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Exploring Clinical Time Series Forecasting with Meta-Features in Variational Recurrent Models

Sibghat Ullah; Zhao Xu; Hao Wang; Stefan Menzel; Bernhard Sendhoff


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  <identifier identifierType="DOI">10.5281/zenodo.3859741</identifier>
  <creators>
    <creator>
      <creatorName>Sibghat Ullah</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-2627-6019</nameIdentifier>
      <affiliation>University of Leiden</affiliation>
    </creator>
    <creator>
      <creatorName>Zhao Xu</creatorName>
      <affiliation>NEC Laboratories GmBH</affiliation>
    </creator>
    <creator>
      <creatorName>Hao Wang</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-4933-5181</nameIdentifier>
      <affiliation>University of Leiden</affiliation>
    </creator>
    <creator>
      <creatorName>Stefan Menzel</creatorName>
      <affiliation>Honda Research Institute Europe GmBH</affiliation>
    </creator>
    <creator>
      <creatorName>Bernhard Sendhoff</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-1233-9584</nameIdentifier>
      <affiliation>Honda Research Institute Europe GmBH</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Exploring Clinical Time Series Forecasting with  Meta-Features in Variational Recurrent Models</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2020</publicationYear>
  <subjects>
    <subject>time series forecasting</subject>
    <subject>recurrent neural networks</subject>
    <subject>deep latent-variable models</subject>
    <subject>MIMIC III</subject>
    <subject>Clinical Applications</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2020-05-27</date>
  </dates>
  <language>en</language>
  <resourceType resourceTypeGeneral="Software"/>
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    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.3859740</relatedIdentifier>
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  <version>1</version>
  <rightsList>
    <rights rightsURI="https://opensource.org/licenses/GPL-3.0">GNU General Public License v3.0 or later</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
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  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;This is the source code used in the following paper:&lt;/p&gt;

&lt;p&gt;Ullah, S., Xu, Z., Wang, H., Menzel, S., Sendhoff, B., &amp;quot;Exploring Clinical Time Series Forecasting with Meta-Features in Variational Recurrent Models&amp;quot;&amp;nbsp;&amp;nbsp;&lt;em&gt;2020 IEEE World Congress on Computational Intelligence&amp;nbsp;&lt;/em&gt;&lt;/p&gt;

&lt;p&gt;This paper investigates the effectiveness of Supplementary Medical Information, for improving the prediction of Variational Recurrent Models in Clinical Time Series Forecasting. &amp;nbsp;&lt;/p&gt;</description>
  </descriptions>
  <fundingReferences>
    <fundingReference>
      <funderName>European Commission</funderName>
      <funderIdentifier funderIdentifierType="Crossref Funder ID">10.13039/501100000780</funderIdentifier>
      <awardNumber awardURI="info:eu-repo/grantAgreement/EC/H2020/766186/">766186</awardNumber>
      <awardTitle>Experience-based Computation: Learning to Optimise</awardTitle>
    </fundingReference>
  </fundingReferences>
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