BioMaths Colloquium Series – 2022/23
02 November 2022 – 4pm (NEW TIME!)
Singleton Campus, Wallace 218 or Zoom (Register Here)
Analysis of diseases clusters and patient outcomes in people with multiple long term conditions using hypergraphs
Our BioMaths Colloquium Series continues with a seminar by James Rafferty from our Medical School (Swansea University). Jim has a background in theoretical physics. After graduating from Swansea University he moved to industry to work for a medical physics research company on the analysis of data from a novel MRI technique. After a position at Cardiff Metropolitan University he moved back to Swansea University in 2017, where he is currently working in the Environment and Health research Centre. Previously he worked on multimorbidity, where the work to be presented here was done, and diabetes related SAIL projects.
Having multiple concurrent long term health conditions, also known as multimorbidity, is becoming increasingly common as populations age. Understanding how clusters of diseases are likely to lead to other diseases, and the effect of multimorbidity on healthcare resource use will be of great importance as this trend continues. Graph based approaches, also called network analysis in the literature, have been used previously to study multimorbidity. The use of hypergraphs, which are generalisations of graphs where edges can connect to any number of nodes, and their application to the problem of understanding multimorbidity will be discussed. Analysis using hypergraphs has been carried out using a very large cohort of people in the Secure Anonymised Information Linkage (SAIL) databank to find the diseases and disease sets which are most important based on a measure of prevalence and a measure of healthcare resource utilisation. The results from this novel analysis will be presented and the strengths and challenges of using hypergraphs to understand multimorbidity will also be discussed. Hypergraphs are very flexible and general mathematical objects and there is still a great deal of development that can be done to make them more useful in epidemiological settings and beyond.