SleepSynth: Evaluating the use of Synthetic Data in Health Digital Twins
Bibliographic record
Abstract
Health Digital Twins (HDTs) are virtual replicas of a patient’s physical/actual data. The major setbacks for applying Machine Learning (ML) in HDTs are the lack of availability of patients’ data due to privacy concerns and Artificial Intelligence (AI) bias. Given these shortcomings, synthetic data has been leveraged to solve privacy issues and increase diversity in datasets. In this paper, we evaluate four synthetic data generation models namely, Gaussian Copula, Conditional Tabular Generative Adversarial Network (CTGAN), CopulaGAN, and Tabular Variational Autoencoder (TVAE) which are used to generate synthetic data for actual sleep data retrieved from a wearable device. Gaussian Copula performed best in capturing the correlation between the variables with the real data with a quality score of approximately 96%. Additionally, we evaluate the efficacy of the synthetic generation models by training five well-known ML models on the generated synthetic data. Our experimental results show that the ML models trained on the synthetic data achieve an MAE (Mean Absolute Error) of less than 10% in the prediction of sleep quality score. The results from this work indicate that synthetic data could be used for ML tasks while preserving the privacy of data subjects.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".