Papers De-identification
“De-identification” 태그가 달린 논문 174편 · 필터 해제
Enhancing Clinical Models with Pseudo Data for De-identification
Many models are pretrained on redacted text for privacy reasons. Clinical foundation models are often trained on de-identified text, which uses special syntax (masked) text in place of protected health information. Even …
Dataset GenerationDe-identificationRedactOR: An LLM-Powered Framework for Automatic Clinical Data De-Identification
Ensuring clinical data privacy while preserving utility is critical for AI-driven healthcare and data analytics. Existing de-identification (De-ID) methods, including rule-based techniques, deep learning models, and larg…
De-identificationRe-identification of De-identified Documents with Autoregressive Infilling
Documents revealing sensitive information about individuals must typically be de-identified. This de-identification is often done by masking all mentions of personally identifiable information (PII), thereby making it mo…
De-identificationRAGLarge Language Model Empowered Privacy-Protected Framework for PHI Annotation in Clinical Notes
The de-identification of private information in medical data is a crucial process to mitigate the risk of confidentiality breaches, particularly when patient personal details are not adequately removed before the release…
De-identificationLanguage ModelingLanguage ModellingLarge Language ModelPalmprint De-Identification Using Diffusion Model for High-Quality and Diverse Synthesis
Palmprint recognition techniques have advanced significantly in recent years, enabling reliable recognition even when palmprints are captured in uncontrolled or challenging environments. However, this strength also intro…
De-identificationNatural Language Processing for Electronic Health Records in Scandinavian Languages: Norwegian, Swedish, and Danish
Background: Clinical natural language processing (NLP) refers to the use of computational methods for extracting, processing, and analyzing unstructured clinical text data, and holds a huge potential to transform healthc…
ArticlesDe-identificationTransfer LearningCan Zero-Shot Commercial APIs Deliver Regulatory-Grade Clinical Text DeIdentification?
We evaluate the performance of four leading solutions for de-identification of unstructured medical text - Azure Health Data Services, AWS Comprehend Medical, OpenAI GPT-4o, and John Snow Labs - on a ground truth dataset…
De-identificationAntibiotic Resistance Microbiology Dataset (ARMD): A De-identified Resource for Studying Antimicrobial Resistance Using Electronic Health Records
The Antibiotic Resistance Microbiology Dataset (ARMD) is a de-identified resource derived from electronic health records (EHR) that facilitates research into antimicrobial resistance (AMR). ARMD encompasses data from adu…
Causal InferenceDecision MakingDe-identificationData-Constrained Synthesis of Training Data for De-Identification
Many sensitive domains -- such as the clinical domain -- lack widely available datasets due to privacy risks. The increasing generative capabilities of large language models (LLMs) have made synthetic datasets a viable p…
De-identificationNERBeyond De-Identification: A Structured Approach for Defining and Detecting Indirect Identifiers in Medical Texts
Sharing sensitive texts for scientific purposes requires appropriate techniques to protect the privacy of patients and healthcare personnel. Anonymizing textual data is particularly challenging due to the presence of div…
De-identificationZero-shot generation of synthetic neurosurgical data with large language models
Clinical data is fundamental to advance neurosurgical research, but access is often constrained by data availability, small sample sizes, privacy regulations, and resource-intensive preprocessing and de-identification pr…
BenchmarkingDe-identificationGenerative Adversarial NetworkLarge Language ModelThe Devil is in the Prompts: De-Identification Traces Enhance Memorization Risks in Synthetic Chest X-Ray Generation
Generative models, particularly text-to-image (T2I) diffusion models, play a crucial role in medical image analysis. However, these models are prone to training data memorization, posing significant risks to patient priv…
BenchmarkingDe-identificationMedical Image AnalysisMemorizationExploring AI-based System Design for Pixel-level Protected Health Information Detection in Medical Images
Purpose: This study aims to evaluate different setups of an AI-based solution to detect Protected Health Information (PHI) in medical images. Materials and Methods: Text from eight PHI and eight non-PHI categories are si…
De-identificationOptical Character RecognitionOptical Character Recognition (OCR)Text DetectionSVIA: A Street View Image Anonymization Framework for Self-Driving Applications
In recent years, there has been an increasing interest in image anonymization, particularly focusing on the de-identification of faces and individuals. However, for self-driving applications, merely de-identifying faces …
De-identificationImage GenerationEnhancing the De-identification of Personally Identifiable Information in Educational Data
Protecting Personally Identifiable Information (PII), such as names, is a critical requirement in learning technologies to safeguard student and teacher privacy and maintain trust. Accurate PII detection is an essential …
De-identificationMedical Manifestation-Aware De-Identification
Face de-identification (DeID) has been widely studied for common scenes, but remains under-researched for medical scenes, mostly due to the lack of large-scale patient face datasets. In this paper, we release MeMa, consi…
De-identificationLLMs-in-the-Loop Part 2: Expert Small AI Models for Anonymization and De-identification of PHI Across Multiple Languages
The rise of chronic diseases and pandemics like COVID-19 has emphasized the need for effective patient data processing while ensuring privacy through anonymization and de-identification of protected health information (P…
De-identificationDocument TranslationNERRelation ExtractionFace De-identification: State-of-the-art Methods and Comparative Studies
The widespread use of image acquisition technologies, along with advances in facial recognition, has raised serious privacy concerns. Face de-identification usually refers to the process of concealing or replacing person…
De-identificationSurveyIn-Context Learning for Preserving Patient Privacy: A Framework for Synthesizing Realistic Patient Portal Messages
Since the COVID-19 pandemic, clinicians have seen a large and sustained influx in patient portal messages, significantly contributing to clinician burnout. To the best of our knowledge, there are no large-scale public pa…
De-identificationIn-Context LearningPrivacy PreservingText GenerationLarge Language Models for Patient Comments Multi-Label Classification
Patient experience and care quality are crucial for a hospital's sustainability and reputation. The analysis of patient feedback offers valuable insight into patient satisfaction and outcomes. However, the unstructured n…
De-identificationFew-Shot LearningIn-Context LearningMulti-Label Classification+7