Event

Healthcare data are often available to payers and healthcare systems in real time, but are massive, high dimensional, and complex. Artificial intelligence and machine learning merge statistics, computer science, and information theory and offer powerful computational tools to enhance the extraction of useful information from complex healthcare data and prediction accuracy. This course gives an overview of basic machine learning concepts and introduces a few commonly used machine learning techniques and their practical applications in healthcare and pharmaceutical outcomes research. Participants will be introduced to foundational principles and concepts of statistical machine learning, then be provided with several specific machine learning techniques and their applications in health and pharmaceutical outcomes research. The course faculty will use R or Radiant to demonstrate several machine learning methods such as penalized regression and tree-based methods, as well as techniques for dimension reduction/feature selection. Participants will have hands-on practical experiences with machine learning and gain experience interpreting and evaluating the results and prediction performance that comes from machine learning modeling. Distinguishing prediction modeling from causal inference research in pharmacoepidemiology will be also presented and discussed. This is an entry-level course but is designed for those with some familiarity with traditional statistical modeling techniques (eg, linear regression, logistic regression).

PREREQUISITES: To get the most out of the course, students should have a basic statistical background. Participants who wish to gain hands-on experience are required to bring their laptops with Radiant (https://radiant-rstats.github.io/docs/install.html) installed.