Efficient automated error detection in medical data using deep-learning and label-clustering

Medical datasets inherently contain errors from subjective or inaccurate test results, or from confounding biological complexities. It is difficult for medical experts to detect these elusive errors manually, due to lack of contextual information, limiting data privacy regulations, and the sheer sca...

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Detalles Bibliográficos
Autores principales: Nguyen, T. V., Diakiw, S. M., VerMilyea, M. D., Dinsmore, A. W., Perugini, M., Perugini, D., Hall, J. M. M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10638377/
https://www.ncbi.nlm.nih.gov/pubmed/37949906
http://dx.doi.org/10.1038/s41598-023-45946-y

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10638377/
https://www.ncbi.nlm.nih.gov/pubmed/37949906
http://dx.doi.org/10.1038/s41598-023-45946-y

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