NFDI4Health workflow and service for synthetic data generation, assessment and risk management

TL;DR

This paper presents a comprehensive workflow and web-based tools for generating, assessing, and managing the risks of synthetic health data, facilitating privacy-preserving data sharing for AI research.

Contribution

It introduces a novel workflow and publicly accessible platform integrating state-of-the-art AI models for synthetic data creation and evaluation within Germany's NFDI4Health initiative.

Findings

Effective synthetic data generation using VAMBN and MultiNODEs

Web-based tool SYNDAT for quality and risk assessment

Successful application on ADNI and RKI datasets

Abstract

Individual health data is crucial for scientific advancements, particularly in developing Artificial Intelligence (AI); however, sharing real patient information is often restricted due to privacy concerns. A promising solution to this challenge is synthetic data generation. This technique creates entirely new datasets that mimic the statistical properties of real data, while preserving confidential patient information. In this paper, we present the workflow and different services developed in the context of Germany's National Data Infrastructure project NFDI4Health. First, two state-of-the-art AI tools (namely, VAMBN and MultiNODEs) for generating synthetic health data are outlined. Further, we introduce SYNDAT (a public web-based tool) which allows users to visualize and assess the quality and risk of synthetic data provided by desired generative models. Additionally, the utility of the proposed methods and the web-based tool is showcased using data from Alzheimer's Disease Neuroimaging Initiative (ADNI) and the Center for Cancer Registry Data of the Robert Koch Institute (RKI).