a Federated Artificial Intelligence solution for moniToring mental Health status after cancer treatment

Risultati finali

D3.4(a) - FAITH Data Visualisation & Reporting

Direct outcome of T34 delivering the visualization and reporting interface to be used in hospital environment

Direct outcome of T21 T22 documenting the stateofplay on existing tools components and methods preexisting knowhow and background knowledge as well as the identification collection and aggregation of the initial FAITH data sources and the privacy issues that need to be considered therein

D4.1(b) - Federated AI Framework & Methodology

An final report that defines the Federated AI framework for the FAITH project, building on D4.1(a) and using the project findings to deliver the definition of the final AI framework.

D4.3(c) - Explainable AI Framework

D4.3 will report on the framework for the human understandable explanation that expresses the rationale of the machine. It will define the library of ML and HCI modules that provide for more understandable AI implementations.

D4.2(b) - Advanced Analytics Methodology

D4.2(b) will present the final report on the AI models that were used to realise the central vision of FAITH, taking into account the earlier version of this deliverable, as well as learnings through the project lifetime.

D4.3(b) - Explainable AI Framework

D43 will report on the framework for the human understandable explanation that expresses the rationale of the machine It will define the library of ML and HCI modules that provide for more understandable AI implementations

D3.4(c) - FAITH Data Visualisation & Reporting

Direct outcome of T3.4 delivering the visualization and reporting interface to be used in hospital environment.

D4.1(a) - Federated AI Framework & Methodology

An initial report that defines the Federated AI framework for the FAITH project comparing leading opensource Federated Learning libraries eg TensorFlow Federated and OpenMined and also investigate modern nonfederated AIML deployment practices to discover the optimum deployment approach for Edge AI

D4.2(a) - Advanced Analytics Methodology

D42a will present an initial report on the AI models that will be used to realise the central vision of FAITH

D3.2(a) - FAITH Common Data Model

A conceptual data services model which will establish a data harmonization methodology to harmonize the diverse data types hospital data sensor data app data solving the interoperability aspects between the different sources so that analysis can be performed In addition

D2.3(a) - FAITH Framework Conceptual Architecture

Direct outcome of T25 and T26 documenting the technical requirements of the FAITH Architecture and the FAITH Reference Architecture

D6.1(b) - Technical Specification & Verification

Direct outcome of T6.1 and T6.2 providing specifications and verification methodology for the integration of the FAITH framework and pilot site trials.

D6.2(b) - Trial Environment Specification

Direct outcome of T6.3 providing the specification for the FAITH trial environment to be used in the intermediate and final trials.

D1.3 - Publishable Final Report

A Final project report

D7.3(a) - First report on Communication and Dissemination activities

Report describing the communication and dissemination activities implemented from M1 to M18 including an evaluation of the impact and effectiveness of those activities and an update of the initial plan if necessary

D2.1(b) - Domain SotA & Data Assets

An update to D21a Direct outcome of T21 T22 documenting the stateofplay on existing tools components and methods preexisting knowhow and background knowledge as well as the identification collection and aggregation of the initial FAITH data sources and the privacy issues that need to be considered therein

D4.4(b) - Model Shrink & Decompression Framework

D4.4(b) will report on state of the art compression techniques that were considered to reduce the size of the global model, the selection process and the final chosen approach.

D8.3 - Interoperability best practice guide

A direct output of T8.4, this report provides a plan towards our interoperability activities and guide to carry this beyond the project lifetime.

D8.1 - Market Analysis

This initial Market Analysis report will present and indepth analysis of the healthcare market with respect to post treatment and monitoring that can then be used to feed into our Exploitation plan and discussions

D2.2(b) - FAITH Requirements, Methodology and MVP

Direct outcome of T2.3 and T2.4 documenting the framework and trials specific functional and non-functional requirements, the integrated methodology that will drive the implementation of the FAITH framework towards a market aligned MVP that will drive its exploitation.

D3.3(b) - FAITH Data Privacy & Protection

A Data Privacy, Trust & Protection framework for the FAITH framework

D3.3(a) - FAITH Data Privacy & Protection

A Data Privacy Trust Protection framework for the FAITH framework

D3.4(b) - FAITH Data Visualisation & Reporting

Direct outcome of T3.4 delivering the visualization and reporting interface to be used in hospital environment.

D6.4 Final FAITH Validation and Analysis

Direct outcome of T6.5 providing the final analysis and validation of the FAITH framework, and recommendations for usage beyond the project’s lifetime

D4.4(a) - Model Shrink & Decompression Framework

D44 will report on early research into state of the art compression techniques that can be used to reduce the size of the global model as well as building a selection process and the chosen approach used at this stage of the project

D3.1 - Hospital Cloud/Network Infrastructures & Integration Methodology

A full architecture model of the hospital infrastructures and a definition of how the data from the FAITH ecosystem can be seamlessly integrated to provide a singular view of all data required by the professional stakeholders

D3.2(b) - FAITH Common Data Model

A conceptual data services model which will establish a data harmonization methodology to harmonize the diverse data types hospital data sensor data app data solving the interoperability aspects between the different sources so that analysis can be performed In addition

D5.2 Local Data Management Report

This describes the Local Data Management methodology used in the mobile app to protect the user data

D7.4 Report on Clustering specific activities

Report describing the activities implemented in Task 7.4, culminating with the hosting of the closure workshop, providing a future roadmap for our work, including policy discussions and a final report on the results of this workshop.

D2.3(b) - FAITH Framework Conceptual Architecture

Direct outcome of T2.5 and T2.6 documenting the technical requirements of the FAITH Architecture and the FAITH Reference Architecture.

D7.3(b) - Final report on Communication and Dissemination activities

Final report describing the communication and dissemination activities implemented from M19 to M36 including an evaluation of their effectiveness and impact.

D6.1(a) - Technical Specification & Verification

Direct outcome of T61 and T62 providing specifications and verification methodology for the integration of the FAITH framework and pilot site trials

D6.2(a) - Trial Environment Specification

Direct outcome of T63 providing the specification for the FAITH trial environment to be used in the intermediate and final trials

D4.3(a) - Explainable AI Framework

D43 will report on the framework for the human understandable explanation that expresses the rationale of the machine It will define the library of ML and HCI modules that provide for more understandable AI implementations

D2.2(a) - FAITH Requirements, Methodology and MVP

Direct outcome of T23 and T24 documenting the framework and trials specific functional and nonfunctional requirements the integrated methodology that will drive the implementation of the FAITH framework towards a market aligned MVP that will drive its exploitation

D6.3 Intermediate Trial Evaluation

Direct outcome of T6.4 detailing the results of the first set of trials as well as recommendations for the second final project trial

D5.5(b) - FAITH Appetite Tracking Module

This is the delivery of the FAITH Appetite Tracking module that will be incorporated into the mobile app.

D5.3(c) - FAITH Activity Monitor

This is the delivery of the module used by the mobile app to monitor user activity.

D5.6(b) - FAITH NLP Module

This is delivery of the FAITH NLP module that is to be incorporated into the mobile app

D5.5(a) - FAITH Appetite Tracking Module

This is the delivery of the FAITH Appetite Tracking module that will be incorporated into the mobile app.

D5.6(c) - FAITH NLP Module

This is delivery of the FAITH NLP module that is to be incorporated into the mobile app.

D5.3(a) - FAITH Activity Monitor

This is the delivery of the module used by the mobile app to monitor user activity

D5.3(b) - FAITH Activity Monitor

This is the delivery of the module used by the mobile app to monitor user activity.

D5.1(c) - FAITH Mobile App

This is final delivery of the mobile app as a smartphone app for end users.

D5.6(a) - FAITH NLP Module

This is delivery of the FAITH NLP module that is to be incorporated into the mobile app.

D5.5(c) - FAITH Appetite Tracking Module

This is delivery of the FAITH Appetite Tracking module that is to be incorporated into the mobile app.

D7.2 FAITH website

FAITH Website and Social Media accounts online with all contents and functionalities

D5.1(b) - FAITH Mobile App

This is the final delivery of the mobile app as a smartphone app for end users

D5.1(a) - FAITH Mobile App

This is the initial delivery of the mobile app as a smartphone app for end users.

Pubblicazioni

Protocol for the Implementation and Assessment of “MoodUP”: A Stepped Care Model Assisted by a Digital Platform to Accelerate Access to Mental Health Care for Cancer Patients Amid the COVID-19 Pandemic

Autori: Diana Frasquilho, Ricardo Matias, Jaime Grácio, Berta Sousa, Fernando Luís-Ferreira, João Leal, Fátima Cardoso, Albino J. Oliveira-Maia
Pubblicato in: International Journal of Environmental Research and Public Health, Numero 18, 2024, Pagina/e 4629, ISSN 1660-4601
Editore: International Journal of Environmental Research and Public Health (IJERPH)
DOI: 10.3390/ijerph18094629

A prospective observational study for a Federated Artificial Intelligence solution for moniToring mental Health status after cancer treatment (FAITH): study protocol

Autori: Raquel Lemos, Sofia Areias-Marques, Pedro Ferreira, Philip O’Brien, María Eugenia Beltrán-Jaunsarás, Gabriela Ribeiro, Miguel Martín, María del Monte-Millán, Sara López-Tarruella, Tatiana Massarrah, Fernando Luís-Ferreira, Giuseppe Frau, Stefanos Venios, Gary McManus, Albino J. Oliveira-Maia
Pubblicato in: BMC Psychiatry, Numero 22, 2022, ISSN 1471-244X
Editore: BioMed Central
DOI: 10.1186/s12888-022-04446-5

Sleeping Movement Detection Towards Mental Health Indicators - A Review

Autori: Fernando Luis-Ferreira, Joao Giao, Joao Sarraipa, Ricardo Jardim-Goncalves, Gary McManus, Philip O'Brien
Pubblicato in: 2020 IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC), 2022
Editore: IEEE
DOI: 10.1109/ice/itmc49519.2020.9198640

Design and methodological considerations for preparing clinical trial environments

Autori: UPM
Pubblicato in: 2023
Editore: IEEE

Digital Oncology: supporting clinical trials with biomedical sensors and wearables in cancer treatment and post-cancer follow up” mini-symposium

Autori: Maria Eugenia Beltran Jaunsaras (UPM)
Pubblicato in: 2024
Editore: IEEE

Attention-Aware Pedagogical Agent for Smart Book Reading

Autori: Andreia Artifice, Joao Sarraipa, Fernando Ferreira, Ricardo Jardim-Goncalves
Pubblicato in: Proceedings of the 10th International Conference on Software Development and Technologies for Enhancing Accessibility and Fighting Info-exclusion, 2023
Editore: ACM
DOI: 10.1145/3563137.3563175

Cancer Survivorship and AI for Well-being White Paper: A European research projects collaborative perspective

Autori: Callejas Carrión, Zoraida; Flynn, Tom
Pubblicato in: Numero 34, 2023
Editore: TFC

Risultati finali

Pubblicazioni

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