Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Boosting Deep Learning Risk Prediction with Generative Adversarial
  Networks for Electronic Health Records   The rapid growth of Electronic Health Records (EHRs), as well as the
accompanied opportunities in Data-Driven Healthcare (DDH), has been attracting
widespread interests and attentions. Recent progress in the design and
applications of deep learning methods has shown promising results and is
forcing massive changes in healthcare academia and industry, but most of these
methods rely on massive labeled data. In this work, we propose a general deep
learning framework which is able to boost risk prediction performance with
limited EHR data. Our model takes a modified generative adversarial network
namely ehrGAN, which can provide plausible labeled EHR data by mimicking real
patient records, to augment the training dataset in a semi-supervised learning
manner. We use this generative model together with a convolutional neural
network (CNN) based prediction model to improve the onset prediction
performance. Experiments on two real healthcare datasets demonstrate that our
proposed framework produces realistic data samples and achieves significant
improvements on classification tasks with the generated data over several
stat-of-the-art baselines.

30
The categories of this paper are: q-alg
23
The categories of this paper are: solv-int
6
The categories of this paper are: astro-ph
7
The categories of this paper are: hep-th
21
The categories of this paper are: adap-org
31
The categories of this paper are: ao-sci
36
The categories of this paper are: cmp-lg
26
The categories of this paper are: bayes-an
18
The categories of this paper are: econ
2
The categories of this paper are: math
29
The categories of this paper are: funct-an
9
The categories of this paper are: nlin
37
The categories of this paper are: supr-con
34
The categories of this paper are: chem-ph
27
The categories of this paper are: comp-gas
35
The categories of this paper are: mtrl-th