AIMC Topic: 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 Confidentiality Disclosure Machine Learning Data Anonymization Information Dissemination Datasets as Topic 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).
Electronic Health Records Humans Confidentiality Computer Security Data Anonymization Machine Learning Algorithms
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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...
Humans Natural Language Processing Confidentiality Data Anonymization Algorithms Electronic Health Records United States Health Insurance Portability and Accountability Act 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...
Datasets as Topic Age Distribution Data Anonymization Privacy Electronic Health Records Female Adult Male Child Models, Theoretical Humans Neural Networks, Computer Computer Simulation
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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...
Delivery of Health Care Washington Personally Identifiable Information Humans Electronic Health Records Machine Learning Data Anonymization Confidentiality Ambulatory Care Facilities Computer Security
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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...
Machine Learning Electronic Health Records Deep Learning Data Anonymization Natural Language Processing
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Impact of De-Identification on Clinical Text Classification Using Traditional and Deep Learning Classifiers.

Studies in health technology and informatics Aug 21, 2019
Clinical text de-identification enables collaborative research while protecting patient privacy and confidentiality; however, concerns persist about the reduction in the utility of the de-identified text for information extraction and machine learnin...
Electronic Health Records Deep Learning Confidentiality Data Anonymization Humans Machine Learning
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Opportunities and challenges in developing deep learning models using electronic health records data: a systematic review.

Journal of the American Medical Informatics Association : JAMIA Oct 1, 2018
OBJECTIVE: To conduct a systematic review of deep learning models for electronic health record (EHR) data, and illustrate various deep learning architectures for analyzing different data sources and their target applications. We also highlight ongoin...
Neural Networks, Computer Disease Prognosis Deep Learning Electronic Health Records Humans Data Anonymization
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Community-Acquired Pneumonia Case Validation in an Anonymized Electronic Medical Record-Linked Expert System.

Clinical infectious diseases : an official publication of the Infectious Diseases Society of America May 15, 2017
An electronic anonymized patient portal analysis using radiographic reports and admission and discharge diagnoses had sensitivity, specificity, positive predictive value, and negative predictive value of 84.7%, 78.2%, 75%, and 87%, respectively, for ...
Hospitalization Data Anonymization Anti-Bacterial Agents Young Adult Pneumonia Community-Acquired Infections Expert Systems Adult Electronic Health Records Predictive Value of Tests Patient Portals Humans Radiography, Thoracic Sensitivity and Specificity Female Algorithms Male
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