Maximizing Personalized Prediction: A Comparison of Individualized Models, Multitask Learning, and Mixed Effects Methods

With the growing importance of precision medicine, researchers must effectively account for individual heterogeneity in clinical predictions. Multiple paradigms for doing so have been advanced, including multitask learning and mixed effects methods, but comparisons between these approaches are rare. In this talk, we discuss benefits and drawbacks of available approaches.