Mitigating Bias in Machine Learning for Medicine
dc.contributor.author
Vokinger, Kerstin N.
dc.contributor.author
Feuerriegel, Stefan
dc.contributor.author
Kesselheim, Aaron S.
dc.date.accessioned
2021-09-01T14:13:21Z
dc.date.available
2021-07-30T20:29:05Z
dc.date.available
2021-08-02T05:30:23Z
dc.date.available
2021-09-01T14:13:21Z
dc.date.issued
2021-08
dc.identifier.issn
2730-664X
dc.identifier.uri
http://hdl.handle.net/20.500.11850/498787
dc.identifier.doi
10.3929/ethz-b-000498787
dc.description.abstract
Several sources of bias can affect the performance of machine learning systems used in medicine and potentially impact clinical care. Here, we discuss solutions to mitigate bias across the different development steps of machine learning-based systems for medical applications.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
Nature Portfolio
en_US
dc.rights.uri
http://creativecommons.org/licenses/by/4.0/
dc.title
Mitigating Bias in Machine Learning for Medicine
en_US
dc.type
Other Journal Item
dc.rights.license
Creative Commons Attribution 4.0 International
dc.date.published
2021-08-23
ethz.journal.title
Communications Medicine
ethz.journal.volume
1
en_US
ethz.journal.issue
1
en_US
ethz.journal.abbreviated
Commun Med
ethz.pages.start
25
en_US
ethz.size
3 p.
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.publication.place
London
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02120 - Dep. Management, Technologie und Ökon. / Dep. of Management, Technology, and Ec.::09623 - Feuerriegel, Stefan (ehemalig) / Feuerriegel, Stefan (former)
en_US
ethz.date.deposited
2021-07-30T20:29:21Z
ethz.source
FORM
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2021-09-01T14:13:31Z
ethz.rosetta.lastUpdated
2022-03-29T11:26:09Z
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true
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