Representation Learning for Integrating Multi-domain Outcomes to Optimize Individualized Treatments.

ABSTRACT: For mental disorders, patients' underlying mental states are non-observed latent constructs which have to be inferred from observed multi-domain measurements such as diagnostic symptoms and patient functioning scores. Additionally, substantial heterogeneity in the disease diagnosis between patients needs to be addressed for optimizing individualized treatment policy in order to achieve precision medicine. To address these challenges, we propose an integrated learning framework that can simultaneously learn patients' underlying mental states and recommend optimal treatments for each individual. This learning framework is based on the measurement theory in psychiatry for modeling multiple disease diagnostic measures as arising from the underlying causes (true mental states). It allows incorp

SUBMITTER: Chen Y 

PROVIDER: S-EPMC8593913 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature