1,311 research outputs found
Biosignal Generation and Latent Variable Analysis with Recurrent Generative Adversarial Networks
The effectiveness of biosignal generation and data augmentation with
biosignal generative models based on generative adversarial networks (GANs),
which are a type of deep learning technique, was demonstrated in our previous
paper. GAN-based generative models only learn the projection between a random
distribution as input data and the distribution of training data.Therefore, the
relationship between input and generated data is unclear, and the
characteristics of the data generated from this model cannot be controlled.
This study proposes a method for generating time-series data based on GANs and
explores their ability to generate biosignals with certain classes and
characteristics. Moreover, in the proposed method, latent variables are
analyzed using canonical correlation analysis (CCA) to represent the
relationship between input and generated data as canonical loadings. Using
these loadings, we can control the characteristics of the data generated by the
proposed method. The influence of class labels on generated data is analyzed by
feeding the data interpolated between two class labels into the generator of
the proposed GANs. The CCA of the latent variables is shown to be an effective
method of controlling the generated data characteristics. We are able to model
the distribution of the time-series data without requiring domain-dependent
knowledge using the proposed method. Furthermore, it is possible to control the
characteristics of these data by analyzing the model trained using the proposed
method. To the best of our knowledge, this work is the first to generate
biosignals using GANs while controlling the characteristics of the generated
data
Generating synthetic mixed-type longitudinal electronic health records for artificial intelligent applications
The recent availability of electronic health records (EHRs) have provided enormous opportunities to develop artificial intelligence (AI) algorithms. However, patient privacy has become a major concern that limits data sharing across hospital settings and subsequently hinders the advances in AI. Synthetic data, which benefits from the development and proliferation of generative models, has served as a promising substitute for real patient EHR data. However, the current generative models are limited as they only generate single type of clinical data for a synthetic patient, i.e., either continuous-valued or discrete-valued. To mimic the nature of clinical decision-making which encompasses various data types/sources, in this study, we propose a generative adversarial network (GAN) entitled EHR-M-GAN that simultaneously synthesizes mixed-type timeseries EHR data. EHR-M-GAN is capable of capturing the multidimensional, heterogeneous, and correlated temporal dynamics in patient trajectories. We have validated EHR-M-GAN on three publicly-available intensive care unit databases with records from a total of 141,488 unique patients, and performed privacy risk evaluation of the proposed model. EHR-M-GAN has demonstrated its superiority over state-of-the-art benchmarks for synthesizing clinical timeseries with high fidelity, while addressing the limitations regarding data types and dimensionality in the current generative models. Notably, prediction models for outcomes of intensive care performed significantly better when training data was augmented with the addition of EHR-M-GAN-generated timeseries. EHR-M-GAN may have use in developing AI algorithms in resource-limited settings, lowering the barrier for data acquisition while preserving patient privacy
Synthetic Observational Health Data with GANs: from slow adoption to a boom in medical research and ultimately digital twins?
After being collected for patient care, Observational Health Data (OHD) can
further benefit patient well-being by sustaining the development of health
informatics and medical research. Vast potential is unexploited because of the
fiercely private nature of patient-related data and regulations to protect it.
Generative Adversarial Networks (GANs) have recently emerged as a
groundbreaking way to learn generative models that produce realistic synthetic
data. They have revolutionized practices in multiple domains such as
self-driving cars, fraud detection, digital twin simulations in industrial
sectors, and medical imaging.
The digital twin concept could readily apply to modelling and quantifying
disease progression. In addition, GANs posses many capabilities relevant to
common problems in healthcare: lack of data, class imbalance, rare diseases,
and preserving privacy. Unlocking open access to privacy-preserving OHD could
be transformative for scientific research. In the midst of COVID-19, the
healthcare system is facing unprecedented challenges, many of which of are data
related for the reasons stated above.
Considering these facts, publications concerning GAN applied to OHD seemed to
be severely lacking. To uncover the reasons for this slow adoption, we broadly
reviewed the published literature on the subject. Our findings show that the
properties of OHD were initially challenging for the existing GAN algorithms
(unlike medical imaging, for which state-of-the-art model were directly
transferable) and the evaluation synthetic data lacked clear metrics.
We find more publications on the subject than expected, starting slowly in
2017, and since then at an increasing rate. The difficulties of OHD remain, and
we discuss issues relating to evaluation, consistency, benchmarking, data
modelling, and reproducibility.Comment: 31 pages (10 in previous version), not including references and
glossary, 51 in total. Inclusion of a large number of recent publications and
expansion of the discussion accordingl
Data Augmentation of IMU Signals and Evaluation via a Semi-Supervised Classification of Driving Behavior
Over the past years, interest in classifying drivers' behavior from data has
surged. Such interest is particularly relevant for car insurance companies who,
due to privacy constraints, often only have access to data from Inertial
Measurement Units (IMU) or similar. In this paper, we present a semi-supervised
learning solution to classify portions of trips according to whether drivers
are driving aggressively or normally based on such IMU data. Since the amount
of labeled IMU data is limited and costly to generate, we utilize Recurrent
Conditional Generative Adversarial Networks (RCGAN) to generate more labeled
data. Our results show that, by utilizing RCGAN-generated labeled data, the
classification of the drivers is improved in 79% of the cases, compared to when
the drivers are classified with no generated data.Comment: Extended version of the paper accepted to The 23rd IEEE International
Conference on Intelligent Transportation System
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