Synthetic generation of hematological data over federated computing frameworks

Secure Society Health & Wellbeing Artificial Intelligence

Project Vision

Haematological diseases (HDs) are a large group of disorders resulting from quantitative or qualitative abnormalities of blood cells, lymphoid organs and coagulation factors. Despite most of them (~74%) are rare, the overall number of HD affected patients worldwide is important, placing a considerable economic burden on healthcare systems and societies. Despite the existence of several collaborative research groups at national and EU level, current clinical approaches are often ineffective, particularly for rarest conditions, due to the relatively low number of patients per disease and the high number of unconnected clinical entities.

SYNTHEMA aims to establish a cross-border data hub where to develop and validate innovative AI-based techniques for clinical data anonymisation and synthetic data generation (SDG), to tackle the scarcity and fragmentation of data and widen the basis for GDPRcompliant research in RHDs. The project will focus on two representative RHD use cases: sickle-cell disease (SCD) and acute myeloid leukaemia (AML). SYNTHEMA will develop a federated learning (FL) infrastructure, equipped with secure multiparty computation (SMPC) and differential privacy (DF) protocols, connecting clinical centres bringing standardised, interoperable multimodal datasets and computing centres from academia and SME. This framework will be utilised to train the developed algorithms and perform SMPCbased global model aggregation in a privacy-preserving fashion. The resulting data will be validated for their clinical value, statistical utility and residual privacy risks.

The project will develop legal and ethical frameworks to guarantee privacy by-design in the collection and processing of health-related personal data and attain an ethics-wise algorithm co-creation. Project outcomes, including pipelines, standards and data, will be made openly available to stakeholders in the healthcare, academia and industry field, and contribute to existing rare disease registries

Project Objectives

SYNTHEMA aims to establish a cross-border health data hub for RHDs. The platform will be based on a privacy-preserving federated learning (FL) network, equipped with secure multi-party computation (SMPC) protocols and differential privacy (DP), connecting health data centres, academic research centres, industries and SMEs to advance translational and clinical research and care in RHDs.

  • O1. Provide novel methods and capabilities to generate synthetic multimodal clinical, omics and imaging data for RHDs with a validated clinical result.
  • O2. Develop de-identification, minimisation and anonymisation pipelines, including automatic assessment of privacy levels, at the service of clinical research and care.
  • O3. Consolidate and scale-up the use of FL applications, SMPC and DP solutions for privacy-preserving local algorithm training and global model aggregation.
  • O4. Ensure ethical and GDPR compliance in anonymised and synthetic data-driven research in RHDs.
  • O5. Ensure wide uptake and scalability of the developed methodologies and tools through effective stakeholder engagement, dissemination and open science practices.

IT Innovation's Role

IT Innovation leads Workpackage 5 : Data protection and privacy assessment

Project Funding

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