Customization scenarios for de-identification of clinical notes

Customization scenarios for de-identification of clinical notes

Abstract 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 per...

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Bibliographic Details
Main Authors: Tzvika Hartman, Michael D. Howell, Jeff Dean, Shlomo Hoory, Ronit Slyper, Itay Laish, Oren Gilon, Danny Vainstein, Greg Corrado, Katherine Chou, Ming Jack Po, Jutta Williams, Scott Ellis, Gavin Bee, Avinatan Hassidim, Rony Amira, Genady Beryozkin, Idan Szpektor, Yossi Matias
Format: Article
Language:English
Published: BMC 2020-01-01
Series:BMC Medical Informatics and Decision Making
Subjects:
De-identification
Electronic health records
Free text
Clinical notes
Natural language processing
Recurrent neural networks
Online Access:https://doi.org/10.1186/s12911-020-1026-2

Internet

https://doi.org/10.1186/s12911-020-1026-2

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