Automated detection of poor-quality data: case studies in healthcare

The detection and removal of poor-quality data in a training set is crucial to achieve high-performing AI models. In healthcare, data can be inherently poor-quality due to uncertainty or subjectivity, but as is often the case, the requirement for data privacy restricts AI practitioners from accessin...

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Detalles Bibliográficos
Autores principales: Dakka, M. A., Nguyen, T. V., Hall, J. M. M., Diakiw, S. M., VerMilyea, M., Linke, R., Perugini, M., Perugini, D.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8429593/
https://www.ncbi.nlm.nih.gov/pubmed/34504205
http://dx.doi.org/10.1038/s41598-021-97341-0

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8429593/
https://www.ncbi.nlm.nih.gov/pubmed/34504205
http://dx.doi.org/10.1038/s41598-021-97341-0

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