Should we synthesize more than we need: impact of synthetic data generation for high-dimensional cross-sectional medical data
Bibliographic record
Abstract
OBJECTIVE: In medical research and education, generative artificial intelligence/machine learning (AI/ML) models to synthesize artificial medical data can enable the sharing of high-quality data while preserving the privacy of patients. Given that such data is often high-dimensional, a relevant consideration is whether to synthesize the entire dataset when only a task-relevant subset is needed. This study evaluates how the number of variables in training impacts fidelity, utility, and privacy of the synthetic data (SD). MATERIAL AND METHODS: We used 12 cross-sectional medical datasets, defined a downstream task with corresponding core variables, and derived 6354 variants by adding adjunct variables to the core. SD was generated using 7 different generative models and evaluated for fidelity, downstream utility, and privacy. Mixed-effect models were used to assess the effect of adjunct variables on the respective evaluation metric, accounting for the medical dataset as a random component. RESULTS: Fidelity was unaffected by the number of adjunct variables in 5/7 SDG models. Similarly, downstream utility remained stable in 6/7 (predictive task) and 5/7 (inferential task) SDG models. Where significant effects were observed, they were minimal, resulting, for example, in a 0.05 decrease in Area under the Receiver Operating Characteristic curve (AUROC) when adding 120 variables. Privacy was not impacted by the number of adjunct variables. DISCUSSION: Our findings show that fidelity, utility, and privacy are preserved when generating a more comprehensive medical dataset than the task-relevant subset. CONCLUSION: Our findings support a cost-effective, utility, and privacy-preserving way of implementing SDG into medical research and education.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.029 | 0.143 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".