ArticleFrontiers in pharmacology2022
Reinforcement learning as an innovative model-based approach: Examples from precision dosing, digital health and computational psychiatry.
Article in Frontiers in pharmacology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Algorithm-guided insulin therapy in hospitalized patients: current evidence, implementation challenges, and future perspectives.Reviews in endocrine & metabolic disorders · 2026Review
- Two Worlds, One Battle: How Bacteria and Malignancies Converge on Drug Resistance.International journal of molecular sciences · 2026Review
- Rising Role of Artificial Intelligence in Clinical Pharmacometrics and Model-Informed Precision Dosing in Pediatrics.The journal of pediatric pharmacology and therapeutics : JPPT : the official journal of PPAG · 2026Article
- The impact of deep learning and omics data in transforming precision therapy for brain cancer.Frontiers in bioinformatics · 2026Review
- Artificial intelligence applications in oxaliplatin-based chemotherapy for colon cancer: advancing prognosis, toxicity prediction, and dose personalization.Frontiers in pharmacology · 2026Review
- Precision Dosing in Presence of Multiobjective Therapies by Integrating Reinforcement Learning and PK-PD Models: Application to Givinostat Treatment of Polycythemia Vera.CPT: pharmacometrics & systems pharmacology · 2025Article
- Recent Advances in Therapeutics for the Treatment of Alzheimer's Disease.Molecules (Basel, Switzerland) · 2024Review
- Machine learning in oncological pharmacogenomics: advancing personalized chemotherapy.Functional & integrative genomics · 2024Review
- Quantitative systems pharmacology in the age of artificial intelligence.CPT: pharmacometrics & systems pharmacology · 2023Article
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Abstract
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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