Search Results for author:
Found 5 papers, 1 papers with code
Machine learning for early prediction of circulatory failure in the intensive care unit
Intensive care clinicians are presented with large quantities of patient information and measurements from a multitude of monitoring systems.
Real-valued (Medical) Time Series Generation with Recurrent Conditional GANs
We also describe novel evaluation methods for GANs, where we generate a synthetic labelled training dataset, and evaluate on a real test set the performance of a model trained on the synthetic data, and vice-versa.
Predicting Clinical Events by Combining Static and Dynamic Information Using Recurrent Neural Networks
In this work we present an approach based on RNNs, specifically designed for the clinical domain, that combines static and dynamic information in order to predict future events.
Predicting the Co-Evolution of Event and Knowledge Graphs
By predicting future events, we also predict likely changes in the knowledge graph and thus obtain a model for the evolution of the knowledge graph as well.
Learning with Memory Embeddings
We introduce a number of hypotheses on human memory that can be derived from the developed mathematical models.
