Big Data

The expected impact of BigData@Heart included: (1) definitions of disease and outcome that are universal, computable, and relevant for patients, clinicians, industry and regulators, (2) informatics platforms that link, visualise and harmonise data sources of varying types, completeness and structure; (3) data science techniques to develop new definitions of disease, identify new phenotypes, and construct personalized predictive models; and (4) guidelines that allow for cross-border usage of big data sources acknowledging ethical and legal constraints as well as data security. Practically, by accessing and harmonizing European wide data sets, the project´s ambition wass to design prognosis algorithms that can predict the evolution of disease based on previous medical history, hospitalization, and country-specific statistics. BigData@Heart formed seven work packages that work interactively and transversally to create the research framework and the new methodologies will be tested in a set of six case studies.

Despite remarkable progress in the management of ACS, AF and HF, their disease burden remains high. Improvement of related mortality & hospitalization rates, quality of life issues, health care expenditures and loss of productivity is crucial. Optimal management of these conditions is complicated by their complex etiologies, poor definition at the molecular level, and the added burden of co- / multi-morbidities. This leads to unpredictable and large variation in interindividual therapeutic response, heterogeneous prognoses, and treatment guidelines that are based on conventional risk factors & clinical markers of end-organ damage.  All of these  barriers pose major problems in the development and delivery of targeted CVD treatments.