Generating and evaluating cross‐sectional synthetic electronic healthcare data: Preserving data utility and patient privacy
Open accessAbstract
Electronic healthcare record data have been used to study risk factors of disease, treatment effectiveness and safety, and to inform healthcare service planning. There has been increasing interest in utilizing these data for new purposes such as for machine learning to develop predictive algorithms to aid diagnostic and treatment decisions. Synthetic data could potentially be an alternative to real‐world data for these purposes as well as reveal any biases in the data used for algorithm development. This article discusses the key requirements of synthetic data for multiple purposes and proposes an approach to generate and evaluate synthetic data focused on, but not limited to, cross‐sectional healthcare data. To our knowledge, this is the first article to propose a framework to generate and evaluate synthetic healthcare data with the aim of simultaneously preserving the complexities of ground truth data in the synthetic data while also ensuring privacy. We include findings and new insights from synthetic datasets modeled on both the Indian liver patient dataset and UK primary care dataset to demonstrate the application of this framework under different scenarios.
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Training on simulated data mitigates concerns regarding privacy breaches, in which specific individuals can be identified from the training data.
“Training on simulated data mitigates concerns regarding privacy breaches, in which specific individuals can be identified from the training data82,83.”
From Few shot learning for phenotype-driven diagnosis of patients with rare genetic diseases (2025) · cites this paper as [83]
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Synthetic data generation for tabular health records: A systematic review
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- Authors
- Wang, Z., Myles, P., & Tucker, A.
- Affiliations
- Clinical Practice Research Datalink (GB)
- Brunel University of London (GB)
- Corresponding author
- Wang, Zhenchen
- Venue
- Computational Intelligence
- Publisher
- Wiley
- Published
- 2021-01-03
- Volume
- 37
- Issue
- 2
- Pages
- 819-851
- ISSN
- 0824-7935 (ISSN-L)
- 1467-8640
- OpenAlex
- W3120071807
- Cited by
- 65
- References
- 49
- Citations by year
- 2026: 7 · 2025: 15 · 2024: 16 · 2023: 15 · 2022: 9 · 2021: 3
- FWCI
- 3.78
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Computational Intelligence (publishedVersion, cc-by-nc-nd)
- Brunel University Research Archive (BURA) (Brunel University London) (submittedVersion, cc-by-nc-nd)
- Funders
- Department for Business, Energy and Industrial Strategy, UK Government (104676)
- Innovate UK
- Pioneer Fund
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- synthetic data generation, electronic health records, cross-sectional data, data utility, patient privacy, data privacy, synthetic data evaluation, Indian Liver Patient Dataset, data bias
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