Learning Individualized Treatment Rules for Multiple-Domain Latent Outcomes.

ABSTRACT: For many mental disorders, latent mental status from multiple-domain psychological or clinical symptoms may perform as a better characterization of the underlying disorder status than a simple summary score of the symptoms, and they may also serve as more reliable and representative features to differentiate treatment responses. Therefore, in order to address the complexity and heterogeneity of treatment responses for mental disorders, we provide a new paradigm for learning optimal individualized treatment rules (ITRs) by modeling patients' latent mental status. We first learn the multi-domain latent states at baseline from the observed symptoms under a restricted Boltzmann machine (RBM) model, through which patients' heterogeneous symptoms are represented using an economical number of lat

SUBMITTER: Chen Y 

PROVIDER: S-EPMC8589272 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature