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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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-02-01
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| Series: | Frontiers in Pharmacology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fphar.2022.1094281/full |
| _version_ | 1797904245326872576 |
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| author | Benjamin Ribba |
| author_facet | Benjamin Ribba |
| author_sort | Benjamin Ribba |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-04-10T09:45:46Z |
| format | Article |
| id | doaj.art-d51ae0b986a54cf5a847346af0269613 |
| institution | Directory Open Access Journal |
| issn | 1663-9812 |
| language | English |
| last_indexed | 2024-04-10T09:45:46Z |
| publishDate | 2023-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Pharmacology |
| spelling | doaj.art-d51ae0b986a54cf5a847346af02696132023-02-17T05:29:37ZengFrontiers Media S.A.Frontiers in Pharmacology1663-98122023-02-011310.3389/fphar.2022.10942811094281Reinforcement learning as an innovative model-based approach: Examples from precision dosing, digital health and computational psychiatryBenjamin RibbaModel-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.https://www.frontiersin.org/articles/10.3389/fphar.2022.1094281/fullpharmacometricsdigital healthreinforcement learningprecision dosingcomputational psychiatry |
| spellingShingle | Benjamin Ribba Reinforcement learning as an innovative model-based approach: Examples from precision dosing, digital health and computational psychiatry Frontiers in Pharmacology pharmacometrics digital health reinforcement learning precision dosing 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 | pharmacometrics digital health reinforcement learning precision dosing computational psychiatry |
| url | https://www.frontiersin.org/articles/10.3389/fphar.2022.1094281/full |
| work_keys_str_mv | AT benjaminribba reinforcementlearningasaninnovativemodelbasedapproachexamplesfromprecisiondosingdigitalhealthandcomputationalpsychiatry |