Adversarial Factor Models for the Generation of Improved Autism Diagnostic Biomarkers

• URL: http://arxiv.org/abs/2111.15347v1
• Date: Fri, 24 Sep 2021 21:56:30 GMT
• Title: Adversarial Factor Models for the Generation of Improved Autism Diagnostic Biomarkers
• Authors: William E. Carson IV, Dmitry Isaev, Samatha Major, Guillermo Sapiro, Geraldine Dawson, David Carlson
• Abstract summary: We present applications of adversarial linear factor models in the creation of improved biomarkers for autism spectrum disorder (ASD) diagnosis. First, we demonstrate that an adversarial linear factor model can be used to remove confounding information from our biomarkers, ensuring that they contain only pertinent information on ASD. Second, we show this same model can be used to learn a disentangled representation of multimodal biomarkers that results in an increase in predictive performance.
• Score: 19.48133927082379
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Discovering reliable measures that inform on autism spectrum disorder (ASD) diagnosis is critical for providing appropriate and timely treatment for this neurodevelopmental disorder. In this work we present applications of adversarial linear factor models in the creation of improved biomarkers for ASD diagnosis. First, we demonstrate that an adversarial linear factor model can be used to remove confounding information from our biomarkers, ensuring that they contain only pertinent information on ASD. Second, we show this same model can be used to learn a disentangled representation of multimodal biomarkers that results in an increase in predictive performance. These results demonstrate that adversarial methods can address both biomarker confounds and improve biomarker predictive performance.

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