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Generative Adversarial Networks for Modeling Clinical Biomarker Profiles in Under-Represented Groups
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Background: Clinical trial simulations and pharmacometric modeling of
biomarker profiles for under-represented groups are challenging because
the underlying studies frequently do not have sufficient participants
from these groups. Objectives: To investigate generative adversarial
networks (GANs), an artificial intelligence (AI) technology that enables
realistic simulations of complex patterns, for modeling 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 modeling univariate and joint
distributions of a panel of 16 diabetes-relevant biomarkers from the
National Health and Nutrition Examination Survey (NHANES), 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 modeled with generator and discriminator
neural networks consisting of two 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 three
multi-dimensional projection methods. Conditional GANs satisfactorily
modeled the joint distribution of the biomarker panel in the Black,
Hispanic, White, and “Other” race/ethnicity categories. Conclusions:
GAN are a promising AI approach for generating virtual patient data with
realistic biomarker distributions for under-represented race/ethnicity
groups.
Title: Generative Adversarial Networks for Modeling Clinical Biomarker Profiles in Under-Represented Groups
Description:
Background: Clinical trial simulations and pharmacometric modeling of
biomarker profiles for under-represented groups are challenging because
the underlying studies frequently do not have sufficient participants
from these groups.
Objectives: To investigate generative adversarial
networks (GANs), an artificial intelligence (AI) technology that enables
realistic simulations of complex patterns, for modeling 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 modeling univariate and joint
distributions of a panel of 16 diabetes-relevant biomarkers from the
National Health and Nutrition Examination Survey (NHANES), 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 modeled with generator and discriminator
neural networks consisting of two 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 three
multi-dimensional projection methods.
Conditional GANs satisfactorily
modeled the joint distribution of the biomarker panel in the Black,
Hispanic, White, and “Other” race/ethnicity categories.
Conclusions:
GAN are a promising AI approach for generating virtual patient data with
realistic biomarker distributions for under-represented race/ethnicity
groups.
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