Should we synthesize more than we need: impact of synthetic data generation for high-dimensional cross-sectional medical data
Our findings support a cost-effective, utility, and privacy-preserving way of implementing SDG into medical research and education.
Authors
- Lisa Pilgram (ORCID: https://orcid.org/0000-0002-1020-0650)
- Dan Liu (ORCID: https://orcid.org/0000-0001-9632-4736)
- Samer El Kababji (ORCID: https://orcid.org/0000-0002-7642-2280)
- Khaled El Emam (ORCID: https://orcid.org/0000-0003-3325-4149)
Institutions
- University of Ottawa (CA)
- Children's Hospital of Eastern Ontario (CA)
- Ottawa Public Health (CA)
- Charité - Universitätsmedizin Berlin (DE)
Publication Details
- Journal
- Journal of the American Medical Informatics Association
- Published
- 2025-09-24
- DOI
- https://doi.org/10.1093/jamia/ocaf169
- Citations
- 1
- Primary Topic
- Privacy-Preserving Technologies in Data
- Type
- article
- Field-Weighted Citation Impact
- 2.48