Chronic Disease Informatics Group
The Chronic Disease Informatics Group is interested in the application of novel statistical and semantic web approaches to create deep phenotyping platforms for the study of chronic disease. The group brings together scientists from the disciplines of medicine, immunology, environmental science, statistics and computer science.
In particular, the group is concerned with better stratifying the patient-level risk associated with flares of ANCA-associated vasculitis. This condition can cause failure of multiple organs as a consequence of overwhelming necrotising inflammation affecting small blood vessels.
The ultimate goal is to support tailoring of therapy to the risk in the individual at a given point in time using a big data approach to incorporate a broad range of potential data streams using the Resource Description Framework data model.
For more information on ANCA vasculitis, please see the following links:
We are addressing the following questions:
- Can environmental factors and/or pathogens be identified that are associated with flares for patients with ANCA vasculitis?
- How should such data be best integrated with other fixed patient-level data, biomarker and external medical influences, to support relapse prediction?
- Can a suitable statistical model be derived that allows for a better understanding of the risk of flare for a patient at a given point in time, which can be used to inform subsequent benefit-risk decisions regarding the need for relevant therapies?
- What is the optimal means of linking flow cytometry data to clinical outcome?
Chronic Disease Informatics Group,
ADAPT SFI Centre
Trinity College Dublin,