20 papers with code • 0 benchmarks • 0 datasets
De-identification is the task of detecting privacy-related entities in text, such as person names, emails and contact data.
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Finally, we discuss the privacy concerns associated with sharing synthetic data produced by GANs and test their ability to withstand a simple membership inference attack.
The proliferation of speech technologies and rising privacy legislation calls for the development of privacy preservation solutions for speech applications.
It yields an F1-score of 97. 85 on the i2b2 2014 dataset, with a recall 97. 38 and a precision of 97. 32, and an F1-score of 99. 23 on the MIMIC de-identification dataset, with a recall 99. 25 and a precision of 99. 06.
A variety of methods existing for generating synthetic electronic health records (EHRs), but they are not capable of generating unstructured text, like emergency department (ED) chief complaints, history of present illness or progress notes.
In order to use medical text for research purposes, it is necessary to de-identify the text for legal and privacy reasons.
Large-scale clinical data is invaluable to driving many computational scientific advances today.
Adversarial Learning of Privacy-Preserving Text Representations for De-Identification of Medical Records
De-identification is the task of detecting protected health information (PHI) in medical text.
Feature Robustness in Non-stationary Health Records: Caveats to Deployable Model Performance in Common Clinical Machine Learning Tasks
When training clinical prediction models from electronic health records (EHRs), a key concern should be a model's ability to sustain performance over time when deployed, even as care practices, database systems, and population demographics evolve.