Synthetic data generation for tabular health records: A systematic review
Synthetic data generation (SDG) research has been ongoing for some time with promising
results in different application domains, including healthcare, biometrics and energy …
results in different application domains, including healthcare, biometrics and energy …
Natural language processing in electronic health records in relation to healthcare decision-making: a systematic review
Abstract Background: Natural Language Processing (NLP) is widely used to extract clinical
insights from Electronic Health Records (EHRs). However, the lack of annotated data …
insights from Electronic Health Records (EHRs). However, the lack of annotated data …
[图书][B] Synthetic data for deep learning
SI Nikolenko - 2021 - Springer
You are holding in your hands… oh, come on, who holds books like this in their hands
anymore? Anyway, you are reading this, and it means that I have managed to release one of …
anymore? Anyway, you are reading this, and it means that I have managed to release one of …
Generative adversarial networks and its applications in biomedical informatics
The basic Generative Adversarial Networks (GAN) model is composed of the input vector,
generator, and discriminator. Among them, the generator and discriminator are implicit …
generator, and discriminator. Among them, the generator and discriminator are implicit …
Differentially private synthetic medical data generation using convolutional GANs
Deep learning models have demonstrated superior performance in several real-world
application problems such as image classification and speech processing. However …
application problems such as image classification and speech processing. However …
Downstream task performance of BERT models pre-trained using automatically de-identified clinical data
Automatic de-identification is a cost-effective and straightforward way of removing large
amounts of personally identifiable information from large and sensitive corpora. However …
amounts of personally identifiable information from large and sensitive corpora. However …
Explanatory predictive model for COVID-19 severity risk employing machine learning, shapley addition, and LIME
The rapid spread of SARS-CoV-2 threatens global public health and impedes the operation
of healthcare systems. Several studies have been conducted to confirm SARS-CoV-2 …
of healthcare systems. Several studies have been conducted to confirm SARS-CoV-2 …
Synthetic tabular data evaluation in the health domain covering resemblance, utility, and privacy dimensions
Background Synthetic tabular data generation is a potentially valuable technology with great
promise for data augmentation and privacy preservation. However, prior to adoption, an …
promise for data augmentation and privacy preservation. However, prior to adoption, an …
Are synthetic clinical notes useful for real natural language processing tasks: A case study on clinical entity recognition
Objective: Developing clinical natural language processing systems often requires access to
many clinical documents, which are not widely available to the public due to privacy and …
many clinical documents, which are not widely available to the public due to privacy and …
Twin: Personalized clinical trial digital twin generation
Clinical trial digital twins are virtual patients that reflect personal characteristics in a high
degree of granularity and can be used to simulate various patient outcomes under different …
degree of granularity and can be used to simulate various patient outcomes under different …