To contribute with a preventive approach for Public Health strategies in facing such pandemic situation, while encompassing such concerns, our project proposes a smartphone app and trustworthy Artificial Intelligence (AI) distributed and service-based platform, to identify symptomatic and asymptomatic patients as well as assess the risk of exposure, where smartphone collected data, including health data, will be securely stored on the device (using a secure data vault), and the access to such secure data vault will need to be controlled and authorized by the user in his/her own device. The basic trust principles are threefold: user-centric, transparent, and adherence to open-source.
We envisage addressing 2 scenarios: (1) risk Groups, i.e. diabetic, hypertensive patients, with heart disease. In this scenario, we plan to monitor various discontinuous physiological variables (coughing, breathing, temperature, heart rate, O2 saturation, physical activity); (2) COVID-19 patients in home care, with the need for supplementary hardware to register the same physiological variables, possibly continuous, 24/7. For both scenarios we will collect also social media data. To serve these scenarios, we will develop a smartphone app and an AI secure backend adopting a federated edge computing architecture based on micro-services. With such system, we will collect data in a secure way, which will be analyzed using AI methodologies (particularly, Machine Learning, ML), aiming at aiding and alerting the user and his/her doctor regarding clinical situations such as: (1) establishing a diagnosis of COVID-19; (2) assessing the risk of being infected by the virus and advising the user, as well as other citizens (in a fully anonymized way), with whom he/she has been recently in contact, to perform a COVID-19 test.
|Approval Date: 16-12-2020|
|Start Date: 01-02-2021|
|End Date: 31-01-2023|
|Team: Hugo Humberto Plácido da Silva|
|Groups: Pattern and Image Analysis – Lx|
|Partners: Associação para Investigação e Desenvolvimento da Faculdade de Medicina (AIDFM/FM/ULisboa), Instituto Universitário de Lisboa (ISCTE-IUL)|
|Local Coordinator: Hugo Humberto Plácido da Silva|