Diagnosing bias in data-driven algorithms for healthcare

A recent analysis highlighting the potential for algorithms to perpetuate existing racial biases in healthcare underscores the importance of thinking carefully about the labels used during algorithm development.

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Vydané v:Nature medicine Ročník 26; číslo 1; s. 25 - 26
Hlavní autori: Wiens, Jenna, Price, W. Nicholson, Sjoding, Michael W.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York Nature Publishing Group US 01.01.2020
Nature Publishing Group
Predmet:
692/499
706/648
Algorithms
Bias
Biomedical and Life Sciences
Biomedicine
Cancer Research
Costs and Cost Analysis
Data Analysis
Delivery of Health Care
Electronic data processing
Health aspects
Health care
Health Expenditures
Humans
Infectious Diseases
Machine Learning
Medical care
Metabolic Diseases
Molecular Medicine
Neurosciences
News & Views
news-and-views
Prejudices
Social aspects
Technology application
United States
United States
ISSN:1078-8956, 1546-170X, 1546-170X
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Abstract A recent analysis highlighting the potential for algorithms to perpetuate existing racial biases in healthcare underscores the importance of thinking carefully about the labels used during algorithm development.
AbstractList A recent analysis highlighting the potential for algorithms to perpetuate existing racial biases in healthcare underscores the importance of thinking carefully about the labels used during algorithm development.
Audience Academic
Author Price, W. Nicholson
Wiens, Jenna
Sjoding, Michael W.
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  surname: Sjoding
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  organization: Department of Internal Medicine, University of Michigan
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Cites_doi 10.1371/journal.pone.0109264
10.1097/CCM.0000000000002936
10.1515/9780804786737
10.1017/ice.2018.16
10.1126/science.aax2342
10.1164/rccm.201609-1848OC
10.1038/s41591-018-0213-5
10.1038/nature24270
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Biomedicine
Cancer Research
Costs and Cost Analysis
Data Analysis
Delivery of Health Care
Electronic data processing
Health aspects
Health care
Health Expenditures
Humans
Infectious Diseases
Machine Learning
Medical care
Metabolic Diseases
Molecular Medicine
Neurosciences
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Prejudices
Social aspects
Technology application
United States
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