Software Open Access
Exploring Clinical Time Series Forecasting with Meta-Features in Variational Recurrent Models
Sibghat Ullah;
Zhao Xu;
Hao Wang;
Stefan Menzel;
Bernhard Sendhoff
This is the source code used in the following paper:
Ullah, S., Xu, Z., Wang, H., Menzel, S., Sendhoff, B., "Exploring Clinical Time Series Forecasting with Meta-Features in Variational Recurrent Models" 2020 IEEE World Congress on Computational Intelligence
This paper investigates the effectiveness of Supplementary Medical Information, for improving the prediction of Variational Recurrent Models in Clinical Time Series Forecasting.
Files
(68.4 kB)
| Name | Size | |
|---|---|---|
|
VRNN-TSForecasting-Code-200131-Open.zip
md5:0a04320548c73dbe38a1998f94b0f03b |
68.4 kB | Download |
85
11
views
downloads
| All versions | This version | |
|---|---|---|
| Views | 85 | 85 |
| Downloads | 11 | 11 |
| Data volume | 752.5 kB | 752.5 kB |
| Unique views | 73 | 73 |
| Unique downloads | 11 | 11 |
Indexed in
- Publication date:
- May 27, 2020
- DOI:
-
- Keyword(s):
- time series forecasting recurrent neural networks deep latent-variable models MIMIC III Clinical Applications
- Grants:
-
European Commission:
- ECOLE - Experience-based Computation: Learning to Optimise (766186)
- Meeting:
- 2020 IEEE World Congress on Computational Intelligence (IJCNN 2020), Glasgow, UK, 19-24th July 2020
- Communities:
- License (for files):
- GNU General Public License v3.0 or later