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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| Principais autores: | , , , , , , , |
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| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
Nature Portfolio
2021-09-01
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| coleção: | Scientific Reports |
| Acesso em linha: | https://doi.org/10.1038/s41598-021-97341-0 |
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