Objectives

• Understand the fundamental concepts of machine learning and their applications in drug development use cases.
• Understand and apply machine learning techniques for covariate modeling and prognostic factor identification.
• Understand and use explainable machine learning techniques to address drug development questions.
• Combine scientific knowledge and neural networks to build scientific machine learning (SciML) pharmacodynamic models.
• Understand the conditional variational autoencoder generative machine learning model and its relationship to nonlinear mixed-effects models.
• Compare pure machine learning, traditional scientific modeling, and hybrid SciML approaches when analyzing longitudinal clinical trial data, with and without random effects.
• Build and use DeepNLME models to analyze disease progression and biomarker data across multiple realistic case studies.