Bipolar Disorder Risk Prediction

We aim to use clinical characteristics of individuals with a bipolar disorder (BD) diagnosis to identify undiagnosed cases and individuals at high predicted risk for being a case.

At three participating sites (VUMC, MGB, GHS), we will derive risk prediction models for BD using several machine-learning approaches (Naïve Bayes, Random Forest, XGBoost).

The performances of these distinct models will be systematically compared and ensembled at each of the participating sites.