Abstract
This paper deals with the validation of a Reinforcement Learning (RL) policy for dosage optimization of Erythropoietin (EPO). This policy was obtained using data from patients in a haemodialysis program during the year 2005. The goal of this policy was to maintain patients’ Haemoglobin (Hb) level between 11.5 g/dl and 12.5 g/dl. An individual management was needed, as each patient usually presents a different response to the treatment. RL provides an attractive and satisfactory solution, showing that a policy based on RL would be much more successful in achieving the goal of maintaining patients within the desired target of Hb than the policy followed by the hospital so far. In this work, this policy is validated using a cohort of patients treated during 2006. Results show the robustness of the policy that is also successful with this new data set.
This work has been partially supported by the research projects ARVIV/2007/094 and TIN2007-61006.
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Martín-Guerrero, J.D., Soria-Olivas, E., Martínez-Sober, M., Climente-Martí, M., De Diego-Santos, T., Jiménez-Torres, N.V. (2007). Validation of a Reinforcement Learning Policy for Dosage Optimization of Erythropoietin. In: Orgun, M.A., Thornton, J. (eds) AI 2007: Advances in Artificial Intelligence. AI 2007. Lecture Notes in Computer Science(), vol 4830. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76928-6_84
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DOI: https://doi.org/10.1007/978-3-540-76928-6_84
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