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Impact of De-Identification on Clinical Text Classification Using Traditional and Deep Learning Classifiers
Clinical text de-identification enables collaborative research while protecting patient privacy and confidentiality; however, concerns persist about the reduction in the utility of the de-identified text for information extraction and machine learning tasks. In the context of a deep learning experim...
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| Publicado no: | Stud Health Technol Inform |
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| Main Authors: | , , , , , , , |
| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
2019
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| Assuntos: | |
| Acesso em linha: | https://ncbi.nlm.nih.gov/pmc/articles/PMC6779034/ https://ncbi.nlm.nih.gov/pubmed/31437930 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.3233/SHTI190228 |
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