Creating patient segments by examining the early signs of Alzheimer's disease
We aim to help clinicians better help patients during the early 'prodromal' phases of Alzheimer’s Disease.
Behavioural data, biomarkers, cognitive testing, and genetic information are all useful in determining the state of Alzheimer’s that the patient is currently at. We will use the ELSA dataset which contains a significant number of cognitive tests and survey data. Trends discovered in the analysis of the rich ELSA data set will then be used to create patient segments and profiles. We will then begin analysis and deep predictive modelling to construct a model to classify patients that may be susceptible to Alzheimer’s Disease. This model could then be applied to patients that may be at risk for developing Alzheimer’s Disease so that clinicians could take early intervention.