Application of Machine Learning in Health Economics and Outcomes Research

3 credits

Introduction to machine learning methods, focusing on understanding applied literature, gaining hands-on experience with basic analysis to address health outcomes, policy, and economics related questions, and communicating results to a general scientific audience. Provides overview of topics related to using machine learning for medical decision-making, including model evaluation, algorithmic bias, model shift, and simulation modeling. Prerequisite: one of the following: (1) BIOST 511; BIOST 512; and BIOST 513 (which may be taken concurrently); (2) permission of instructor; recommended: graduate-level coursework in either statistics, biostatistics, econometrics, or equivalent; familiarity with multivariable statistical methods, including linear and logistic regression; and familiarity with R statistical programming language. Offered: Sp.