Quantifying Resemblance of Synthetic Medical Time-Series

Abstract

Access to medical data is often restricted due to privacy laws e.g. HIPAA and GDPR. We address the viability of substituting real data with synthetic data to protect privacy while maintaining utility. Medical data records are fundamentally longitudinal, with one patient having multiple health events influenced by covariates like gender, age etc. Synthesis of medical data, hence, falls under time-series generative modeling. We demonstrate methods to measure synthetic medical time-series quality on datasets from previously published synthetic data research. We deploy four time-series metrics to quantify resemblance in synthetic and real covariate plots while comparing baseline data generation methods.