  We propose a novel framework that combines deep generative time series models
with decision theory for generating personalized treatment strategies. It
leverages historical patient trajectory data to jointly learn the generation of
realistic personalized treatment and future outcome trajectories through deep
generative time series models. In particular, our framework enables the
generation of novel multivariate treatment strategies tailored to the
personalized patient history and trained for optimal expected future outcomes
based on conditional expected utility maximization. We demonstrate our
framework by generating personalized insulin treatment strategies and blood
glucose predictions for hospitalized diabetes patients, showcasing the
potential of our approach for generating improved personalized treatment
strategies. Keywords: deep generative model, probabilistic decision support,
personalized treatment generation, insulin and blood glucose prediction
