software . 2020

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

Sibghat Ullah; Zhao Xu; Hao Wang; Stefan Menzel; Bernhard Sendhoff;
Open Source English
  • Published: 01 Jan 2020
  • Publisher: Zenodo
Abstract
<p>This is the source code used in the following paper:</p> <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> <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>
Subjects
free text keywords: time series forecasting, recurrent neural networks, deep latent-variable models, MIMIC III, Clinical Applications
Funded by
EC| ECOLE
Project
ECOLE
Experience-based Computation: Learning to Optimise
  • Funder: European Commission (EC)
  • Project Code: 766186
  • Funding stream: H2020 | MSCA-ITN-EID
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Software . 2020
Provider: Zenodo
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