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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
Reliable Generation of Privacy-preserving Synthetic Electronic Health Record Time Series via Diffusion ModelsCode1
A Deep Learning Architecture for De-identification of Patient Notes: Implementation and Evaluation—0
Applying and Sharing pre-trained BERT-models for Named Entity Recognition and Classification in Swedish Electronic Patient Records—0
AnonymousNet: Natural Face De-Identification with Measurable Privacy—0
Beyond De-Identification: A Structured Approach for Defining and Detecting Indirect Identifiers in Medical Texts—0
Beyond Accuracy: Automated De-Identification of Large Real-World Clinical Text Datasets—0
Antibiotic Resistance Microbiology Dataset (ARMD): A De-identified Resource for Studying Antimicrobial Resistance Using Electronic Health Records—0
Building a De-identification System for Real Swedish Clinical Text Using Pseudonymised Clinical Text—0
Can Zero-Shot Commercial APIs Deliver Regulatory-Grade Clinical Text DeIdentification?—0
Benchmarking Modern Named Entity Recognition Techniques for Free-text Health Record De-identification—0
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