Develop algorithms and tools for 360º patient monitoring using multi-source RWD
Develop the statistical models and methodology for use of RWD in clinical studies on cancer-related CCC
Develop and apply methods for causal modelling to perform inference, prediction and counterfactual reasoning on real-world observational data using deep latent confounder models
Develop a methodology for integrating detailed monitoring into existing clinical workflows, to improve management of patients with cancer-related chronic conditions
Define a common, interoperable data representation and implement a federated learning mechanism for cross-country data exchange on breast cancer and its comorbidities
Integrate the REBECCA advanced data management and analysis platform
Demonstrate the value of REBECCA RWD for advancing understanding of CCC in 3 studies on breast cancer comorbidities
Demonstrate the use of REBECCA advanced monitoring mechanism as a means of supporting clinical decisions, improving clinical outcomes and quality of life
Disseminate project methodologies, technology and study outcomes to research scientists, clinicians, public health bodies and stakeholders from the pharmaceutical and health insurance sectors around Europe
Develop and implement a plan for the sustainability and uptake of the project’s technical and methodology outcomes by clinicians and public health policy makers
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