Deep Reinforcement Learning for Sepsis Treatment

@article{Raghu2017DeepRL,
  title={Deep Reinforcement Learning for Sepsis Treatment},
  author={Aniruddh Raghu and Matthieu Komorowski and Imran Ahmed and Leo A. Celi and Peter Szolovits and Marzyeh Ghassemi},
  journal={CoRR},
  year={2017},
  volume={abs/1711.09602}
}
Sepsis is a leading cause of mortality in intensive care units and costs hospitals billions annually. Treating a septic patient is highly challenging, because individual patients respond very differently to medical interventions and there is no universally agreed-upon treatment for sepsis. In this work, we propose an approach to deduce treatment policies for septic patients by using continuous state-space models and deep reinforcement learning. Our model learns clinically interpretable… CONTINUE READING
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A Markov Decision Process to suggest optimal treatment of severe infections in intensive care

M. Komorowski, A. Gordon, L. A. Celi, A. Faisal
In Neural Information Processing Systems Workshop on Machine Learning for Health, • 2016
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