Protecting Multiple Sensitive Attributes in Synthetic Micro-data

This paper explores the use of synthetic data as a privacy-preserving measure in data analysis, emphasizing the need to protect sensitive attributes while maintaining data utility. It investigates enhancements to the DataSynthesizer model, using Bayesian Networks to generate synthetic data that safeguards multiple sensitive attributes against inference attacks. The study contributes to the field by analyzing the impact of these techniques on data utility, presented at the 2023 IEEE International Conference on Big Data.

Federated learning for causal inference using deep generative disentangled models

In the context of decentralized and privacy-constrained healthcare data settings, we introduce an innovative approach to estimate individual treatment effects (ITE) via federated learning. Emphasizing the critical importance of data privacy in healthcare, especially when drawing on data from various global hospitals, we address challenges arising from data scarcity and specific treatment assignment criteria influenced by the availability of the medication of interest. Our methodology uses federated learning applied to neural network-based generative causal inference models to bridge the gap between decentralized and centralized ITE estimation on a benchmark dataset.

Sickle cell disease landscape and challenges in the EU: the ERN-EuroBloodNet perspective

Sickle cell disease is a hereditary multiorgan disease that is considered rare in the EU. In 2017, the Rare Diseases Plan was implemented within the EU and 24 European Reference Networks (ERNs) were created, including the ERN on Rare Haematological Diseases (ERN-EuroBloodNet), dedicated to rare haematological diseases. The role of the ERN-EuroBloodNet is to improve the overall approach to and the management of individuals with sickle cell disease in the EU through specific on the pooling of expertise, knowledge, and best practices; the development of training and education programmes; the strategy for systematic gathering and standardisation of clinical data; and its reuse in clinical research.

Synthetic Data Generation by Artificial Intelligence to Accelerate Research and Precision Medicine in Hematology

Synthetic data are artificial data generated without including any real patient information by an algorithm trained to learn the characteristics of a real source data set and became widely used to accelerate research in life sciences. In this work researchers apply generative artificial intelligence to build synthetic data in different hematologic neoplasms; develop a synthetic validation framework to assess data fidelity and privacy preservability; and test the capability of synthetic data to accelerate clinical/translational research in hematology.

D7.2 Ethic Design requirements – Public deliverable

This deliverable presents the requirements for an ethical and trustworthy design of SYNTHEMA technologies. This deliverable outlines the regulatory challenges for AI systems in healthcare, discussing the role of regulations as both a barrier and a driver of technology innovation. Then, the seven principles for Trustworthy AI are discussed: these introduce the forthcoming AI Act. The deliverable analyses the forthcoming regulation and the risk categories and gives an overview of the standardisation activities related to the AI Act in the EU. The Value-Sensitive Design methodology is presented, and its implementation in the first year of SYNTHEMA is discussed. In the final section, the discussion on the ethical framework is narrowed down to seven key ethics requirements for the development of the SYNTHEMA technologies.

D7.1 Quality Assurance Guidelines – Public deliverable

The present deliverable constitutes a reference document at consortium partners’ disposal for ensuring the highest quality in the execution of the project, by providing a framework of procedures, guidelines, standards and rules to guarantee the quality of project outcomes (e.g., deliverables, periodic reports, software, infrastructure).

D6.4 Project website – Public deliverable

The SYNTHEMA website ( includes key information about the project and aims to communicate to both professionals and wider audiences.

D6.1 Impact Master Plan

This document outlines the project dissemination, communication exploitation strategies for the SYNTHEMA Horizon Europe project.

D5.1 Data management plan – Public deliverable

This deliverable provides the Data management plan (DMP) initial draft for SYNTHEMA. SYNTHEMA has populated this DMP in line with recommended EC guidelines. It will be updated as the project proceeds. SYNTHEMA is novel in that the data assets to beproduced will be synthetic in nature. There may yet be a risk of re-identification given that the synthetic data will be generated based on inference from real data. The plan therefore considers this paradigm carefully in its plans.

Synthema 2nd Newsletter

Welcome to the SYNTHEMA’s 2nd Newsletter – Dive into the heart of a Horizon Europe initiative revolutionizing rare hematological diseases research. This edition brings you a glimpse into SYNTHEMA’s core, synergy in action (SYNTHEMA/Genodmed4All) and voices from the 8th General Assembly Harmony Plus.

Synthema 1st Newsletter

Dive into the heart of a Horizon Europe initiative revolutionizing rare hematological diseases research. This edition delves into SYNTHEMA’s approach to overcoming data fragmentation and advancing research through a collaborative platform, linking clinical centers, research facilities, and industries. Explore SYNTHEMA’s focus on Sickle-Cell Disease (SCD) and Acute Myeloid Leukemia (AML), and its five strategic research objectives.

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