Individualization of pharmacological anemia management using reinforcement learning.
Effective management of anemia due to renal failure poses many challenges to physicians. Individual response to treatment varies across patient populations and, due to the prolonged character of the therapy, changes over time. In this work, a Reinforcement Learning-based approach is proposed as an alternative method for individualization of drug administration in the treatment of renal anemia. Q-learning, an off-policy approximate dynamic programming method, is applied to determine the proper dosing strategy in real time. Simulations compare the proposed methodology with the currently used dosing protocol. Presented results illustrate the ability of the proposed method to achieve the therapeutic goal for individuals with different response characteristics and its potential to become an alternative to currently used techniques.
Gaweda
Adam E
AE
Department of Medicine, University of Louisville, Louisville, KY 40292, USA. adam.gaweda@kdp.louisville.edu
Muezzinoglu
Mehmet K
MK
Aronoff
George R
GR
Jacobs
Alfred A
AA
Zurada
Jacek M
JM
Brier
Michael E
ME
D000465:Algorithms; D000740:Anemia / Q000188:drug therapy*; D001185:Artificial Intelligence; D002965:Classification; D004921:Erythropoietin / Q000008:administration & dosage* / Q000627:therapeutic use*; D006454:Hemoglobins / Q000378:metabolism; D006801:Humans; D008390:Markov Chains; D008959:Models, Neurological; D016571:Neural Networks, Computer; D011994:Recombinant Proteins; D012054:Reinforcement, Psychology
D006454:Hemoglobins; D011994:Recombinant Proteins; D004921:Erythropoietin