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Reinforcement learning and Bayesian data assimilation for model-informed precision dosing in oncology
[article]
2020
arXiv
pre-print
Model-informed precision dosing (MIPD) using therapeutic drug/biomarker monitoring offers the opportunity to significantly improve the efficacy and safety of drug therapies. Current strategies comprise model-informed dosing tables or are based on maximum a-posteriori estimates. These approaches, however, lack a quantification of uncertainty and/or consider only part of the available patient-specific information. We propose three novel approaches for MIPD employing Bayesian data assimilation
arXiv:2006.01061v1
fatcat:xfknpe4hg5cvbnulg2verho3aa