A learning algorithm for predicting mental health symptoms and substance use

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 dichotomized mental health symptoms as moderate-severe vs. none-mild, individual predictions yielded areas under the ROC curve (AUCs) of 0.84 [95% confidence interval 0.80–0.88] and 0.85 [0.82–0.88] for symptoms of depression in the two cohorts, AUCs of 0.84 [0.79–0.88] and 0.85 [0.82–0.88] for symptoms of anxiety, and an AUC of 0.77 [0.72–0.82] for manic symptoms. Predictions of substance use yielded an AUC of 0.92 [0.88–0.97] for heroin use, 0.90 [0.82–0.97] for cocaine use, and 0.90 [0.88–092] for alcohol misuse. This rigorous, mathematically grounded approach could provide patient-specific predictions at the point of care. It can be applied to other psychiatric symptoms and substance use indicators, and is customizable to specific health systems. Such approaches can realize the potential of a learning health system to transform ever-increasing quantities of data into tangible guidance for patient care.