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.
%0 Generic
%1 Wiemken2019
%A Wiemken, Timothy L.
%A Kelley, Robert R.
%D 2019
%I Annual Reviews Inc.
%J Annual Review of Public Health
%K artificialintelligence biostatistics deeplearning predictivemodeling treatmenteffects walkthrough
%P 21-36
%R 10.1146/annurev-publhealth-040119-094437
%T Machine learning in epidemiology and health outcomes research
%U https://pubmed.ncbi.nlm.nih.gov/31577910/
%V 41
%X 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.
@generic{Wiemken2019,
abstract = {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.},
added-at = {2023-02-03T11:44:35.000+0100},
author = {Wiemken, Timothy L. and Kelley, Robert R.},
biburl = {https://www.bibsonomy.org/bibtex/2850719daa3ffb701fb29e4d9b0bcfcc0/jepcastel},
doi = {10.1146/annurev-publhealth-040119-094437},
interhash = {0df2d728f8f97cfaacfb84d87e847d15},
intrahash = {850719daa3ffb701fb29e4d9b0bcfcc0},
issn = {15452093},
journal = {Annual Review of Public Health},
keywords = {artificialintelligence biostatistics deeplearning predictivemodeling treatmenteffects walkthrough},
month = {4},
note = {Models predictius; Machine learning; Introductori},
pages = {21-36},
pmid = {31577910},
publisher = {Annual Reviews Inc.},
timestamp = {2023-02-03T11:44:35.000+0100},
title = {Machine learning in epidemiology and health outcomes research},
url = {https://pubmed.ncbi.nlm.nih.gov/31577910/},
volume = 41,
year = 2019
}