Objective
Cardiovascular disease (CVD) remains the main cause of mortality worldwide, accounting for about a third of annual deaths. Re-use of both structured and unstructured data has the potential for major health benefits for the population suffering from CVD. Healthcare data re-use in Europe faces privacy and fragmentation issues, a high diversity in data formats and languages, and a lack of technical and clinical interoperability. DataTools4Heart (DT4H) will tackle such challenges and develop a comprehensive, federated, privacy-preserving cardiology data toolbox. This will include, in an integrated platform, standardised data ingestion and harmonisation tools providing a common data model, multilingual natural language processing, federated machine learning, differentially private data synthesis generation, and 7 language models adapted to the cardiology domain. DT4H virtual assistants will help scientists and clinicians navigate through large-scale multi-source cardiology data. These tools will be: i) implemented ensuring privacy-by-design and thorough compliance with European regulations and data standards; ii) optimised as based on multi-stakeholder user-centred requirements; and, iii) validated in 7 clinical sites across Europe.
DT4H will unlock currently inaccessible health data in unstructured data and allow multi-site federated data use. Together with its toolbox, DT4H will leave the legacy of a federated learning platform with an embedded metadata catalogue and AI virtual assistants, and the CardioSynth open database of synthetic data remaining as available for further research and AI experimentation. Effective use of the federated learning platform will improve enable improved AI diagnostic and treatment tools. Deployment of regulated solutions will extend existing healthcare management paradigms to reduce disease burden. Finally, DT4H tools, systems and methodology are highly generalised and will translate well to other clinical and research areas in medicine.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
- social sciencessociologydemographymortality
- natural sciencescomputer and information sciencesdatabases
- natural sciencescomputer and information sciencesdata sciencenatural language processing
- medical and health sciencesclinical medicinecardiologycardiovascular diseases
- natural sciencescomputer and information sciencesartificial intelligencemachine learning
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Programme(s)
Funding Scheme
HORIZON-RIA - HORIZON Research and Innovation ActionsCoordinator
08007 Barcelona
Spain
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Participants (16)
00198 Rome
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The organization defined itself as SME (small and medium-sized enterprise) at the time the Grant Agreement was signed.
08034 Barcelona
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06800 Ankara
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The organization defined itself as SME (small and medium-sized enterprise) at the time the Grant Agreement was signed.
151 25 Maroussi
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3584 CX Utrecht
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00187 Roma
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00144 Roma
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The organization defined itself as SME (small and medium-sized enterprise) at the time the Grant Agreement was signed.
062204 Bucuresti
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00168 Roma
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602 00 Brno
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08035 Barcelona
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Legal entity other than a subcontractor which is affiliated or legally linked to a participant. The entity carries out work under the conditions laid down in the Grant Agreement, supplies goods or provides services for the action, but did not sign the Grant Agreement. A third party abides by the rules applicable to its related participant under the Grant Agreement with regard to eligibility of costs and control of expenditure.
08007 Barcelona
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104 22 Stockholm
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014461 Bucuresti
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06903 Biot Sophia Antipolis
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1081 HV Amsterdam
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Partners (1)
Partner organisations contribute to the implementation of the action, but do not sign the Grant Agreement.
WC1E 6BT London
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