Machine Learning in Health Care: Fairness, Issues, and Challenges

Authors: Margrét Bjarnadóttir, University of Maryland, David Anderson (Villanova University)

This tutorial discusses considerations relevant to operations researchers undertaking machine learning projects in the healthcare domain. We introduce readers to the unique considerations of healthcare data, from data cleaning and preparation to feature generation and dimension reduction. We then focus on the modeling techniques that tend to perform well in the healthcare context, and we highlight common stumbling blocks. We close with a discussion of fairness and transparency in healthcare modeling. This tutorial assumes that readers are familiar with basic machine learning techniques and terminologies but do not have a background in the health care field.