Systematic evaluation of over 100 ML4H papers finds poorer reproducibility than other ML fields, driven by limited data and code access, and offers recommendations to data providers, publishers, and researchers.
Annotating longitudinal clinical narratives for de- identification: The 2014 i2b2/UTHealth corpus
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Reproducibility in Machine Learning for Health
Systematic evaluation of over 100 ML4H papers finds poorer reproducibility than other ML fields, driven by limited data and code access, and offers recommendations to data providers, publishers, and researchers.