A learning algorithm for predicting mental health symptoms and substance use.

ABSTRACT: Learning health systems use data to generate knowledge that informs clinical care, but few studies have evaluated how to leverage patient-reported mental health symptoms and substance use data to make patient-specific predictions. We developed a general Bayesian prediction algorithm that uses self-reported psychiatric symptoms and substance use within a population to predict future symptoms and substance use for individuals in that population. We validated our approach in 2444 participants from two clinical cohorts - the National Network of Depression Centers and the Johns Hopkins HIV Clinical Cohort - by predicting symptoms of depression, anxiety, and mania as well as alcohol, heroin, and cocaine use and comparing our predictions to observed symptoms and substance use. When we dichotomize

SUBMITTER: Fojo AT 

PROVIDER: S-EPMC8323478 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature