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We propose a novel framework that combines deep generative time series modelswith decision theory for generating personalized treatment strategies. Itleverages historical patient trajectory data to jointly learn the generation ofrealistic personalized treatment and future outcome trajectories through deepgenerative time series models. In particular, our framework enables thegeneration of novel multivariate treatment strategies tailored to thepersonalized patient history and trained for optimal expected future outcomesbased on conditional expected utility maximization. We demonstrate ourframework by generating personalized insulin treatment strategies and bloodglucose predictions for hospitalized diabetes patients, showcasing thepotential of our approach for generating improved personalized treatmentstrategies. Keywords: deep generative model, probabilistic decision support,personalized treatment generation, insulin and blood glucose prediction

Generating Personalized Insulin Treatments Strategies with Deep Conditional Generative Time Series Models