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. Three machine-learning models (Naive Bayesian Classifier, Random Forest, eXtreme Gradient Boosting) are being developed for EHR-based risk prediction of BD. Models are being tested for timeliness, sensitivity, specificity, and calibration at the model development sites and at partner sites.


Associate Professor of Medicine

Vanderbilt University Medical Center

Professor and Chair, Department of Population Health Sciences


PhD Student

Vanderbilt University

Application Developer

Vanderbilt University Medical Center


Harvard and Massachusetts General Hospital

Workgroup Lead

Associate Professor

Vanderbilt University Medical Center