Periodic Reporting for period 2 - FAITH (a Federated Artificial Intelligence solution for moniToring mental Health status after cancer treatment)
Periodo di rendicontazione: 2021-05-01 al 2022-07-31
In the past few years, machine learning has led to major breakthroughs in various areas, such as natural language processing, computer vision and speech recognition. Much of this success has been based on collecting huge amounts of data. While machine learning can benefit from this “big data” to generate state-of-the-art models, most healthcare data is hard to obtain due to legal, privacy, technical, and data-ownership challenges, especially among international institutions where HIPAA and GDPR concerns need to be addressed. An emerging solution to this problem is Federated Learning, a new approach to machine learning where the training data does not leave the users’ computer at all. Instead of sharing their data, the devices of the users computes weight updates themselves using their locally available data. It is a way of training a model without directly inspecting users’ data on a server. The goal of the FAITH project is to provide an ‘AI Angel’ that remotely analyses depression markers, using federated learning, to predict negative trends in their disease trajectory, giving their healthcare providers advanced warnings to allow for timely intervention. These markers are treated under several distinct categories; Activity, Sleep, Appetite, and Voice.
The need for two related, but distinct framework implementations emerged as the project matured; a framework that would best serve the needs of the FAITH trial in terms of data capture and analysis, and one that would deliver the results of that analysis using federated learning (FL). An analysis of FL techniques was led in WP4, along with review of many of the necessary, supporting tools and libraries. Fundamental of course is the FAITH mobile application. In this regard a core code project including libraries necessary was provided, alongside with UI integration procedures, while an integration plan was developed and has been put in force to monitor the integration activities that have been performed. The project partners have had in-depth weekly meetings to define the clinical trial protocol, the Case Report Form (CRF) and integrated statistical plan, which needs to be approved before starting the trial.
Additionally, this reporting period has seen the rigorous specification of variables as part of study protocol, app development to capture these variables, and trial specification to frame it all. In parallel, the initial versions of the WP4 deliverables have laid out our approach towards a federated framework, explainable AI, and model compression; all important components in terms of ultimately deploying federated models.
In terms of implementing a voice/NLP interface to gauge a user’s mental outlook, this objective is multi-faceted, and consequently the project has worked towards it in several paths: capturing voice input via the mobile application and offloading for vocal feature analysis to cater for the trial, experimenting with both on-device and browser-based approaches to the vocal feature analysis so that ultimately this objective could be achieved in a federated privacy-preserving manner, investigation of different methods for capturing the voice recording (based on methods used in the literature), investigating methods for using NLP to automate questionnaire delivery e.g. speech recognition using TensorFlow.js.
A significant outcome of this reporting period was the extent of the dissemination efforts. The FAITH Consortium engaged in dedicated healthcare stakeholder clustering activities, in addition to the standard dissemination and communication activities. These activities targeted the healthcare community, to collect feedback able to influence the direction FAITH should go in. FAITH is a member of the Health & Care cluster funded by the European Commission. In parallel FAITH formed the establishment of a “Cancer Survivorship – AI for Well-being” cluster, five EU-funded projects brought together by common research interests (mental health, well-being, depression, and patient support) and a common approach (a participatory research vision).