What is Machine Learning? A Primer for the Epidemiologist

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...

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Veröffentlicht in:American journal of epidemiology Jg. 188; H. 12; S. 2222
Hauptverfasser: Bi, Qifang, Goodman, Katherine E, Kaminsky, Joshua, Lessler, Justin
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States 31.12.2019
Schlagworte:
Epidemiologic Studies
Epidemiology
Machine Learning
Terminology as Topic
ensemble models
Big Data
machine learning
ISSN:1476-6256, 1476-6256
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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
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ObjectType-Feature-2
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ISSN:1476-6256
1476-6256
DOI:10.1093/aje/kwz189