AIMC Topic: Data Anonymization
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A textual dataset of de-identified health records in Spanish and Catalan for medical entity recognition and anonymization.
Scientific data
Jul 1, 2025
The advancement of clinical natural language processing systems is crucial to exploit the wealth of textual data contained in medical records. Diverse data sources are required in different languages and from different sites to represent global healt...
Empowering Data Sharing in Neuroscience: A Deep Learning Deidentification Method for Pediatric Brain MRIs.
AJNR. American journal of neuroradiology
May 2, 2025
BACKGROUND AND PURPOSE: Privacy concerns, such as identifiable facial features within brain scans, have hindered the availability of pediatric neuroimaging data sets for research. Consequently, pediatric neuroscience research lags adult counterparts,...
De-identification of clinical notes with pseudo-labeling using regular expression rules and pre-trained BERT.
BMC medical informatics and decision making
Feb 17, 2025
BACKGROUND: De-identification of clinical notes is essential to utilize the rich information in unstructured text data in medical research. However, only limited work has been done in removing personal information from clinical notes in Korea.
Automated anonymization of radiology reports: comparison of publicly available natural language processing and large language models.
European radiology
Oct 31, 2024
PURPOSE: Medical reports, governed by HIPAA regulations, contain personal health information (PHI), restricting secondary data use. Utilizing natural language processing (NLP) and large language models (LLM), we sought to employ publicly available me...
Summary of the National Cancer Institute 2023 Virtual Workshop on Medical Image De-identification-Part 2: Pathology Whole Slide Image De-identification, De-facing, the Role of AI in Image De-identification, and the NCI MIDI Datasets and Pipeline.
Journal of imaging informatics in medicine
Jul 9, 2024
De-identification of medical images intended for research is a core requirement for data sharing initiatives, particularly as the demand for data for artificial intelligence (AI) applications grows. The Center for Biomedical Informatics and Informati...
Responsible AI for cardiovascular disease detection: Towards a privacy-preserving and interpretable model.
Computer methods and programs in biomedicine
Jun 17, 2024
BACKGROUND AND OBJECTIVE: Cardiovascular disease (CD) is a major global health concern, affecting millions with symptoms like fatigue and chest discomfort. Timely identification is crucial due to its significant contribution to global mortality. In h...
Automatic de-identification of French electronic health records: a cost-effective approach exploiting distant supervision and deep learning models.
BMC medical informatics and decision making
Feb 16, 2024
BACKGROUND: Electronic health records (EHRs) contain valuable information for clinical research; however, the sensitive nature of healthcare data presents security and confidentiality challenges. De-identification is therefore essential to protect pe...
De-identification of free text data containing personal health information: a scoping review of reviews.
International journal of population data science
Dec 12, 2023
INTRODUCTION: Using data in research often requires that the data first be de-identified, particularly in the case of health data, which often include Personal Identifiable Information (PII) and/or Personal Health Identifying Information (PHII). Ther...
Disentangled Representation Learning for Multiple Attributes Preserving Face Deidentification.
IEEE transactions on neural networks and learning systems
Jan 5, 2022
Face is one of the most attractive sensitive information in visual shared data. It is an urgent task to design an effective face deidentification method to achieve a balance between facial privacy protection and data utilities when sharing data. Most...
Privacy and artificial intelligence: challenges for protecting health information in a new era.
BMC medical ethics
Sep 15, 2021
BACKGROUND: Advances in healthcare artificial intelligence (AI) are occurring rapidly and there is a growing discussion about managing its development. Many AI technologies end up owned and controlled by private entities. The nature of the implementa...
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