AIMC Topic: Data Anonymization

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Technical Note: An embedding-based medical note de-identification approach with sparse annotation.

Medical physics Feb 12, 2021
PURPOSE: Medical note de-identification is critical for the protection of private information and the security of data sharing in collaborative research. The task demands the complete removal of all patient names and other sensitive information such ...
Sensitivity and Specificity Natural Language Processing Data Anonymization Humans Electronic Health Records
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Canadian Association of Radiologists White Paper on De-identification of Medical Imaging: Part 2, Practical Considerations.

Canadian Association of Radiologists journal = Journal l'Association canadienne des radiologistes Nov 3, 2020
The application of big data, radiomics, machine learning, and artificial intelligence (AI) algorithms in radiology requires access to large data sets containing personal health information. Because machine learning projects often require collaboratio...
Humans Artificial Intelligence Machine Learning Societies, Medical Diagnostic Imaging Algorithms Radiologists Canada Data Anonymization
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Canadian Association of Radiologists White Paper on De-Identification of Medical Imaging: Part 1, General Principles.

Canadian Association of Radiologists journal = Journal l'Association canadienne des radiologistes Nov 3, 2020
The application of big data, radiomics, machine learning, and artificial intelligence (AI) algorithms in radiology requires access to large data sets containing personal health information. Because machine learning projects often require collaboratio...
Radiologists Humans Canada Machine Learning Data Anonymization Artificial Intelligence Diagnostic Imaging Societies, Medical Algorithms
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FPGAN: Face de-identification method with generative adversarial networks for social robots.

Neural networks : the official journal of the International Neural Network Society Sep 20, 2020
In this paper, we propose a new face de-identification method based on generative adversarial network (GAN) to protect visual facial privacy, which is an end-to-end method (herein, FPGAN). First, we propose FPGAN and mathematically prove its converge...
Humans Image Processing, Computer-Assisted Robotics Data Anonymization Automated Facial Recognition
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Anonymization Through Data Synthesis Using Generative Adversarial Networks (ADS-GAN).

IEEE journal of biomedical and health informatics Mar 12, 2020
The medical and machine learning communities are relying on the promise of artificial intelligence (AI) to transform medicine through enabling more accurate decisions and personalized treatment. However, progress is slow. Legal and ethical issues aro...
Data Anonymization Information Dissemination Humans Neural Networks, Computer Female Male Electronic Health Records
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De-identification of Clinical Text via Bi-LSTM-CRF with Neural Language Models.

AMIA ... Annual Symposium proceedings. AMIA Symposium Mar 4, 2020
De-identification of clinical text, the prerequisite of electronic clinical data reuse, is a typical named entity recogni tion (NER) problem. A number of state-of-the-art deep learning methods for NER, such as Bi-LSTM-CRF (bidirec tional long-short-t...
Neural Networks, Computer Natural Language Processing Data Anonymization Language Deep Learning
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Customization scenarios for de-identification of clinical notes.

BMC medical informatics and decision making Jan 30, 2020
BACKGROUND: Automated machine-learning systems are able to de-identify electronic medical records, including free-text clinical notes. Use of such systems would greatly boost the amount of data available to researchers, yet their deployment has been ...
Electronic Health Records Data Anonymization Datasets as Topic Humans Machine Learning
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A study of deep learning methods for de-identification of clinical notes in cross-institute settings.

BMC medical informatics and decision making Dec 5, 2019
BACKGROUND: De-identification is a critical technology to facilitate the use of unstructured clinical text while protecting patient privacy and confidentiality. The clinical natural language processing (NLP) community has invested great efforts in de...
Deep Learning Natural Language Processing Data Anonymization Linguistics Confidentiality Humans Electronic Health Records
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Parsing clinical text using the state-of-the-art deep learning based parsers: a systematic comparison.

BMC medical informatics and decision making Apr 4, 2019
BACKGROUND: A shareable repository of clinical notes is critical for advancing natural language processing (NLP) research, and therefore a goal of many NLP researchers is to create a shareable repository of clinical notes, that has breadth (from mult...
Surveys and Questionnaires Natural Language Processing Data Anonymization Information Dissemination Patient Participation Humans Confidentiality Databases as Topic Biomedical Research New York Male Female Deep Learning Adult
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Big Data Analysis and Machine Learning in Intensive Care Units.

Medicina intensiva Dec 24, 2018
Intensive care is an ideal environment for the use of Big Data Analysis (BDA) and Machine Learning (ML), due to the huge amount of information processed and stored in electronic format in relation to such care. These tools can improve our clinical re...
Information Dissemination Intensive Care Units Electronic Health Records Information Storage and Retrieval Randomized Controlled Trials as Topic Data Analysis Humans Natural Language Processing Data Anonymization Databases, Factual Big Data Machine Learning Critical Care Retrospective Studies
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