Synthetic Data Quality Score Calculator

Estimate the quality score of synthetic data for ML training using fidelity, diversity, and privacy metrics. Compare against real data baselines using statistical distance measures

Each component has a specific meaning:

• Fidelity — The fidelity recorded for the patient or scenario being assessed.
• Diversity — The diversity recorded for the patient or scenario being assessed.
• Privacy metrics — The privacy metrics recorded for the patient or scenario being assessed.

Note: Interpret the synthetic data quality result against the clinical thresholds and context described above.

Enter the fidelity, diversity, privacy metrics for the patient or scenario you are assessing. Estimate the quality score of synthetic data for ML training using fidelity, diversity, and privacy metrics. Compare against real data baselines using statistical distance measures. Use the synthetic data quality result to inform your clinical assessment.