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Empirical assessment of bias in machine learning diagnostic test accuracy studies
48
Zitationen
3
Autoren
2020
Jahr
Abstract
The present study identifies pitfalls that threaten the validity, generalizability, and clinical value of ML diagnostic tools and provides recommendations for improvement.
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