Improving Sepsis Treatment Strategies by Combining Deep and Kernel-Based Reinforcement Learning.

Journal: AMIA ... Annual Symposium proceedings. AMIA Symposium
PMID: 30815131

Abstract

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.

Authors

  • Xuefeng Peng
    Harvard University, Paulson School of Engineering and Applied Sciences, Cambridge, MA.
  • Yi Ding
    Clinical and Research Center of AIDS, Beijing Ditan Hospital, Capital Medical University, Beijing, China.
  • David Wihl
    Harvard University, Paulson School of Engineering and Applied Sciences, Cambridge, MA.
  • Omer Gottesman
    Harvard University, Paulson School of Engineering and Applied Sciences, Cambridge, MA.
  • Matthieu Komorowski
    Imperial College London, London, UK.
  • Li-Wei H Lehman
  • Andrew Ross
    Harvard University, Paulson School of Engineering and Applied Sciences, Cambridge, MA.
  • Aldo Faisal
    Imperial College London, London, UK.
  • Finale Doshi-Velez
    School of Engineering and Applied Sciences, Harvard University, Cambridge, MA.

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