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

翻译：我们提出了一种新颖的框架，将深度生成时间序列模型与决策理论相结合，用于生成个性化治疗策略。该框架利用历史患者轨迹数据，通过深度生成时间序列模型联合学习生成逼真的个性化治疗轨迹与未来结果轨迹。具体而言，我们的框架能够根据个性化患者病史生成新颖的多变量治疗策略，并基于条件期望效用最大化训练以实现最优预期未来结果。我们通过为住院糖尿病患者生成个性化胰岛素治疗方案和血糖预测来展示该框架，凸显了该方法在生成改进的个性化治疗策略方面的潜力。关键词：深度生成模型、概率决策支持、个性化治疗方案生成、胰岛素与血糖预测