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Reinforcement learning as an innovative model-based approach: Examples from precision dosing, digital health and computational psychiatry

Model-based approaches are instrumental for successful drug development and use. Anchored within pharmacological principles, through mathematical modeling they contribute to the quantification of drug response variability and enables precision dosing. Reinforcement learning (RL)—a set of computation...

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Detalles Bibliográficos
Autor principal: Ribba, Benjamin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9981647/
https://www.ncbi.nlm.nih.gov/pubmed/36873047
http://dx.doi.org/10.3389/fphar.2022.1094281
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author Ribba, Benjamin
author_facet Ribba, Benjamin
author_sort Ribba, Benjamin
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description Model-based approaches are instrumental for successful drug development and use. Anchored within pharmacological principles, through mathematical modeling they contribute to the quantification of drug response variability and enables precision dosing. Reinforcement learning (RL)—a set of computational methods addressing optimization problems as a continuous learning process—shows relevance for precision dosing with high flexibility for dosing rule adaptation and for coping with high dimensional efficacy and/or safety markers, constituting a relevant approach to take advantage of data from digital health technologies. RL can also support contributions to the successful development of digital health applications, recognized as key players of the future healthcare systems, in particular for reducing the burden of non-communicable diseases to society. RL is also pivotal in computational psychiatry—a way to characterize mental dysfunctions in terms of aberrant brain computations—and represents an innovative modeling approach forpsychiatric indications such as depression or substance abuse disorders for which digital therapeutics are foreseen as promising modalities.
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spelling pubmed-99816472023-03-04 Reinforcement learning as an innovative model-based approach: Examples from precision dosing, digital health and computational psychiatry Ribba, Benjamin Front Pharmacol Pharmacology Model-based approaches are instrumental for successful drug development and use. Anchored within pharmacological principles, through mathematical modeling they contribute to the quantification of drug response variability and enables precision dosing. Reinforcement learning (RL)—a set of computational methods addressing optimization problems as a continuous learning process—shows relevance for precision dosing with high flexibility for dosing rule adaptation and for coping with high dimensional efficacy and/or safety markers, constituting a relevant approach to take advantage of data from digital health technologies. RL can also support contributions to the successful development of digital health applications, recognized as key players of the future healthcare systems, in particular for reducing the burden of non-communicable diseases to society. RL is also pivotal in computational psychiatry—a way to characterize mental dysfunctions in terms of aberrant brain computations—and represents an innovative modeling approach forpsychiatric indications such as depression or substance abuse disorders for which digital therapeutics are foreseen as promising modalities. Frontiers Media S.A. 2023-02-17 /pmc/articles/PMC9981647/ /pubmed/36873047 http://dx.doi.org/10.3389/fphar.2022.1094281 Text en Copyright © 2023 Ribba. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Pharmacology
Ribba, Benjamin
Reinforcement learning as an innovative model-based approach: Examples from precision dosing, digital health and computational psychiatry
title Reinforcement learning as an innovative model-based approach: Examples from precision dosing, digital health and computational psychiatry
title_full Reinforcement learning as an innovative model-based approach: Examples from precision dosing, digital health and computational psychiatry
title_fullStr Reinforcement learning as an innovative model-based approach: Examples from precision dosing, digital health and computational psychiatry
title_full_unstemmed Reinforcement learning as an innovative model-based approach: Examples from precision dosing, digital health and computational psychiatry
title_short Reinforcement learning as an innovative model-based approach: Examples from precision dosing, digital health and computational psychiatry
title_sort reinforcement learning as an innovative model-based approach: examples from precision dosing, digital health and computational psychiatry
topic Pharmacology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9981647/
https://www.ncbi.nlm.nih.gov/pubmed/36873047
http://dx.doi.org/10.3389/fphar.2022.1094281
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