Incorporation of Synthetic Data Generation Techniques within a Controlled Data Processing Workflow in the Health and Wellbeing Domain

Abstract

To date, the use of synthetic data generation techniques in the health and wellbeing domain has been mainly limited to research activities. Although several open source and commercial packages have been released, they have been oriented to generating synthetic data as a standalone data preparation process and not integrated into a broader analysis or experiment testing workflow. In this context, the VITALISE project is working to harmonize Living Lab research and data capture protocols and to provide controlled processing access to captured data to industrial and scientific communities. In this paper, we present the initial design and implementation of our synthetic data generation approach in the context of VITALISE Living Lab controlled data processing workflow, together with identified challenges and future developments. By uploading data captured from Living Labs, generating synthetic data from them, developing analysis locally with synthetic data, and then executing them remotely with real data, the utility of the proposed workflow has been validated. Results have shown that the presented workflow helps accelerate research on artificial intelligence, ensuring compliance with data protection laws. The presented approach has demonstrated how the adoption of state-of-the-art synthetic data generation techniques can be applied for real-world applications.

Synthetic Subject Generation with Coupled Coherent Time Series Data

“…In terms of privacy, the use of Wilcoxon signed-rank tests, the analysis of re-identification risks and computation of similarity to real data will be considered. Finally, it is intended to incorporate the proposed approaches in the VITALISE controlled data processing workflow presented by Hernandez et al [4]. This workflow enables…”

“…In this context, a workflow to make LL data accessible for external researchers by generating synthetic data (SD) has been proposed by Hernandez et al [4]. As explained in the proposed workflow, SD is created with a clear purpose, enabling researchers to develop algorithms and analyses locally using it.…”

dsSynthetic: synthetic data generation for the DataSHIELD federated analysis system

“…Synthetic data generation is also available as part of other privacy preserving frameworks like OpenSAFELY [ 15 ] and other packages [ 16 – 18 ]. A version of synthpop for DataSHIELD is offered in [ 16 ] but the simstudy method is not available, nor are the detailed workflows for generating synthetic data.…”

A Semi-Supervised Machine Learning Approach in Predicting High-Risk Pregnancies in the Philippines

“…The SDV generates synthetic data by applying mathematical techniques and machine learning models, such as the deep learning model. Several studies have embraced the use of synthetic data, which can be applied for real-world applications [ 21 ]. An additional 900 new rows were generated for testing.…”