Integration of health initiatives with tools and services under the European Open Science Cloud (LETHE, FEMaLe and HealthyCloud)
- Ville Tenhunen ()
The aim of the workshop is to showcase the use of EOSC tools and services as instrument for increasing effectiveness of health initiatives. Specifically, health initiatives will serve as an intermediary between the public and private organizations managing care services and the scientific world and the European Cloud Infrastructure (ECI) through the direct collaboration with EOSC and will provide storage capacity and processing power through EGI infrastructure. The IT environment of health initiatives such as LETHE and HealthyCloud is going to guide health institutions in reusing the wealth of services and data available on EOSC and at the same time will enable them to publish new open data sets to be re-used seamlessly across borders, and among institutions and research disciplines, supporting health and research institutions to grasp the whole potential of the EOSC services and data, making it possible for researchers to use different data sources and making it possible to move, share and re-use data seamlessly across borders, and among institutions.
Schedule and presenters:
15:45 Welcome and overview of EOSC services and their potential for use in health initiatives
Ville Tenhunen, EGI
16:00 Concrete case: integration of LETHE project with EOSC
Presenter: Francesco Mureddu
16:30 Concrete case: integration of HealthyCloud project with EOSC
Presenter: Juan Gonzalez-Garcia, IACS
17:00 Prospective case: potential for the use of EOSC in the FEMaLe project
Presenters: Dmitrijs Bļizņuks, Riga Technical University and Ulrik Bak Kirk, Aarhus Universitet
17:30 Structured question & discussion
Moderator: Francesco Mureddu
18:00 Wrap up
Introduction to LETHE (λήθη) – A personalized prediction and intervention model for early detection and reduction of risk factors causing dementia, based on AI and distributed Machine Learning
Dementia is the most severe expression of cognitive impairment, the main cause of disability in elderly people, currently affecting nearly 50 million individuals worldwide. LETHE is an Horizon 2020 project designed to prevent cognitive decline in an ageing population at an early time point by a multi-domain interventional lifestyle approach built on a person centred digital solution. In LETHE a broad approach to prevention of Dementia is built at the intersection of clinical and technological displines. Icin that regard, LETHE is developing a data-driven risk factor prediction model for older individuals at risk of cognitive decline, novel digital biomarkers and a digital enabled intervention based on the evolution of the FINGER study. FINGER is a 2-year multi-center randomized controlled intervention trial carried out in Finland (Coordinated by the Finnish Institute for Health and Welfare, Helsinki). The project is performing on an existing clinical observation and intervention data from the 11-year follow-up FINGER study and re-using results from former EU projects’ validated sensing and interaction technologies.
Introduction to HealthyCloud - Health Research & Innovation Cloud
The need for advances in health and biomedical sciences requires that health research is performed timely, efficiently and oriented to high-quality results. To meet this need, health data must be oriented towards access, sharing, and secondary use in support of translational, clinical, and epidemiological-population level research. In other words, to maximise the impact of health research, it is necessary to adopt best practices on how to efficiently manage health data. In that regard, the objective of HealthyCloud is to generate a number of guidelines, recommendations and specifications that will enable distributed health research across Europe in the form of a Ready-to-implement Roadmap. This roadmap together with the feedback gathered from a broad range of stakeholders will be the basis to produce the final HealthyCloud Strategic Agenda for the European Health Research and Innovation Cloud (HRIC).
Introduction to FEMaLe – Finding Endometriosis Using Machine Learning
Healthcare tools for predicting and preventing diseases as well as personalising treatment and patient management offer great clinical benefits and cost reduction. The EU-funded FEMaLe project is working on a machine-learning multi-omics platform that can analyse omics data sets and feed the information into a personalised predictive model. The main focus of the project is to improve intervention for individuals with endometriosis, a condition where tissue normally lining the uterus grows outside the uterus. A combination of tools such as a mobile application and augmented reality surgery software will be developed, facilitating improved disease management and the delivery of precision medicine. The FEMaLe project will build bridges across disciplines and sectors to translate genetic and epidemiological knowledge into clinical tools that support decision-making in terms of diagnosis and care aimed at both general practice and highly specialised endometriosis clinics – all via machine learning and artificial intelligence.