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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 

Welcome to the second 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 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. If you missed the first post of this series or need a refresher, check it out here.

This is a three-part blog series reflecting on the ACoP (American Conference on Pharmacometrics) 13 meeting held October 30th - November 2nd, 2022.

Several weeks ago I finally had the opportunity to attend my first ACoP in person after the pandemic, and I was especially excited for the talks and discussions around artificial intelligence/machine learning (AI/ML) in pharmacometrics. After reflecting on the conference I noticed some themes, which tie in nicely with the internal MetrumRG discussions, projects, and viewpoint on AI/ML.