Disparate Censorship & Undertesting: A Source of Label Bias in Clinical Machine Learning.
Journal
Proceedings of machine learning research
ISSN: 2640-3498
Titre abrégé: Proc Mach Learn Res
Pays: United States
ID NLM: 101735789
Informations de publication
Date de publication:
Aug 2022
Aug 2022
Historique:
medline:
8
5
2023
pubmed:
8
5
2023
entrez:
8
5
2023
Statut:
ppublish
Résumé
As machine learning (ML) models gain traction in clinical applications, understanding the impact of clinician and societal biases on ML models is increasingly important. While biases can arise in the labels used for model training, the many sources from which these biases arise are not yet well-studied. In this paper, we highlight
Types de publication
Journal Article
Langues
eng
Pagination
343-390Subventions
Organisme : NHLBI NIH HHS
ID : R01 HL158626
Pays : United States
Organisme : AHRQ HHS
ID : R01 HS027431
Pays : United States
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