Diagnostic framework to validate clinical machine learning models locally on temporally stamped data
Abstract Background Real-world medical environments such as oncology are highly dynamic due to rapid changes in medical practice, technologies, and patient characteristics. This variability, if not addressed, can result in data shifts with potentially poor model performance. Presently, there are few...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-07-01
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| Series: | Communications Medicine |
| Online Access: | https://doi.org/10.1038/s43856-025-00965-w |
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