AIMC Topic: 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...
Switzerland Data Anonymization Electronic Health Records Natural Language Processing Humans
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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...
Humans Confidentiality Machine Learning Data Anonymization Quality Assurance, Health Care Brain Magnetic Resonance Imaging Random Forest
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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...
Humans Republic of Korea Deep Learning Data Anonymization
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Transferability of neural network clinical deidentification systems.

Journal of the American Medical Informatics Association : JAMIA Nov 25, 2021
OBJECTIVE: Neural network deidentification studies have focused on individual datasets. These studies assume the availability of a sufficient amount of human-annotated data to train models that can generalize to corresponding test data. In real-world...
Humans Neural Networks, Computer Data Anonymization
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Application of Bayesian networks to generate synthetic health data.

Journal of the American Medical Informatics Association : JAMIA Mar 18, 2021
OBJECTIVE: This study seeks to develop a fully automated method of generating synthetic data from a real dataset that could be employed by medical organizations to distribute health data to researchers, reducing the need for access to real data. We h...
Data Management Bayes Theorem Disclosure Machine Learning Information Dissemination Datasets as Topic Confidentiality Data Anonymization Humans Neural Networks, Computer
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Fold-stratified cross-validation for unbiased and privacy-preserving federated learning.

Journal of the American Medical Informatics Association : JAMIA Aug 1, 2020
OBJECTIVE: We introduce fold-stratified cross-validation, a validation methodology that is compatible with privacy-preserving federated learning and that prevents data leakage caused by duplicates of electronic health records (EHRs).
Confidentiality Data Anonymization Computer Security Algorithms Electronic Health Records Humans Machine Learning
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Using word embeddings to improve the privacy of clinical notes.

Journal of the American Medical Informatics Association : JAMIA Jun 1, 2020
OBJECTIVE: In this work, we introduce a privacy technique for anonymizing clinical notes that guarantees all private health information is secured (including sensitive data, such as family history, that are not adequately covered by current technique...
Natural Language Processing Algorithms Confidentiality Electronic Health Records Health Insurance Portability and Accountability Act Data Anonymization Humans United States Health Records, Personal
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Ensuring electronic medical record simulation through better training, modeling, and evaluation.

Journal of the American Medical Informatics Association : JAMIA Jan 1, 2020
OBJECTIVE: Electronic medical records (EMRs) can support medical research and discovery, but privacy risks limit the sharing of such data on a wide scale. Various approaches have been developed to mitigate risk, including record simulation via genera...
Child Models, Theoretical Privacy Datasets as Topic Data Anonymization Adult Humans Male Electronic Health Records Neural Networks, Computer Female Computer Simulation Age Distribution
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The machine giveth and the machine taketh away: a parrot attack on clinical text deidentified with hiding in plain sight.

Journal of the American Medical Informatics Association : JAMIA Dec 1, 2019
OBJECTIVE: Clinical corpora can be deidentified using a combination of machine-learned automated taggers and hiding in plain sight (HIPS) resynthesis. The latter replaces detected personally identifiable information (PII) with random surrogates, allo...
Ambulatory Care Facilities Computer Security Confidentiality Data Anonymization Humans Delivery of Health Care Washington Machine Learning Electronic Health Records Personally Identifiable Information
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Deep Learning Approaches Outperform Conventional Strategies in De-Identification of German Medical Reports.

Studies in health technology and informatics Sep 3, 2019
One of the major obstacles for research on German medical reports is the lack of de-identified medical corpora. Previous de-identification tasks focused on non-German medical texts, which raised the demand for an in-depth evaluation of de-identificat...
Data Anonymization Deep Learning Machine Learning Natural Language Processing Electronic Health Records
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