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The importance of being external. methodological insights for the external validation of machine learning models in medicine

Authors: Federico Cabitza; Andrea Campagner; Felipe Soares; Luis García de Guadiana-Romualdo; Feyissa Challa; Adela Sulejmani; Michela Seghezzi; +1 Authors

The importance of being external. methodological insights for the external validation of machine learning models in medicine

Abstract

In this repository you can find the pre-print and data associated with the publication titled "The Importance of Being External. Methodological insights for the external validation of machine learning models in medicine. The data is collected in the file "Datasets.zip" and, more in particular: "all-data-processed-v3.xlsx" contains the Brazil-1, Brazil-2 and Brazil-3 datasets "desio_cbc.xls", "bergamo_cbc.xls" and "HSR_novembre.xlsx" contain the Italy-1, Italy-2 and Italy-3 datasets "CBC Italy Octubre (sent 24-12-2020).xlsx" contains the Spain dataset "Etiopia 200 COVID +.xlsx" and "cbc data_anna copy.xlsx" contains the Ethiopia dataset (positive and negative cases, respectively) Code showing how to process the data, as well as all code for producing the visualizations shown in the paper is available at https://github.com/AndreaCampagner/qualiMLpy For any further information, you can contact me at a.campagner@campus.unimib.it

Country
Italy
Keywords

Cohort Studies, Machine Learning, SARS-CoV-2, COVID-19; Dataset cardinality; Dataset similarity; Medical machine learning; Validation;, COVID-19, Humans, Reproducibility of Results

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
views
OpenAIRE UsageCountsViews provided by UsageCounts
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240
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