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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 51–75 of 174 papers

TitleStatusHype
Generating Synthetic Free-text Medical Records with Low Re-identification Risk using Masked Language ModelingCode0
Enhancing Clinical Models with Pseudo Data for De-identificationCode0
Generation and De-Identification of Indian Clinical Discharge Summaries using LLMsCode0
PHICON: Improving Generalization of Clinical Text De-identification Models via Data AugmentationCode0
De-identifying Free Text of Japanese Dummy Electronic Health Records—0
Can Zero-Shot Commercial APIs Deliver Regulatory-Grade Clinical Text DeIdentification?—0
De-identification without losing faces—0
De-identification of Unstructured Clinical Texts from Sequence to Sequence Perspective—0
Building a De-identification System for Real Swedish Clinical Text Using Pseudonymised Clinical Text—0
De-identifying Australian Hospital Discharge Summaries: An End-to-End Framework using Ensemble of Deep Learning Models—0
Applying and Sharing pre-trained BERT-models for Named Entity Recognition and Classification in Swedish Electronic Patient Records—0
De-identification of medical records using conditional random fields and long short-term memory networks—0
Beyond De-Identification: A Structured Approach for Defining and Detecting Indirect Identifiers in Medical Texts—0
De-Identification of French Unstructured Clinical Notes for Machine Learning Tasks—0
De-Identification of Emails: Pseudonymizing Privacy-Sensitive Data in a German Email Corpus—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
De-identification of clinical free text using natural language processing: A systematic review of current approaches—0
De-Identification of Clinical Free Text in Dutch with Limited Training Data: A Case Study—0
Benchmarking Modern Named Entity Recognition Techniques for Free-text Health Record De-identification—0
De-identification is not always enough—0
De-identification In practice—0
Automatic end-to-end De-identification: Is high accuracy the only metric?—0
An Overview of AI and Blockchain Integration for Privacy-Preserving—0
A Deep Learning Architecture for De-identification of Patient Notes: Implementation and Evaluation—0
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