Bayesian Additive Regression Trees (BART) is a Bayesian non-parametric machine learning ensemble model with excellent out of sample predictive capabilities; yet, its use in biostatistical analyses and biomedical research is in its infancy. One barrier is the implicit assumption of independence between observations that is inherent in the original BART model. In contrast, repeated measures data is common in biomedical research and so using BART may not be justified. For instance, in personalized medicine, BART can be applied to clinical trial data with the goal of finding treatment response heterogeneity, or individualized treatment rules, based on patient characteristics. In many clinical trials, however, the outcome is measured at multiple time points, which may violate the independence assumption. Therefore, I am proposing a model that extends BART to account for both between and within group variation. I will demonstrate different priors for the between subject variance including inverse-gamma, uniform, and half-t. An application to individualized treatment for diabetes subjects will be presented.