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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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{
  "publisher": "Zenodo", 
  "DOI": "10.5281/zenodo.3859741", 
  "language": "eng", 
  "title": "Exploring Clinical Time Series Forecasting with  Meta-Features in Variational Recurrent Models", 
  "issued": {
    "date-parts": [
      [
        2020, 
        5, 
        27
      ]
    ]
  }, 
  "abstract": "<p>This is the source code used in the following paper:</p>\n\n<p>Ullah, S., Xu, Z., Wang, H., Menzel, S., Sendhoff, B., &quot;Exploring Clinical Time Series Forecasting with Meta-Features in Variational Recurrent Models&quot;&nbsp;&nbsp;<em>2020 IEEE World Congress on Computational Intelligence&nbsp;</em></p>\n\n<p>This paper investigates the effectiveness of Supplementary Medical Information, for improving the prediction of Variational Recurrent Models in Clinical Time Series Forecasting. &nbsp;</p>", 
  "author": [
    {
      "family": "Sibghat Ullah"
    }, 
    {
      "family": "Zhao Xu"
    }, 
    {
      "family": "Hao Wang"
    }, 
    {
      "family": "Stefan Menzel"
    }, 
    {
      "family": "Bernhard Sendhoff"
    }
  ], 
  "id": "3859741", 
  "event-place": "Glasgow, UK", 
  "version": "1", 
  "type": "article", 
  "event": "2020 IEEE World Congress on Computational Intelligence (IJCNN 2020)"
}
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