What is Machine Learning? A Primer for the Epidemiologist

TLDR

Machine learning offers epidemiologists powerful tools to analyze complex data and address problems beyond the reach of classical methods. The paper aims to evaluate how to integrate machine learning with traditional epidemiologic methods, addressing language and technical barriers, and to recommend strategies for effective incorporation. The authors provide an overview of machine learning concepts, introduce five common algorithms and four ensemble methods, and review their epidemiologic applications.

Abstract

Abstract Machine learning is a branch of computer science that has the potential to transform epidemiologic sciences. Amid a growing focus on “Big Data,” it offers epidemiologists new tools to tackle problems for which classical methods are not well-suited. In order to critically evaluate the value of integrating machine learning algorithms and existing methods, however, it is essential to address language and technical barriers between the two fields that can make it difficult for epidemiologists to read and assess machine learning studies. Here, we provide an overview of the concepts and terminology used in machine learning literature, which encompasses a diverse set of tools with goals ranging from prediction to classification to clustering. We provide a brief introduction to 5 common machine learning algorithms and 4 ensemble-based approaches. We then summarize epidemiologic applications of machine learning techniques in the published literature. We recommend approaches to incorporate machine learning in epidemiologic research and discuss opportunities and challenges for integrating machine learning and existing epidemiologic research methods.