Individualization of pharmacological anemia management using reinforcement learning

By Adam E. Gaweda, Mehmet K. Muezzinoglu, George R. Aronoff, Alfred A. Jacobs, J. Zurada, Michael E. Brier. 2005

70 citations recorded by the source corpus.

Subfields: Cardiology and Cardiovascular Medicine, Control and Systems Engineering, Molecular Biology

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Citations in the corpus

Reinforcement Learning in Healthcare: A Survey (2019, methodology): “Q-learning, such as Q-learning with function approximation, or directly based on state-aggregation [135], [136], [137], in providing effective treatment regimes in anemia.”
Adaptive treatment of anemia on hemodialysis patients: A reinforcement learning approach (2011, background): “The clinicians often have to adjust a treatment in a trial and error fashion until a desired response is reached [ 5 ].”
Incorporating prior knowledge into Q-learning for drug delivery individualization (2005, methodology): “The work presented in this paper is based on the findings presented in [ 2 ].”
Using clinical information in goal-oriented learning. (2007, methodology): “Preparation of the Data We evaluated the proposed method using an experimental setup similar to the ones used in [13], [ 14 ], and [15].”
Optimization of anemia treatment in hemodialysis patients via reinforcement learning (2014, methodology): “Q-learning is probably the most popular RL a lgorithm, it has been used in many applications, including th e treatment of anemia in hemodialysis patients [29, 30].”
A reinforcement learning approach for individualizing erythropoietin dosages in hemodialysis patients (2009, background): “count the average response of populations of patients (Gaweda et al., 2005).”
Improving management of Anemia in End Stage Renal Disease using Reinforcement Learning (2009, methodology): “In [ 5 ], we demonstrated the use of an RL technique called”
Model Predictive Control with Reinforcement Learning for Drug Delivery in Renal Anemia Management (2006, unknown): “To emulate this process numerically, we simulated the anemia management using Reinforcement Learning methods, such as SARSA [6] and Q-learning [7].”
An Introduction to Machine Learning (2020, methodology): “to personalize pharmacological anemia management.(68) A similar approach was used to develop a “closed loop” system for glucose control by combining a mathematical model, a glucose sensor, and a reinforcement learning model.”
Optimizing drug therapy with Reinforcement Learning: The case of Anemia Management (2011, methodology): “We and others have also investigated addressing this challenge using techniques rooted in the theory of Computational Intelligence and Machine Learning [ 4 , 5]. In this work we explore Reinforcement Learning (RL) methods for the drug dosing”