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

Abstract 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...

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Autores principales: M. A. Dakka, T. V. Nguyen, J. M. M. Hall, S. M. Diakiw, M. VerMilyea, R. Linke, M. Perugini, D. Perugini
Formato: article
Lenguaje:EN
Publicado: Nature Portfolio 2021
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Acceso en línea:https://doaj.org/article/92ef4fc958d544dcababaa3db903e24e
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