Sepsis is the leading cause of mortality in the ICU. It is challenging to manage because individual patients respond differently to treatment. Thus, tailoring treatment to the individual patient is essential for the best outcomes. In this paper, we take steps toward this goal by applying a mixture-of-experts framework to personalize sepsis treatment. The mixture model selectively alternates between neighbor-based (kernel) and deep reinforcement learning (DRL) experts depending on patient's current history. On a large retrospective cohort, this mixture-based approach outperforms physician, kernel only, and DRL-only experts.
@article{arxiv.1901.04670,
title = {Improving Sepsis Treatment Strategies by Combining Deep and Kernel-Based Reinforcement Learning},
author = {Xuefeng Peng and Yi Ding and David Wihl and Omer Gottesman and Matthieu Komorowski and Li-wei H. Lehman and Andrew Ross and Aldo Faisal and Finale Doshi-Velez},
journal= {arXiv preprint arXiv:1901.04670},
year = {2019}
}