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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Detalles Bibliográficos
Autores principales: Obeid, Jihad S., Heider, Paul M., Weeda, Erin R., Matuskowitz, Andrew J., Carr, Christine M., Gagnon, Kevin, Crawford, Tami, Meystre, Stephane M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2019
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6779034/
https://www.ncbi.nlm.nih.gov/pubmed/31437930
http://dx.doi.org/10.3233/SHTI190228

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6779034/
https://www.ncbi.nlm.nih.gov/pubmed/31437930
http://dx.doi.org/10.3233/SHTI190228

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