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De-identification

De-identification is the task of detecting privacy-related entities in text, such as person names, emails and contact data.

Papers

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Showing 141–150 of 174 papers

TitleStatusHype
Augmenting a De-identification System for Swedish Clinical Text Using Open Resources and Deep Learning—0
Feature Robustness in Non-stationary Health Records: Caveats to Deployable Model Performance in Common Clinical Machine Learning TasksCode0
KU\_ai at MEDIQA 2019: Domain-specific Pre-training and Transfer Learning for Medical NLI—0
Reversible Privacy Preservation using Multi-level Encryption and Compressive Sensing—0
Scrubbing Sensitive PHI Data from Medical Records made Easy by SpaCy -- A Scalable Model Implementation Comparisons—0
Adversarial Learning of Privacy-Preserving Text Representations for De-Identification of Medical RecordsCode0
Audio De-identification - a New Entity Recognition Task—0
Towards Automatic Generation of Shareable Synthetic Clinical Notes Using Neural Language ModelsCode0
AnonymousNet: Natural Face De-Identification with Measurable Privacy—0
Publicly Available Clinical BERT EmbeddingsCode0
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