Pablo Escandell-Montero1, Milena Chermisi2, José M Martínez-Martínez3, Juan Gómez-Sanchis3, Carlo Barbieri2, Emilio Soria-Olivas3, Flavio Mari2, Joan Vila-Francés3, Andrea Stopper2, Emanuele Gatti4, José D Martín-Guerrero3. 1. Intelligent Data Analysis Laboratory, University of Valencia, Av. de la Universidad, s/n, 46100 Burjassot (Valencia), Spain. Electronic address: pablo.escandell@uv.es. 2. Healthcare and Business Advanced Modeling, Fresenius Medical Care, Else-Kröner-Strasse 1, 61352 Bad Homburg, Germany. 3. Intelligent Data Analysis Laboratory, University of Valencia, Av. de la Universidad, s/n, 46100 Burjassot (Valencia), Spain. 4. Healthcare and Business Advanced Modeling, Fresenius Medical Care, Else-Kröner-Strasse 1, 61352 Bad Homburg, Germany; Centre for Biomedical Technology at Danube, University of Krems, Dr.-Karl-Dorrek-Strasse 30, 3500 Krems, Austria.
Abstract
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. When comparing FQI and the protocol, FQI achieves an increment of 27.6% in the proportion of patients that are within the targeted range of hemoglobin during the period of treatment. In addition, the quantity of drug needed is reduced by 5.13%, which indicates a more efficient use of ESAs. CONCLUSION: Although prospective validation is required, promising results demonstrate the potential of RL to become an alternative to current protocols.
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. When comparing FQI and the protocol, FQI achieves an increment of 27.6% in the proportion of patients that are within the targeted range of hemoglobin during the period of treatment. In addition, the quantity of drug needed is reduced by 5.13%, which indicates a more efficient use of ESAs. CONCLUSION: Although prospective validation is required, promising results demonstrate the potential of RL to become an alternative to current protocols.
Authors: Miguel Hueso; Alfredo Vellido; Nuria Montero; Carlo Barbieri; Rosa Ramos; Manuel Angoso; Josep Maria Cruzado; Anders Jonsson Journal: Kidney Dis (Basel) Date: 2018-01-25
Authors: Alexandru Burlacu; Adrian Iftene; Daniel Jugrin; Iolanda Valentina Popa; Paula Madalina Lupu; Cristiana Vlad; Adrian Covic Journal: Biomed Res Int Date: 2020-06-10 Impact factor: 3.411