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Diagnostic framework to validate clinical machine learning models locally on temporally stamped data
2
Zitationen
5
Autoren
2025
Jahr
Abstract
The work in this study emphasizes the importance of data timeliness and relevance. The results on ACU in cancer patients show moderate signs of drift and corroborate the relevance of temporal considerations when validating machine learning models for deployment at the point of care.
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