Title | Medical data sharing and synthetic clinical data generation - maximizing biomedical resource utilization and minimizing participant re-identification risks. |
Publication Type | Journal Article |
Year of Publication | 2025 |
Authors | Marino S, Cassidy R, Nanni J, Wang Y, Liu Y, Tang M, Yuan Y, Chen T, Sinha A, Pandian B, Dinov ID, Burns ML |
Journal | NPJ Digit Med |
Volume | 8 |
Issue | 1 |
Pagination | 526 |
Date Published | 2025 Aug 16 |
ISSN | 2398-6352 |
Abstract | The sensitive nature of electronic health records (EHR) and wearable data presents challenges in sharing biomedical resources while minimizing re-identification risks. This article introduces an end-to-end, titratable pipeline that generates privacy-preserving "digital twin" datasets from complex EHR and wearable-device records (Apple Watch data from 3029 participants) using DataSifter and Synthetic Data Vault (SDV) methods. Various obfuscation levels were applied (DataSifter: small, medium, large; SDV: CTGAN, Gaussian Copula) and benchmarked using utility (statistical fidelity, machine learning performance) and privacy (re-identification risk, detection likelihood) metrics. The highest-obfuscation DataSifter twin delivered the strongest privacy protection (0.83) while preserving key statistical and predictive signals (83.1% confidence interval overlap in regression models), outperforming SDV, particularly for longitudinal data. Despite declining performance in machine learning tasks with higher obfuscation, utility was generally preserved. The study underscores the importance of digital twin datasets and highlights DataSifter's adaptability in privacy-utility trade-offs, advocating its utility for secure data sharing. |
DOI | 10.1038/s41746-025-01935-1 |
Alternate Journal | NPJ Digit Med |
PubMed ID | 40818998 |
PubMed Central ID | PMC12357896 |
Grant List | 1916425, 1734853, 1636840 / / NSF / R01 MH126137 / MH / NIMH NIH HHS / United States R01MH126137, R41TR004515, T32GM141746 / GF / NIH HHS / United States R41 TR004515 / TR / NCATS NIH HHS / United States T32 GM141746 / GM / NIGMS NIH HHS / United States |