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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| Auteurs principaux: | , , , , , , , |
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| Format: | article |
| Langue: | EN |
| Publié: |
Nature Portfolio
2021
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| Accès en ligne: | https://doaj.org/article/92ef4fc958d544dcababaa3db903e24e |
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