Machine Learning Methods for Precision Medicine Research Designed to Reduce Health Disparities A Structured Tutorial

• Published in: Ethnicity & disease Vol. 30; no. Suppl 1; pp. 217 - 228
• Main Authors: Basu, Sanjay, Faghmous, James H., Doupe, Patrick
• Format: Journal Article
• Language: English
• Published: United States Ethnicity & Disease, Inc 2020
• Subjects: Clinical Decision-Making; Health Status Disparities; Humans; Machine Learning; Original Report: Methods and Interventions with Precision Medicine Approaches; Precision Medicine; Health Disparities; Gradient Boosting Machines; Machine Learning; Deep Learning; Precision Medicine; Random Forest
• ISSN: 1049-510X; 1945-0826
• DOI: 10.18865/ed.30.S1.217

Precision medicine research designed to reduce health disparities often involves studying multi-level datasets to understand how diseases manifest disproportionately in one group over another, and how scarce health care resources can be directed precisely to those most at risk for disease. In this article, we provide a structured tutorial for medical and public health researchers on the application of machine learning methods to conduct precision medicine research designed to reduce health disparities. We review key terms and concepts for understanding machine learning papers, including supervised and unsupervised learning, regularization, cross-validation, bagging, and boosting. Metrics are reviewed for evaluating machine learners and major families of learning approaches, including tree-based learning, deep learning, and ensemble learning. We highlight the advantages and disadvantages of different learning approaches, describe strategies for interpreting “black box” models, and demonstrate the application of common methods in an example dataset with open-source statistical code in R. Ethu Dis.