Teaching yourself about structural racism will improve your machine learning-Reference-Cited by-同舟云学术

Teaching yourself about structural racism will improve your machine learning

Published:2019-11-19 Issue:2 Volume:21 Page:339-344
ISSN:1465-4644
Container-title:Biostatistics
language:en
Short-container-title:

Author:

Robinson Whitney R¹,Renson Audrey²,Naimi Ashley I³

Affiliation:

1. Department of Epidemiology, UNC Gillings School of Global Public Health, University of North Carolina at Chapel Hill; Carolina Population Center, University of North Carolina at Chapel Hill, CB #7345 McGavran-Greenberg, Chapel Hill, NC 27599-7435, USA

2. Department of Epidemiology, University of North Carolina at Chapel Hill, 135 Dauer Dr., Chapel Hill, NC 27599, USA

3. Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, 130 DeSoto Street, 5131 Public Health Building, Pittsburgh, PA 15261-3100, USA

Abstract

Summary In this commentary, we put forth the following argument: Anyone conducting machine learning in a health-related domain should educate themselves about structural racism. We argue that structural racism is a critical body of knowledge needed for generalizability in almost all domains of health research.

Funder

National Institute of Minority Health and Health Disparities

NIMHD

National Institutes of Health

NIH

Eunice Kennedy Shriver National Institute of Child Health & Human Development

NICHD

Publisher

Oxford University Press (OUP)

Subject

Statistics, Probability and Uncertainty,General Medicine,Statistics and Probability

Link

http://academic.oup.com/biostatistics/article-pdf/21/2/339/36209046/kxz040.pdf

Reference16 articles.

1. Structural racism and health inequities in the USA: evidence and interventions;Bailey,;The Lancet,2017

2. FDA analysis of racial demographics in multiple myeloma trials;Bhatnagar,;Blood,2017

3. Race, ethnicity and lung function: a brief history;Braun;Canadian Journal of Respiratory Therapy: CJRT,2015

4. A few useful things to know about machine learning;Domingos,;Communications of the ACM,2012

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