The use of multi-task multi-modal data can be challenging, but it is well known that
leveraging the complementary information can provide increase sensitivity. In this
work we present an approach to extract guided multimodal signatures using a multivariate
data fusion approach and show in both longitudinal and independent data that the resulting
signatures are predictive of substance use, ADHD, major depression, and schizophrenia.
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© 2020 Published by Elsevier Inc.
