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Cited by 9Domain Adaptation and Few-Shot LearningMachine Learning in HealthcareSepsis Diagnosis and Treatment

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Unifying Domain Adaptation and Domain Generalization for Robust Prediction Across Minority Racial Groups

Khoshnevisan, Farzaneh, Chi, Min·2021·Lecture Notes in Computer Science
DOI 10.1007/978-3-030-86486-6_32
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Crossref · 10.1007/978-3-030-86486-6_32

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  • Addressing the robustness of the model to specific minority groups directly relates to the notion of fairness.

    “addressing the robustness of the model to specific minority groups directly relates to the notion of fairness23–25.”

    From A scoping review of robustness concepts for machine learning in healthcare (2025) · cites this paper as [24]

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Metadata

Authors
Khoshnevisan, F., & Chi, M.
Affiliations
North Carolina State University (US)
Corresponding author
Khoshnevisan, Farzaneh
ORCID
Chi, Min
Venue
Lecture Notes in Computer Science
Publisher
Springer International Publishing
Published
2021
Pages
521-537
ISSN
  • 0302-9743 (ISSN-L)
  • 1611-3349
DOI
10.1007/978-3-030-86486-6_32
OpenAlex
W3200523656
Cited by
9
References
2
Citations by year
2025: 3 · 2024: 2 · 2023: 3 · 2022: 1
FWCI
0.69
Type
conference paper
Language
en
Open access
closed
Licence
Locations
Lecture notes in computer science (publishedVersion)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
  • Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
  • Health Sciences › Medicine › Critical Care and Intensive Care Medicine › Sepsis Diagnosis and Treatment
Keywords
domain adaptation, domain generalization, covariate shift, systematic bias, early prediction, racial disparities, minority patients, medical systems, clinical prediction model

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