스마트미디어저널 (Smart Media Journal)

  • 제10권2호
  • /
  • Pages.48-54
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  • 2021
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  • 2287-1322(pISSN)
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  • 2288-9671(eISSN)
한국스마트미디어학회 (THE KOREAN INSTITUTE OF SMART MEDIA)
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Application of Deep Recurrent Q Network with Dueling Architecture for Optimal Sepsis Treatment Policy

  • 투고 : 2021.03.04
  • 심사 : 2021.05.25
  • 발행 : 2021.06.30
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초록

Sepsis is one of the leading causes of mortality globally, and it costs billions of dollars annually. However, treating septic patients is currently highly challenging, and more research is needed into a general treatment method for sepsis. Therefore, in this work, we propose a reinforcement learning method for learning the optimal treatment strategies for septic patients. We model the patient physiological time series data as the input for a deep recurrent Q-network that learns reliable treatment policies. We evaluate our model using an off-policy evaluation method, and the experimental results indicate that it outperforms the physicians' policy, reducing patient mortality up to 3.04%. Thus, our model can be used as a tool to reduce patient mortality by supporting clinicians in making dynamic decisions.

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과제정보

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT). (NRF-2020R1A2B5B01002085). This work was supported by the Bio & Medical Technology Development Program of the National Research Foundation (NRF)& funded by the Korean government (MSIT) (NRF-2019M3E5D1A02067961).

참고문헌

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