Generating & Validating Synthetic Data

Healthcare synthetic data is typically produced via:

• Statistical methods: Parametric models (e.g., multivariate Gaussian) or bootstrapping of real data summaries
• Machine learning models: Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), or diffusion models

Popular open-source tools:

• SDV (Synthetic Data Vault): A Python library offering tabular, sequential, and relational synthesis
• Faker: Great for generating realistic-looking demo values (names, dates, addresses)
• Synthpop (R): For rapid prototyping of health survey data

Best practices:

1. Assess quality: Compare marginal and joint distributions of real vs. synthetic data
2. Test on real workflows: Run existing analysis pipelines (e.g., predictive models, dashboards) to ensure compatibility
3. Iterate & refine: Tune your synthesizer to balance realism with privacy (e.g., differentially private GANs)

By following these guidelines, you can confidently integrate synthetic data into your clinical research, machine-learning experiments, and software demos—without ever touching real patient records.