Format:
1 Online-Ressource (x, 138 Seiten, 24662 KB)
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Illustrationen, Diagramme
Content:
While patients are known to respond differently to drug therapies, current clinical practice often still follows a standardized dosage regimen for all patients. For drugs with a narrow range of both effective and safe concentrations, this approach may lead to a high incidence of adverse events or subtherapeutic dosing in the presence of high patient variability. Model-informedprecision dosing (MIPD) is a quantitative approach towards dose individualization based on mathematical modeling of dose-response relationships integrating therapeutic drug/biomarker monitoring (TDM) data. MIPD may considerably improve the efficacy and safety of many drug therapies. Current MIPD approaches, however, rely either on pre-calculated dosing tables or on simple point predictions of the therapy outcome. These approaches lack a quantification of uncertainties and the ability to account for effects that are delayed. In addition, the underlying models are not improved while applied to patient data. Therefore, current approaches are not well suited for ...
Note:
Dissertation Universität Potsdam 2021
Additional Edition:
Erscheint auch als Druck-Ausgabe Maier, Corinna Sabrina Bayesian data assimilation and reinforcement learning for model-informed precision dosing in oncology Potsdam, 2021
Language:
English
Keywords:
Arzneimitteldosis
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Datenassimilation
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Operante Konditionierung
;
Hochschulschrift
DOI:
10.25932/publishup-51587
URN:
urn:nbn:de:kobv:517-opus4-515870
URL:
https://doi.org/10.25932/publishup-51587
URL:
https://nbn-resolving.org/urn:nbn:de:kobv:517-opus4-515870
URL:
https://d-nb.info/1240760434/34
Author information:
Röblitz, Susanna 1979-
