Predictive modelling of atrial fibrillation
In collaboration with the Data Science Institute, the Departments of Computing and Aeronautics and the ElectroCardioMaths Group, we have support from the Rosetrees Trust to take the first steps in bringing modelling, machine learning and clinical procedures together to improve the diagnosis and treatment of atrial fibrillation (AF). This project is an exciting research challenge to better understand and treat AF by:
- investigating the rich signal content of the intracardiac electrogram and its relationship to disease;
- enhancing the interpretation and fusion of imaging data with other clinical measurements;
- creating a new generation of modelling technologies built on artificial intelligence.
To meet this goal we are exploiting recent developments in machine learning and advanced numerical modelling techniques.
Predictive Modelling for AF
Predictive modelling plays an important role in healthcare, and one that is growing. We are working at developing improved tools for answering “what if” questions through modelling. Our approach is to exploit deep networks for this purpose, drawing on their ability to learn complex mappings between inputs (including clinical data) and outputs. Our focus in on data that take the form of a spatial or spatio-temporal nature. Our vision is to replace the very long, cumbersome processes that are currently involved in applying numerical modeling to provide patient-specific treatments. This requires:
- absorbing numerical models into the predictive capabilities of the mappings performed by deep networks
- building tools that can explore the space of possible mappings from inputs – such as image data – through to inference or predictions
- being honest about uncertainty in models and data when making predictions
Some of these goals require “baby steps”, such as designing the right network architectures and training regimes in order to achieve acceptable performance in real-world data.
At present, we are seeking post-docs to join this project. For further details, see the flyer opportunities in deep learning for predictive modelling. Note that we are also seeking PhD candidates with similar interests, starting either in 2017 or 2018, so if you are considering doing a PhD in this area, please check with us.