Informing sequential clinical decision-making through reinforcement learning: an empirical study

@article{Shortreed2010InformingSC,
  title={Informing sequential clinical decision-making through reinforcement learning: an empirical study},
  author={Susan M. Shortreed and Eric B. Laber and Daniel J. Lizotte and T. Scott Stroup and Joelle Pineau and Susan A. Murphy},
  journal={Machine Learning},
  year={2010},
  volume={84},
  pages={109-136}
}
This paper highlights the role that reinforcement learning can play in the optimization of treatment policies for chronic illnesses. Before applying any off-the-shelf reinforcement learning methods in this setting, we must first tackle a number of challenges. We outline some of these challenges and present methods for overcoming them. First, we describe a multiple imputation approach to overcome the problem of missing data. Second, we discuss the use of function approximation in the context of… CONTINUE READING

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Multiple imputation after 18+ years (with discussion)

  • D. B. Rubin
  • Journal of the American Statistical Association…
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Highly Influential
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