What is machine learning? A primer for the epidemiologist

Qifang Bi, Katherine E. Goodman, Joshua Kaminsky, Justin Lessler

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Pages (from-to)2222-2239
Number of pages18
JournalAmerican journal of epidemiology
Volume188
Issue number12
DOIs
StatePublished - Dec 31 2019

Keywords

  • Big Data
  • ensemble models
  • machine learning

ASJC Scopus subject areas

  • Epidemiology

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