Population pharmacokinetic model selection assisted by machine learning.

ABSTRACT: A fit-for-purpose structural and statistical model is the first major requirement in population pharmacometric model development. In this manuscript we discuss how this complex and computationally intensive task could benefit from supervised machine learning algorithms. We compared the classical pharmacometric approach with two machine learning methods, genetic algorithm and neural networks, in different scenarios based on simulated pharmacokinetic data. Genetic algorithm performance was assessed using a fitness function based on log-likelihood, whilst neural networks were trained using mean square error or binary cross-entropy loss. Machine learning provided a selection based only on statistical rules and achieved accurate selection. The minimization process of genetic algorithm was succe

SUBMITTER: Sibieude E 

PROVIDER: S-EPMC8940812 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature