Digital Twins for Stress Management Utilizing Synthetic Data
Bibliographic record
Abstract
In the era of Medical 4.0, technologies such as big data, wearables, and Machine Learning (ML) are being deployed for predictive healthcare delivery. In this regard, digital twins have been adopted in healthcare to enhance diagnosis and personalized treatment. Health Digital Twins (HDTs) are virtual representations of patients’ data, mirroring the health state of patients to provide insights. Despite its promise, the existing works on HDTs relied on large historical data to train ML models. These historical data may be difficult to obtain due to privacy concerns of data fiduciaries and subjects. In this paper, we propose a Digital Twin for Stress Management (DTSM) that employs generative models to learn the distribution of patients’ data retrieved from a wearable device for stress management score prediction. To obtain a virtual replica of a patient’s data, we used synthetic data generative models such as Conditional Tabular Generative Adversarial Network (CTGAN), Tabular Variational Autoencoder (TVAE), Gaussian Copula, and Large Language Models (LLM) (REaLTabFormer and GReaT). The best result came from REaLTabFormer which accurately learns the distributions of the real data with a data quality score of approximately 93%. Furthermore, four well-known ML models trained on the synthetic data obtained a mean absolute error (MAE) of less than 5% in the prediction of stress score. Our experimental results show that the proposed DTSM can be used for the prediction of stress management scores.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".