository of holding all private patient data). FAITH’s ‘AI Angel’ remotely analysed depression markers, predicting negative trends in their disease trajectory, giving their healthcare providers advanced warnings to allow for timely intervention. These markers were treated under several distinct categories: Activity, Outlook, Sleep, and Appetite, in accordance with the 3M strategy for population health: Monitor–Measure–Manage. A key strength of FAITH was the involvement of eminent cancer hospitals and specialists in the consortium to provide relevant applicable cancer care related use cases that could effectively leverage a big data framework. FAITH had trial sites in Madrid, Waterford, and Lisbon, with real end users involved in assessing and validating the AI application to ensure its usefulness.