Leveraging machine learning in limited sampling strategies for efficient estimation of the area under the curve in pharmacokinetic analysis: a review.
Journal:
European journal of clinical pharmacology
PMID:
39570408
Abstract
OBJECTIVE: Limited sampling strategies are widely employed in clinical practice to minimize the number of blood samples required for the accurate area under the curve calculations, as obtaining these samples can be costly and challenging. Traditionally, the maximum a posteriori Bayesian estimation has been the standard method for the area under the curve estimation based on limited samples. However, machine learning is emerging as a promising alternative for this purpose. Here, we review studies that utilize machine learning approaches to develop limited sampling strategies and compare the strengths and weaknesses of these machine learning methods.