Customization scenarios for de-identification of clinical notes

BACKGROUND: Automated machine-learning systems are able to de-identify electronic medical records, including free-text clinical notes. Use of such systems would greatly boost the amount of data available to researchers, yet their deployment has been limited due to uncertainty about their performance...

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Detalles Bibliográficos
Autores principales: Hartman, Tzvika, Howell, Michael D., Dean, Jeff, Hoory, Shlomo, Slyper, Ronit, Laish, Itay, Gilon, Oren, Vainstein, Danny, Corrado, Greg, Chou, Katherine, Po, Ming Jack, Williams, Jutta, Ellis, Scott, Bee, Gavin, Hassidim, Avinatan, Amira, Rony, Beryozkin, Genady, Szpektor, Idan, Matias, Yossi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6993314/
https://www.ncbi.nlm.nih.gov/pubmed/32000770
http://dx.doi.org/10.1186/s12911-020-1026-2

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6993314/
https://www.ncbi.nlm.nih.gov/pubmed/32000770
http://dx.doi.org/10.1186/s12911-020-1026-2

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