Anonymization through data synthesis using generative adversarial networks (ads-gan)
J Yoon, LN Drumright… - IEEE journal of …, 2020 - ieeexplore.ieee.org
The medical and machine learning communities are relying on the promise of artificial
intelligence (AI) to transform medicine through enabling more accurate decisions and …
intelligence (AI) to transform medicine through enabling more accurate decisions and …
[HTML][HTML] Analyzing medical research results based on synthetic data and their relation to real data results: systematic comparison from five observational studies
AR Benaim, R Almog, Y Gorelik… - JMIR medical …, 2020 - medinform.jmir.org
Background: Privacy restrictions limit access to protected patient-derived health information
for research purposes. Consequently, data anonymization is required to allow researchers …
for research purposes. Consequently, data anonymization is required to allow researchers …
[HTML][HTML] SynSigGAN: Generative adversarial networks for synthetic biomedical signal generation
Simple Summary This paper proposes a novel generative adversarial networks model,
SynSigGAN, to generate any kind of synthetic biomedical signals. The generation of …
SynSigGAN, to generate any kind of synthetic biomedical signals. The generation of …
[PDF][PDF] CorGAN: correlation-capturing convolutional generative adversarial networks for generating synthetic healthcare records
Deep learning models have demonstrated high-quality performance in areas such as image
classification and speech processing. However, creating a deep learning model using …
classification and speech processing. However, creating a deep learning model using …
A novel approach to create synthetic biomedical signals using BiRNN
A Hernandez-Matamoros, H Fujita, H Perez-Meana - Information Sciences, 2020 - Elsevier
Human health is threatened by several diseases for this reason automated medical
diagnosis systems has been developed several years ago. These systems need databases …
diagnosis systems has been developed several years ago. These systems need databases …
Ensuring electronic medical record simulation through better training, modeling, and evaluation
Abstract 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 …
discovery, but privacy risks limit the sharing of such data on a wide scale. Various …
CONAN: complementary pattern augmentation for rare disease detection
Rare diseases affect hundreds of millions of people worldwide but are hard to detect since
they have extremely low prevalence rates (varying from 1/1,000 to 1/200,000 patients) and …
they have extremely low prevalence rates (varying from 1/1,000 to 1/200,000 patients) and …
Synthetic data: Opening the data floodgates to enable faster, more directed development of machine learning methods
Many ground-breaking advancements in machine learning can be attributed to the
availability of a large volume of rich data. Unfortunately, many large-scale datasets are …
availability of a large volume of rich data. Unfortunately, many large-scale datasets are …
[图书][B] Statistics and machine learning methods for EHR data: From Data Extraction to Data Analytics
The use of Electronic Health Records (EHR)/Electronic Medical Records (EMR) data is
becoming more prevalent for research. However, analysis of this type of data has many …
becoming more prevalent for research. However, analysis of this type of data has many …
[图书][B] End-to-end machine learning frameworks for medicine: Data imputation, model interpretation and synthetic data generation
J Yoon - 2020 - search.proquest.com
Tremendous successes in machine learning have been achieved in a variety of applications
such as image classification and language translation via supervised learning frameworks …
such as image classification and language translation via supervised learning frameworks …