The specific focus of iHELP is on early identification and mitigation of the risks associated with Pancreatic Cancer based on the application of advance AI-based learning and decision support techniques on the historic (primary) data of Cancer patients gathered from established data banks and cohorts. This analysis helps to (i) determine key risks associated with Pancreatic Cancer, (ii) develop predictive models for identified risks, and (iii) develop adaptive models for targeted prevention and intervention measures. Based on these developments, the project selects high-risk individuals that are invited to take part in the pilot activities or digital trials. The digital trials are carried out through user-centric mobile and wearable applications that apply proven usability principles to offer more awareness, more engaging experience for health monitoring, risk assessment and personalised decision support. Close collaboration between clinical and AI experts focus on drawing decision support against identified/predicted risks and providing personalised recommendations (e.g. lifestyle changes, behavioural nudges, screening test etc) to the participants in the digital trials. The iHELP (mobile and wearable) technology solutions help in validating iHELP solutions and raising health related awareness at individual level. The (secondary) data gathered through the mobile and wearable applications (concerning life style, behavioural, social interactions and response to targeted prevention and intervention measures) is integrated with primary data in the standardised HHR format – within a big data platform. Frugal AI-based learning techniques are developed to provide near real-time risk assessment based on the integrated and standardised HHR data. iHELP solutions are targeted at multiple stakeholders, including policymakers that will get decision support on the design of new screening programs and new guidelines for bringing improvements in clinical and lifestyle aspects.

The European health care system is moving toward personalised, distributed, and home-based services. This is made possible via new and improved connected medical devices (MDs) and in vitro diagnostic devices connected to the internet (together, CMDs), and will benefit health care providers in terms of reduced cost (fewer hospital beds) and improved service. Patients will see improved quality of life in terms of reduced travel time and reduced stress via treatment at home or where they want it. However, for these benefits to be fully realised, the cybersecurity of CMDs needs to be ensured. NEMECYS will benefit practitioners such as cybersecurity communities, MD manufacturers, CMD scenario system integrators and CMD scenario operators (e.g. health care providers), with downstream benefits to patients and the wider public, through more cost-effective and efficient care enabled via effective and streamlined cybersecurity. NEMECYS helps practitioners to (1) comply with MD regulations; (2) to be able to apply proportionate MD cybersecurity (too little security risks exposure, too much is costly and can obstruct clinical care) and (3) build in cybersecurity by design for both MDs and the connected scenarios they operate in. This is achieved by (1) providing recommendations for best practice and guidelines for MD cybersecurity by design, along with compliance assurance tooling; (2) providing a risk-benefit scheme to address cybersecurity risk balanced with clinical benefit; and (3) providing a set of specific tools to address MD cybersecurity by design and their deployment in connected scenarios. The NEMECYS team has cybersecurity risk experts, two hospitals who are already implementing IoT and remote care-based scenarios, three medical device manufacturers, major computer science research players and experienced systems integrators. This team is ideally placed to ensure that NEMECYS can enable practitioners to apply the right security at the right place, at low cost.

Data analytics is one of the main cornerstones in many enterprise architectures and the data lake paradigm is more and more adopted to assist organizations in taking reliable, accurate, and fast decisions. Although the initial approaches to address these issues saw the data lakes as the evolution of data warehouses to be implemented on-premises, cloud providers are nowadays including in their offerings platforms able to setup and run them. Nevertheless, the increasing amount of data generated at the edge and the need to enable the data sharing among organizations are posing new challenges in terms of performances, energy efficiency, and privacy/confidentiality which can be properly addressed with data lakes which are deployed along the whole computing continuum as well as building a federation of such data lakes. The ambition of TEADAL is to provide key cornerstone technologies to create stretched data lakes spanning the cloud-edge continuum and multi-cloud, providing privacy, confidentiality, and energy-efficient data management. The TEADAL data lake technologies will enable trusted, verifiable and energy efficient data flows, both in a stretched data lake and across a trustworthy mediatorless federation of them, based on a shared approach for defining, enforcing, and tracking privacy/confidentiality requirements balanced with the need for energy reduction.

Health care is an essential service that uses a great deal of sensitive personal data which has a high black market value being a lucrative target for data theft and ransomware attacks.The EU NIS Directive (EU 2016/1148) and GDPR (EU 2016/679) will harmonize and improve information security in Europe. Both require relevant ICT infrastructure operators to perform risk assessments, introduce appropriate security measures to manage identified risks, and report security breaches. Unfortunately, risk-based approaches are notoriously difficult to implement in a consistent and comprehensive fashion. They depend on a high level of understanding of both cybersecurity and of the system or network to be protected, are labour intensive and costly and typically done by small teams. This is increasingly inappropriate as health care providers introduce IoT systems, cloud services and (in the near future) 5G networks to provide services in which patients are more engaged, may own some of the devices used, and want access in hospitals, on the move or at home. The ProTego project will develop a toolkit and guidelines to help health care systems users address cybersecurity risks in this new environment by introducing 3 main advances over current approaches: Extensive use of machine intelligence: a combination of machine inference exploiting a priory knowledge for security-by-design, and machine learning from data for run-time threat detection and diagnosis; Advanced data protection measures: advanced encryption techniques and hardware based full memory encryption, and multi-stakeholder IAM to control access to and by user devices, to protect data at rest and provide ultra-secure data exchange portals; Innovative protocols for stakeholder education: using security-by-design analysis to target training and support stakeholders to contribute to networok overall security.The toolkit will be integrated and validated in IoT and BYOD-based case studies at two hospitals.