Dementia is a diverse set of diseases characterized by a progressive cognitive decline, behaviour change, loss of functional and social ability and high five-year mortality rates.
Temporal changes in these observable features can be thought of as disease trajectories or dementia phenotypes. Clearly classifying these diverse and informative trajectories is central to research on the causes of dementia and to provide effective care for those with dementia. This project aims to develop and apply statistical and machine learning methods to i) longitudinal trajectory clustering of multiple repeatedly measured features of cognitive decline, and ii) to quantify causal relationships between modifiable risk factors and cognitive decline.
