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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 126–150 of 174 papers

TitleStatusHype
A Deep Learning Architecture for De-identification of Patient Notes: Implementation and Evaluation—0
Adversarial Privacy Preservation in MRI Scans of the Brain—0
Alternating Loss Correction for Preterm-Birth Prediction from EHR Data with Noisy Labels—0
An Analysis Of Protected Health Information Leakage In Deep-Learning Based De-Identification Algorithms—0
An Easy-to-use and Robust Approach for the Differentially Private De-Identification of Clinical Textual Documents—0
An Overview of AI and Blockchain Integration for Privacy-Preserving—0
Antibiotic Resistance Microbiology Dataset (ARMD): A De-identified Resource for Studying Antimicrobial Resistance Using Electronic Health Records—0
Applying and Sharing pre-trained BERT-models for Named Entity Recognition and Classification in Swedish Electronic Patient Records—0
A Semi-supervised Approach for De-identification of Swedish Clinical Text—0
AspirinSum: an Aspect-based utility-preserved de-identification Summarization framework—0
A survey of automatic de-identification of longitudinal clinical narratives—0
A Systematical Solution for Face De-identification—0
Audio De-identification: A New Entity Recognition Task—0
Audio De-identification - a New Entity Recognition Task—0
Augmenting a De-identification System for Swedish Clinical Text Using Open Resources and Deep Learning—0
Automatic end-to-end De-identification: Is high accuracy the only metric?—0
Benchmarking Modern Named Entity Recognition Techniques for Free-text Health Record De-identification—0
Beyond Accuracy: Automated De-Identification of Large Real-World Clinical Text Datasets—0
Beyond De-Identification: A Structured Approach for Defining and Detecting Indirect Identifiers in Medical Texts—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
Classifying Cyber-Risky Clinical Notes by Employing Natural Language Processing—0
CodE Alltag 2.0 --- A Pseudonymized German-Language Email Corpus—0
Conditional De-Identification of 3D Magnetic Resonance Images—0
Creating and Evaluating a Synthetic Norwegian Clinical Corpus for De-Identification—0
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