… the prediction model using at least one ML algorithm, (2) were focused on dose
individualization (eg, for the prediction of drug dose or exposure related parameters or assisting …

… Overall, this review emphasises the clinical relevance of Machine Learning methods for
anticancer drug dose optimisation, as many algorithms have shown promising results enabling …

… drug monitoring (TDM) and model-informed precision dosing (… intelligence (AI) and machine
learning (ML) approaches for … However, AI and ML applications for precision dosing have …

… of more machine learning algorithms to support therapeutic drug management and model-
informed precision dosing for other categories of drugs? Will machine learning take over from …

… period for drug initiation[10]. There is a recommended algorithm for making dose adjustments
… the patterns of dofetilide dose adjustment and the role of machine learning to develop …

… dosing. To the best of our knowledge, this study first adopts machine learning techniques
to construct digoxin dosage … only 8 drug types for DDIs; in this study, a total of 26 drugs with …

… to perform drug interaction tests for all possible drug pairs. Our machine learning approach
to … to different drug doses, results using the standard dose were selected. However, if several …

… to evaluate dose–response relationships must be able to account for autocorrelation when
individuals are subjected to multiple doses of the exposure (eg increasing doses of a drug). …

… setting, although it is possible that ML can help achieve accurate drug dose settings. … of ML
for drug dose setting. Our aim was to construct an optimal algorithm for initial dose settings of …

… for a drug resistant strain to … dose, thus minimizing excessive drug, machine learning
techniques were used to develop a framework for analyzing the trade-off between the drug dosage …