5 research outputs found
Distributed Conditional GAN (discGAN) For Synthetic Healthcare Data Generation
In this paper, we propose a distributed Generative Adversarial Networks
(discGANs) to generate synthetic tabular data specific to the healthcare
domain. While using GANs to generate images has been well studied, little to no
attention has been given to generation of tabular data. Modeling distributions
of discrete and continuous tabular data is a non-trivial task with high
utility. We applied discGAN to model non-Gaussian multi-modal healthcare data.
We generated 249,000 synthetic records from original 2,027 eICU dataset. We
evaluated the performance of the model using machine learning efficacy, the
Kolmogorov-Smirnov (KS) test for continuous variables and chi-squared test for
discrete variables. Our results show that discGAN was able to generate data
with distributions similar to the real data
The intersection of video capsule endoscopy and artificial intelligence: addressing unique challenges using machine learning
Introduction: Technical burdens and time-intensive review processes limit the
practical utility of video capsule endoscopy (VCE). Artificial intelligence
(AI) is poised to address these limitations, but the intersection of AI and VCE
reveals challenges that must first be overcome. We identified five challenges
to address. Challenge #1: VCE data are stochastic and contains significant
artifact. Challenge #2: VCE interpretation is cost-intensive. Challenge #3: VCE
data are inherently imbalanced. Challenge #4: Existing VCE AIMLT are
computationally cumbersome. Challenge #5: Clinicians are hesitant to accept
AIMLT that cannot explain their process.
Methods: An anatomic landmark detection model was used to test the
application of convolutional neural networks (CNNs) to the task of classifying
VCE data. We also created a tool that assists in expert annotation of VCE data.
We then created more elaborate models using different approaches including a
multi-frame approach, a CNN based on graph representation, and a few-shot
approach based on meta-learning.
Results: When used on full-length VCE footage, CNNs accurately identified
anatomic landmarks (99.1%), with gradient weighted-class activation mapping
showing the parts of each frame that the CNN used to make its decision. The
graph CNN with weakly supervised learning (accuracy 89.9%, sensitivity of
91.1%), the few-shot model (accuracy 90.8%, precision 91.4%, sensitivity
90.9%), and the multi-frame model (accuracy 97.5%, precision 91.5%, sensitivity
94.8%) performed well. Discussion: Each of these five challenges is addressed,
in part, by one of our AI-based models. Our goal of producing high performance
using lightweight models that aim to improve clinician confidence was achieved