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FAITH

FAITH is an EU-funded research project that aims to provide an Artificial Intelligence application that remotely identifies depression markers, using Federated Learning, in people that have undergone cancer treatment.

The FAITH concept would present healthcare providers with advanced warnings to allow timely intervention, giving patients the possibility to improve their quality of life and receive intelligent post-cancer support. Trial sites in Madrid, Waterford, and Lisbon will assess and test the concept to ensure its usefulness, involving real end users (both clinicians and patients).

Tags:

  • Artificial Intelligence
  • Future Health
  • Pervasive Sensing
  • Research
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Funding

H2020. Grant Agreement number: 875358

Duration

4years

Vertical alignment / Department

Future Health / Mobile Ecosystem and Pervasive Sensing (MEPS)

Project Website

Overview

FAITH is creating an innovative solution that uses Artificial Intelligence based technologies to track targeted depression markers in cancer survivors to be able to monitor downward trajectories in these markers, and ultimately inform their point of care of these declines.

By using the FAITH solution, cancer survivors will be brought to the attention of their healthcare services once their depression markers start to show signs of downward trajectories. This means they can be offered intelligent post-cancer support as early as possible, helping to identify trends which could affect their quality of life.

By doing this, cancer survivors who begin to experience such declines get the chance to be more aware of their mental health situation, receive as early as possible intelligent post-cancer support, and therefore, in the end, improve their quality of life.

Implementation

In order to ensure that FAITH’s project objectives are achieved, a clearly defined methodology has been created that fosters interdependency and flow between the different work packages in the project.

The project will start with gathering requirements (WP2) from our end user organisations (hospitals/doctors/patients) as well as relevant stakeholder and policy maker organisations. These requirements drive the architecture specifications, the data reference models as well as real-life use case scenarios (WP2), which are then acted upon for the building of our platform components (WP3, WP4 & WP5). Data from the various sources, including the FAITH App, any connected sensors and our federated AI models are fed into our framework, which through the in-built intelligence will be used to monitor specific mental state markers and further

provided for analysis (WP4). This provides the hospital liaison person with a full overview of what is happening in relation to the person who has finished cancer treatment (WP3). These interactions will be trialled (WP6) in consortium end user hospitals, gathering feedback and being validated by doctors and patients with regards to their specific needs. This feedback from these trials will be fed back into a second, and third, round of requirements gathering. After the third iteration, and once we are sure that we have a framework that meets the market needs, we will explore market deployment activities (WP8), which is fully supported by our dissemination, policy & stakeholder engagement activities (WP7).

Key Objectives

  • Specify the scope of the of the FAITH framework through elicitation of use-cases; leading to the development of functional and non-functional requirements, user stories to cover these, and a reference architecture.
  • Successfully integrate the appropriate middleware, software components, tools and libraries in order to deliver a federated learning framework for secure experimentation, composition, exploration, and ultimately deployment in line with requirements.
  • Deploy Federated AI models across a broad population base to deliver distinct personalised models.
  • Implement a voice/NLP interface to gauge a user’s mental outlook.
  • Demonstrate the applicability, usability effectiveness and value of the FAITH concepts, models, mechanisms, and techniques in real-world scenario under pragmatic conditions against a pre-defined set of use-cases.