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Conference Papers Year : 2020

Synthesizing Quality Open Data Assets from Private Health Research Studies


Generating synthetic data represents an attractive solution for creating open data, enabling health research and education while preserving patient privacy. We reproduce the research outcomes obtained on two previously published studies, which used private health data, using synthetic data generated with a method that we developed, called HealthGAN. We demonstrate the value of our methodology for generating and evaluating the quality and privacy of synthetic health data. The dataset are from OptumLabs R Data Warehouse (OLDW). The OLDW is accessed within a secure environment and doesn't allow exporting of patient level data of any type of data, real or synthetic, therefore the HealthGAN exports a privacy-preserving generator model instead. The studies examine questions related to comorbidites of Autism Spectrum Disorder (ASD) using medical records of children with ASD and matched patients without ASD. HealthGAN generates high quality synthetic data that produce similar results while preserving patient privacy. By creating synthetic versions of these datasets that maintain privacy and achieve a high level of resemblance and utility, we create valuable open health data assets for future research and education efforts.
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hal-03158556 , version 1 (04-03-2021)


  • HAL Id : hal-03158556 , version 1


Andrew Yale, Saloni Dash, Karan Bhanot, Isabelle Guyon, John S Erickson, et al.. Synthesizing Quality Open Data Assets from Private Health Research Studies. BIS 2020 - International Conference on Business Information Systems, Jun 2020, Colorado Springs, United States. pp.324-335. ⟨hal-03158556⟩
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