Generating Personalized Insulin Treatments Strategies with Conditional Generative Time Series Models

Manuel Schürch, Xiang Li, Ahmed Allam, Giulia Hofer, Amina Mollaysa, Claudia Cavelti-Weder, Michael Krauthammer

Keywords: deep generative model, probabilistic decision support, personalized treatment generation, insulin and blood glucose prediction

TL;DR: We propose a novel framework that combines deep generative time series models with decision theory for generating personalized treatment strategies.

Abstract: 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.

Submission Number: 6