De-identification is the task of detecting protected health information (PHI) in medical text.
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.
We test the generalizability of three de-identification methods across languages and domains.
Exploiting natural language processing in the clinical domain requires de-identification, i. e., anonymization of personal information in texts.
In many real-world scenarios like people tracking or action recognition, it is important to be able to process the data while taking careful consideration in protecting people's identity.
The software is able to perform named entity recognition using some of the state-of-the-art techniques and then mask or redact recognized entities.
We propose a reversible face de-identification method for low resolution video data, where landmark-based techniques cannot be reliably used.
The clinical time-series setting poses a unique combination of challenges to data modeling and sharing.
Machine Learning approaches to Natural Language Processing tasks benefit from a comprehensive collection of real-life user data.
De-identification is the task of identifying protected health information (PHI) in the clinical text.