Year: 2021
Type: conference-paper
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.
Institutions: Rensselaer Polytechnic Institute, Optum (United States), Birla Institute of Technology and Science, Pilani - Goa Campus, Birla Institute of Technology and Science, Pilani, Centre National de la Recherche Scientifique, Institut national de recherche en sciences et technologies du numérique, Université Paris-Saclay, Laboratoire Interdisciplinaire des Sciences du Numérique
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W3206085496