4 citations · 8 across the 16 of their papers we have counts for
16 papers
Exploring the Requirements of Clinicians for Explainable AI Decision Support Systems in Intensive Care
Jeffrey N. Clark, Matthew Wragg, Emily Nielsen +7
There is a growing need to understand how digital systems can support clinical decision-making, particularly as artificial intelligence (AI) models become increasingly complex and…
Investigating Brain Connectivity and Regional Statistics from EEG for early stage Parkinson's Classification
Amarpal Sahota, Amber Roguski, Matthew W Jones +2
We evaluate the effectiveness of combining brain connectivity metrics with signal statistics for early stage Parkinson's Disease (PD) classification using electroencephalogram data…
Towards Personalised Patient Risk Prediction Using Temporal Hospital Data Trajectories
Thea Barnes, Enrico Werner, Jeffrey N. Clark +1
Quantifying a patient's health status provides clinicians with insight into patient risk, and the ability to better triage and manage resources. Early Warning Scores (EWS) are wide…
Safe and Robust Reinforcement Learning: Principles and Practice
Taku Yamagata, Raul Santos-Rodriguez
Reinforcement Learning (RL) has shown remarkable success in solving relatively complex tasks, yet the deployment of RL systems in real-world scenarios poses significant challenges…
An Interactive Human-Machine Learning Interface for Collecting and Learning from Complex Annotations
Jonathan Erskine, Matt Clifford, Alexander Hepburn +1
Human-Computer Interaction has been shown to lead to improvements in machine learning systems by boosting model performance, accelerating learning and building user confidence. In…
LL-VQ-VAE: Learnable Lattice Vector-Quantization For Efficient Representations
Ahmed Khalil, Robert Piechocki, Raul Santos-Rodriguez
In this paper we introduce learnable lattice vector quantization and demonstrate its effectiveness for learning discrete representations. Our method, termed LL-VQ-VAE, replaces the…