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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