Generative adversarial networks for modelling clinical biomarker profiles with race/ethnicity. Issue 5 (20th December 2022) - Nair, Rahul; Mohan, Deen Dayal; Frank, Sandra; Setlur, Srirangaraj; Govindaraju, Venugopal; Ramanathan, Murali
Abstract : Aims: Modelling biomarker profiles for under‐represented race/ethnicity groups are challenging because the underlying studies frequently do not have sufficient participants from these groups. The aim was to investigate generative adversarial networks (GANs), an artificial intelligence technology that enables realistic simulations of complex patterns, for modelling clinical biomarker profiles of under‐represented groups. Methods: GANs consist of generator and discriminator neural networks that operate in tandem. GAN architectures were developed for modelling univariate and joint distributions of a panel of 16 diabetes‐relevant biomarkers from the National Health and Nutrition Examination Survey, which contains laboratory and clinical biomarker data from a population‐based sample of individuals of all ages, racial groups and ethnicities. Conditional GANs were used to model biomarker profiles for race/ethnicity categories. GAN performance was assessed by comparing GAN outputs to test data. Results: The biomarkers exhibited non‐normal distributions and varied in their bivariate correlation patterns. Univariate distributions were modelled with generator and discriminator neural networks consisting of 2 dense layers with rectified linear unit‐activation. The distributions of GAN‐generated biomarkers were similar to the test data distributions. The joint distributions of the biomarker panel in the GAN‐generated data were dispersed and overlapped with the joint distribution of the test data as assessed by 3 multidimensional projection methods. Conditional GANs satisfactorily modelled the joint distribution of the biomarker panel in the Black, Hispanic, White and Other race/ethnicity categories. Conclusion: GAN is a promising artificial intelligence approach for generating virtual patient data with realistic biomarker distributions for under‐represented race/ethnicity groups.