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 300-level STAT course; recommended: advanced coursework in either statistics, biostatistics, or econometrics; and familiarity with multivariable statistical methods, including linear and logistic regression. Offered: jointly with HEOR 452; Sp.