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A framework for understanding label leakage in machine learning for health care

Journal of the American Medical Informatics Association : JAMIA, 2023-12, Vol.31 (1), p.274-280 [Peer Reviewed Journal]

The Author(s) 2023. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com 2023 ;The Author(s) 2023. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com. ;ISSN: 1067-5027 ;EISSN: 1527-974X ;DOI: 10.1093/jamia/ocad178 ;PMID: 37669138

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  • Title:
    A framework for understanding label leakage in machine learning for health care
  • Author: Davis, Sharon E ; Matheny, Michael E ; Balu, Suresh ; Sendak, Mark P
  • Subjects: Delivery of Health Care ; Health Facilities ; Language ; Machine Learning
  • Is Part Of: Journal of the American Medical Informatics Association : JAMIA, 2023-12, Vol.31 (1), p.274-280
  • Description: Abstract Introduction The pitfalls of label leakage, contamination of model input features with outcome information, are well established. Unfortunately, avoiding label leakage in clinical prediction models requires more nuance than the common advice of applying “no time machine rule.” Framework We provide a framework for contemplating whether and when model features pose leakage concerns by considering the cadence, perspective, and applicability of predictions. To ground these concepts, we use real-world clinical models to highlight examples of appropriate and inappropriate label leakage in practice. Recommendations Finally, we provide recommendations to support clinical and technical stakeholders as they evaluate the leakage tradeoffs associated with model design, development, and implementation decisions. By providing common language and dimensions to consider when designing models, we hope the clinical prediction community will be better prepared to develop statistically valid and clinically useful machine learning models.
  • Publisher: England: Oxford University Press
  • Language: English
  • Identifier: ISSN: 1067-5027
    EISSN: 1527-974X
    DOI: 10.1093/jamia/ocad178
    PMID: 37669138
  • Source: Geneva Foundation Free Medical Journals at publisher websites
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