A clinician’s guide to understanding and critically appraising machine learning studies: a checklist for Ruling Out Bias Using Standard Tools in Machine Learning (ROBUST-ML)

Developing functional machine learning (ML)-based models to address unmet clinical needs requires unique considerations for optimal clinical utility. Recent debates about the rigours, transparency, explainability, and reproducibility of ML models, terms which are defined in this article, have raised...

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Detalles Bibliográficos
Autores principales: Al-Zaiti, Salah S, Alghwiri, Alaa A, Hu, Xiao, Clermont, Gilles, Peace, Aaron, Macfarlane, Peter, Bond, Raymond
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2022
Materias:
Point of View
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9708024/
https://www.ncbi.nlm.nih.gov/pubmed/36713011
http://dx.doi.org/10.1093/ehjdh/ztac016

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9708024/
https://www.ncbi.nlm.nih.gov/pubmed/36713011
http://dx.doi.org/10.1093/ehjdh/ztac016

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