@inproceedings{
sch{\"u}rch2023generating,
title={Generating Personalized Insulin Treatments Strategies with Conditional Generative Time Series Models},
author={Manuel Sch{\"u}rch and Xiang Li and Ahmed Allam and Giulia Hofer and Amina Mollaysa and Claudia Cavelti-Weder and Michael Krauthammer},
booktitle={Deep Generative Models for Health Workshop NeurIPS 2023},
year={2023},
url={https://openreview.net/forum?id=SXw8DBKoRg}
}
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.
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