Machine Learning in Epidemiology and Health Outcomes Research

Machine learning approaches to modeling of epidemiologic data are becoming increasingly more prevalent in the literature. These methods have the potential to improve our understanding of health and opportunities for intervention, far beyond our past capabilities. This article provides a walkthrough...

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Veröffentlicht in:Annual review of public health Jg. 41; S. 21
Hauptverfasser: Wiemken, Timothy L, Kelley, Robert R
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States 02.04.2020
Schlagworte:
Epidemiologic Methods
Epidemiologic Research Design
Humans
Machine Learning
Outcome Assessment, Health Care - methods
biostatistics
deep learning
treatment effects
walkthrough
predictive modeling
artificial intelligence
ISSN:1545-2093, 1545-2093
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Zusammenfassung:Machine learning approaches to modeling of epidemiologic data are becoming increasingly more prevalent in the literature. These methods have the potential to improve our understanding of health and opportunities for intervention, far beyond our past capabilities. This article provides a walkthrough for creating supervised machine learning models with current examples from the literature. From identifying an appropriate sample and selecting features through training, testing, and assessing performance, the end-to-end approach to machine learning can be a daunting task. We take the reader through each step in the process and discuss novel concepts in the area of machine learning, including identifying treatment effects and explaining the output from machine learning models.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:1545-2093
1545-2093
DOI:10.1146/annurev-publhealth-040119-094437