title: Model predictive control with reinforcement learning for drug delivery in renal anemia management.
abstract: Treatment of chronic conditions often creates the challenge of an adequate drug administration. The intra- and inter-individual variability of drug response requires periodic adjustments of the dosing protocols. We describe a method, combining model predictive control for simulation of patient response and reinforcement learning for estimation of dosing strategy, to facilitate the management of anemia due to kidney failure.
authors.affiliation: Department of Medicine, University of Louisville, Louisville, KY 40292, USA. agaweda@kdp.louisville.edu
authors.forename: Adam E
authors.initials: AE
authors.lastname: Gaweda
authors.forename: Mehmet K
authors.initials: MK
authors.lastname: Muezzinoglu
authors.forename: Alfred A
authors.initials: AA
authors.lastname: Jacobs
authors.forename: George R
authors.initials: GR
authors.lastname: Aronoff
authors.forename: Michael E
authors.initials: ME
authors.lastname: Brier
mesh_terms: D000465:Algorithms; D000740:Anemia / Q000628:therapy; D001698:Biomedical Engineering / Q000295:instrumentation / Q000379:methods; D003198:Computer Simulation; D003201:Computers; D004305:Dose-Response Relationship, Drug; D016503:Drug Delivery Systems; D006454:Hemoglobins / Q000378:metabolism; D006801:Humans; D015233:Models, Statistical; D008962:Models, Theoretical; D006435:Renal Dialysis; D051437:Renal Insufficiency / Q000175:diagnosis* / Q000628:therapy; D012984:Software; D013997:Time Factors
chemical_list: D006454:Hemoglobins