  Objective: Anemia is a frequent comorbidity in hemodialysis patients that can
be successfully treated by administering erythropoiesis-stimulating agents
(ESAs). ESAs dosing is currently based on clinical protocols that often do not
account for the high inter- and intra-individual variability in the patient's
response. As a result, the hemoglobin level of some patients oscillates around
the target range, which is associated with multiple risks and side-effects.
This work proposes a methodology based on reinforcement learning (RL) to
optimize ESA therapy.
  Methods: RL is a data-driven approach for solving sequential decision-making
problems that are formulated as Markov decision processes (MDPs). Computing
optimal drug administration strategies for chronic diseases is a sequential
decision-making problem in which the goal is to find the best sequence of drug
doses. MDPs are particularly suitable for modeling these problems due to their
ability to capture the uncertainty associated with the outcome of the treatment
and the stochastic nature of the underlying process. The RL algorithm employed
in the proposed methodology is fitted Q iteration, which stands out for its
ability to make an efficient use of data.
  Results: The experiments reported here are based on a computational model
that describes the effect of ESAs on the hemoglobin level. The performance of
the proposed method is evaluated and compared with the well-known Q-learning
algorithm and with a standard protocol. Simulation results show that the
performance of Q-learning is substantially lower than FQI and the protocol.
  Conclusion: Although prospective validation is required, promising results
demonstrate the potential of RL to become an alternative to current protocols.
