AIMC Topic: Data Anonymization

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Ensemble-based Methods to Improve De-identification of Electronic Health Record Narratives.

AMIA ... Annual Symposium proceedings. AMIA Symposium Dec 5, 2018
Text de-identification is an application of clinical natural language processing that offers significant efficiency and scalability advantages. Hence, various learning algorithms have been applied to this task to yield better performance. Instead of ...
Methods Humans Machine Learning Natural Language Processing Data Anonymization Algorithms Electronic Health Records
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Is Multiclass Automatic Text De-Identification Worth the Effort?

Methods of information in medicine Sep 24, 2018
OBJECTIVES: Automatic de-identification to remove protected health information (PHI) from clinical text can use a "binary" model that replaces redacted text with a generic tag (e.g., ""), or can use a "multiclass" model that retains more class i...
Algorithms Humans Data Anonymization Electronic Health Records False Positive Reactions
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Artificial Intelligence in Public Health and Epidemiology.

Yearbook of medical informatics Aug 29, 2018
OBJECTIVES:  To introduce and summarize current research in the field of Public Health and Epidemiology Informatics.
Public Health Data Anonymization Public Health Informatics Epidemiology Artificial Intelligence Machine Learning Neural Networks, Computer
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Leveraging existing corpora for de-identification of psychiatric notes using domain adaptation.

AMIA ... Annual Symposium proceedings. AMIA Symposium Apr 16, 2018
De-identification of clinical notes is a special case of named entity recognition. Supervised machine-learning (ML) algorithms have achieved promising results for this task. However, ML-based de-identification systems often require annotating a large...
Electronic Health Records Diabetes Mellitus Algorithms Psychiatry Datasets as Topic Humans Patient Discharge Summaries Natural Language Processing Outpatients Machine Learning Data Anonymization
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Leveraging text skeleton for de-identification of electronic medical records.

BMC medical informatics and decision making Mar 22, 2018
BACKGROUND: De-identification is the first step to use these records for data processing or further medical investigations in electronic medical records. Consequently, a reliable automated de-identification system would be of high value.
Data Anonymization Humans Neural Networks, Computer Electronic Health Records
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A hybrid approach to automatic de-identification of psychiatric notes.

Journal of biomedical informatics Jun 7, 2017
De-identification, or identifying and removing protected health information (PHI) from clinical data, is a critical step in making clinical data available for clinical applications and research. This paper presents a natural language processing syste...
Data Anonymization Mental Disorders Automation Humans Natural Language Processing
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Evaluating LLMs' Potential to Identify Rare Patient Identifiers in Patient Health Records.

Studies in health technology and informatics May 15, 2025
This study explores the utility of Large Language Models (LLMs) to support finding rare patient record details that could make a patient identifiable. Whilst most research has focused on what we call direct patient identifiers, indirect patient ident...
Electronic Health Records Natural Language Processing Humans Programming Languages Confidentiality Patient Identification Systems Data Anonymization
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Accelerating Clinical Text Annotation in Underrepresented Languages: A Case Study on Text De-Identification.

Studies in health technology and informatics Aug 22, 2024
Clinical notes contain valuable information for research and monitoring quality of care. Named Entity Recognition (NER) is the process for identifying relevant pieces of information such as diagnoses, treatments, side effects, etc., and bring them to...
Natural Language Processing Switzerland Humans Electronic Health Records Data Anonymization
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Quality Assessment of Brain MRI Defacing Using Machine Learning.

Studies in health technology and informatics Aug 22, 2024
Defacing of brain magnetic resonance imaging (MRI) scans is a crucial process in medical imaging research aimed at preserving patient privacy while maintaining data integrity. However, existing defacing algorithms are prone to errors, potentially com...
Magnetic Resonance Imaging Machine Learning Random Forest Brain Confidentiality Quality Assurance, Health Care Humans Data Anonymization
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A De-Identification Model for Korean Clinical Notes: Using Deep Learning Models.

Studies in health technology and informatics Jan 25, 2024
To extract information from free-text in clinical records due to the patient's protected health information PHI in the records pre-processing of de-identification is required. Therefore we aimed to identify PHI list and fine-tune the deep learning BE...
Deep Learning Data Anonymization Humans Republic of Korea
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