Develope stable, consistent, operationalisable, reproducible, and explaninable clusters of multimorbidities. We will analyze these clusters in Clinical Practice Research Datalink and validate them in DataLoch to find their genetic basis and in Scottish Longitudinal Study to infer their socio-economic basis.
Develop understanding on the causes of childhood poverty. We are focussing on whether children’s physical accessibility to services, such as health facilities or schools contribute to their deprivation.
Develop new concise and informative crystal structure descriptors, focussed on maintaining chemical interpretability and generalisability.
Develop sustainable machine learning models to improve intercensal population estimates in Mozambique. We are using satellite images and microcensus data to estimate population in rural and semi-unban areas independent of census data.
Develop, validate and disseminate a suite of new risk prediction models for a set of adverse outcomes such as mortality, increased care needs or hospitalisation. In the context of population ageing and resource constrained services, risk prediction tools have great potential to ensure the delivery of the right care to the right person in the most cost-effective way.
Develop machine learning models for analysing fluorescence, Raman, and time-resolved spectroscopy signals in the context of interventional pulmonology, in particular cancer delineation.
Develop segmentation algorithms for very large scale 4D microtomography images without human supervision. The segmented images will inform critical parameters to understand the mechanism of rock deformation.