268 citations · 316 across the 4 of their papers we have counts for
5 papers
Machine learning for early prediction of circulatory failure in the intensive care unit
Stephanie L. Hyland, Martin Faltys, Matthias Hüser +12
Intensive care clinicians are presented with large quantities of patient information and measurements from a multitude of monitoring systems. The limited ability of humans to proce…
Real-valued (Medical) Time Series Generation with Recurrent Conditional GANs
Cristóbal Esteban, Stephanie L. Hyland, Gunnar Rätsch
Generative Adversarial Networks (GANs) have shown remarkable success as a framework for training models to produce realistic-looking data. In this work, we propose a Recurrent GAN…
Predicting Clinical Events by Combining Static and Dynamic Information Using Recurrent Neural Networks
Cristóbal Esteban, Oliver Staeck, Yinchong Yang +1
In clinical data sets we often find static information (e.g. patient gender, blood type, etc.) combined with sequences of data that are recorded during multiple hospital visits (e.…
Predicting the Co-Evolution of Event and Knowledge Graphs
Cristóbal Esteban, Volker Tresp, Yinchong Yang +2
Embedding learning, a.k.a. representation learning, has been shown to be able to model large-scale semantic knowledge graphs. A key concept is a mapping of the knowledge graph to a…
Learning with Memory Embeddings
Volker Tresp, Cristóbal Esteban, Yinchong Yang +2
Embedding learning, a.k.a. representation learning, has been shown to be able to model large-scale semantic knowledge graphs. A key concept is a mapping of the knowledge graph to a…