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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 151–160 of 174 papers

Title	Date	Tasks	Status	Hype
DEDUCE: A pattern matching method for automatic de-identification of Dutch medical text	Jul 1, 2018	De-identification	CodeCode Available	0
Feature Robustness in Non-stationary Health Records: Caveats to Deployable Model Performance in Common Clinical Machine Learning Tasks	Aug 2, 2019	De-identificationLength-of-Stay prediction	CodeCode Available	0
DeIDClinic: A Multi-Layered Framework for De-identification of Clinical Free-text Data	Oct 2, 2024	De-identification	CodeCode Available	0
Publicly Available Clinical BERT Embeddings	Apr 6, 2019	De-identification	CodeCode Available	0
Medical Manifestation-Aware De-Identification	Dec 14, 2024	De-identification	CodeCode Available	0
Computational Job Market Analysis with Natural Language Processing	Apr 29, 2024	Active LearningDe-identification	CodeCode Available	0
Generating Synthetic Free-text Medical Records with Low Re-identification Risk using Masked Language Modeling	Sep 15, 2024	Causal Language ModelingDe-identification	CodeCode Available	0
Generation and De-Identification of Indian Clinical Discharge Summaries using LLMs	Jul 8, 2024	De-identificationIn-Context Learning	CodeCode Available	0
Natural Language Generation for Electronic Health Records	Jun 1, 2018	DecoderDe-identification	CodeCode Available	0
Zero-shot generation of synthetic neurosurgical data with large language models	Feb 13, 2025	BenchmarkingDe-identification	CodeCode Available	0

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