@article{ref1,
title="A learning algorithm for predicting mental health symptoms and substance use",
journal="Journal of psychiatric research",
year="2020",
author="Fojo, Anthony T. and Lesko, Catherine R. and Benke, Kelly S. and Chander, Geetanjali and Lau, Bryan and Moore, Richard D. and Zandi, Peter P. and Zeger, Scott L.",
volume="134",
number="",
pages="22-29",
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 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.<p /> <p>Language: en</p>",
language="en",
issn="0022-3956",
doi="10.1016/j.jpsychires.2020.12.049",
url="http://dx.doi.org/10.1016/j.jpsychires.2020.12.049"
}