Synthetic data generation for a longitudinal cohort study – evaluation, method extension and reproduction of published data analysis results

Access to individual-level health data is essential for gaining new insights and advancing science. In particular, modern methods based on artificial intelligence rely on the availability of and access to large datasets. In the health sector, access to individual-level data is often challenging due...

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Bibliographic Details
Published in:Scientific reports Vol. 14; no. 1; pp. 14412 - 15
Main Authors: Kühnel, Lisa, Schneider, Julian, Perrar, Ines, Adams, Tim, Moazemi, Sobhan, Prasser, Fabian, Nöthlings, Ute, Fröhlich, Holger, Fluck, Juliane
Format: Journal Article
Language:English
Published: London Nature Publishing Group UK 22.06.2024
Nature Publishing Group
Nature Portfolio
Subjects:
692/700
692/700/2814
Algorithms
Artificial Intelligence
Bioinformatics
Cohort analysis
Data Analysis
Datasets
Deep learning
Electronic health records
Humanities and Social Sciences
Humans
Longitudinal Studies
Machine learning
multidisciplinary
Personal health
Science
Science (multidisciplinary)
Statistical analysis
ISSN:2045-2322, 2045-2322
Online Access:Get full text
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Summary:Access to individual-level health data is essential for gaining new insights and advancing science. In particular, modern methods based on artificial intelligence rely on the availability of and access to large datasets. In the health sector, access to individual-level data is often challenging due to privacy concerns. A promising alternative is the generation of fully synthetic data, i.e., data generated through a randomised process that have similar statistical properties as the original data, but do not have a one-to-one correspondence with the original individual-level records. In this study, we use a state-of-the-art synthetic data generation method and perform in-depth quality analyses of the generated data for a specific use case in the field of nutrition. We demonstrate the need for careful analyses of synthetic data that go beyond descriptive statistics and provide valuable insights into how to realise the full potential of synthetic datasets. By extending the methods, but also by thoroughly analysing the effects of sampling from a trained model, we are able to largely reproduce significant real-world analysis results in the chosen use case.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-024-62102-2