The Problem

Understanding the progression of Alzheimer’s disease (AD) is critical to the optimal design of clinical trials, and the ability to plan an individuals care. However, the current FDA-approved model of AD progression only takes into account age, gender and the main genetic risk factor (APOE). While this needs to be improved, this work is held back by data with short follow-up and many data analysis challenges. A vast amount of data on AD progression is available through anonymised medical records, but a key problem is that patients can be at different disease stages at first assessment.
Our Research
In this project we aim to overcome this problem through the introduction of a novel approach – Temporal Clustering – that jointly learns a set of common trajectories and a new time frame in which, at first assessments individuals are predicted to have been at a similar disease stage.
Theme
Precision Medicine
Disease
Mental Health
People
Elizabeth Baker
Chris Wallace
Alice Parodi
Richard Dobson
Publications: http://biorxiv.org/content/early/2016/06/27/060830