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For mental disorders, patients' underlying mental states are non-observedlatent constructs which have to be inferred from observed multi-domainmeasurements such as diagnostic symptoms and patient functioning scores.Additionally, substantial heterogeneity in the disease diagnosis betweenpatients needs to be addressed for optimizing individualized treatment policyin order to achieve precision medicine. To address these challenges, we proposean 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 formodeling multiple disease diagnostic measures as arising from the underlyingcauses (true mental states). It allows incorporation of the multivariate pre-and post-treatment outcomes as well as biological measures while preserving theinvariant structure for representing patients' latent mental states. Amulti-layer neural network is used to allow complex treatment effectheterogeneity. Optimal treatment policy can be inferred for future patients bycomparing their potential mental states under different treatments given theobserved multi-domain pre-treatment measurements. Experiments on simulated dataand a real-world clinical trial data show that the learned treatment policescompare favorably to alternative methods on heterogeneous treatment effects,and have broad utilities which lead to better patient outcomes on multipledomains.

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