Welcome to the final post of a three-part blog series reflecting on the ACoP (American Conference on Pharmacometrics) 13 meeting held October 30th - November 2nd, 2022.
In the previous (second) post of the series we explored machine learning (ML) methodologies, specifically, Shapley values to aid in interpreting ML models. In the first post we discussed the realistic goals, reasonable scope, and perspective on the effort required to integrate artificial intelligence/machine learning (AI/ML) in pharmacometrics. Today, we dive deeper into several specific applications of large-scale ML projects providing additional insights to pharmacometric projects.
Pharmacometrics-specific ML approaches for classically challenging models