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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 21–30 of 174 papers

TitleStatusHype
Synthesis of Realistic ECG using Generative Adversarial NetworksCode1
Enhancing Clinical Models with Pseudo Data for De-identificationCode0
RedactOR: An LLM-Powered Framework for Automatic Clinical Data De-Identification—0
Re-identification of De-identified Documents with Autoregressive Infilling—0
Large Language Model Empowered Privacy-Protected Framework for PHI Annotation in Clinical Notes—0
Palmprint De-Identification Using Diffusion Model for High-Quality and Diverse Synthesis—0
Natural Language Processing for Electronic Health Records in Scandinavian Languages: Norwegian, Swedish, and Danish—0
Can Zero-Shot Commercial APIs Deliver Regulatory-Grade Clinical Text DeIdentification?—0
Antibiotic Resistance Microbiology Dataset (ARMD): A De-identified Resource for Studying Antimicrobial Resistance Using Electronic Health Records—0
Data-Constrained Synthesis of Training Data for De-Identification—0
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