Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: 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.
\textit{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
\textit{single type} of clinical data, i.e., either continuous-valued or
discrete-valued. In this paper, we propose a generative adversarial network
(GAN) entitled EHR-M-GAN which synthesizes \textit{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 in
performance over state-of-the-art benchmarks for synthesizing clinical
timeseries with high fidelity. 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.

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The categories of this paper are: cond-mat
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The categories of this paper are: astro-ph
36
The categories of this paper are: cmp-lg
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The categories of this paper are: eess
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The categories of this paper are: chao-dyn
37
The categories of this paper are: supr-con
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The categories of this paper are: hep-ex
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The categories of this paper are: gr-qc
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The categories of this paper are: hep-th
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The categories of this paper are: alg-geom
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The categories of this paper are: patt-sol
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The categories of this paper are: chem-ph
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The categories of this paper are: quant-ph
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The categories of this paper are: physics
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The categories of this paper are: comp-gas
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The categories of this paper are: q-alg